TLC in the Analysis of Plant Material

Abstract

This paper provides an overview of thin-layer chromatography (TLC) and high-performance thin-layer chromatography (HPTLC) methods for analyzing plant materials and herbal formulations, as described in scientific publications from January 2022 to July 2025. It describes the use of TLC in the qualitative and quantitative examination of plant materials and pharmaceutical preparations containing herbs, including profiling plant materials using TLC and applying it to HPTLC plates. It also describes other modern methods that improve component separations, such as applying TLC to profile plant formulations and detect adulterations and contaminants in them. Additionally, it discusses TLC coupled with other methods, such as principal component analysis (PCA), hierarchical cluster analysis (HCA), orthogonal partial least squares discriminant analysis (OPLS-DA), mass spectrometry (MS), nuclear magnetic resonance (NMR), surface-enhanced Raman spectroscopy (SERS), and image analysis (IA). The quantitative determination of biologically active compounds in herbs and herbal formulations is presented based on methods that combine TLC with densitometry. The paper also discusses TLC with effect-oriented analysis, including the detection of antimicrobial, antioxidant, enzyme-inhibiting, endocrine-disrupting, genotoxic, and cytotoxic substances. The advantages, disadvantages, and prospects of analyzing plant material using the TLC technique are indicated. TLC/HPTLC has great prospects for use by regulatory authorities due to the low cost of analysis and high throughput.

1. Introduction

Plant material is a source of food, including dietary supplements and herbal medicines [1]. Ensuring the consistent, high-quality composition of plant material is extremely difficult due to the presence of many biologically active compounds. The content of these compounds varies depending on the part of the plant, its geographical origin, and the season of collection (spring, summer, or autumn) [2,3]. Plant material can be misidentified due to its morphological similarity to the plant from which it is obtained. On the other hand, herbal preparations may contain an incorrect amount of the active substance compared to that declared, or they may contain contaminants or additives from synthetic drugs [4,5]. Therefore, to ensure consumers safety, it is essential that plant material and herbal preparations undergo quality testing. It is therefore necessary to characterize the compounds contained in plant material, as there may be more than 200 of them, as is the case with Ruta graveolens L. [6]. Metabolomics deals with the identification of these compounds, which may be primary or secondary metabolites of plants. Metabolomics most often uses analytical techniques such as gas chromatography with mass spectrometry (GC-MS), liquid chromatography with mass spectrometry (LC-MS), and nuclear magnetic resonance (NMR). Metabolomic studies using these techniques provide a very large amount of data, which is then analyzed using statistical methods. To ensure repeatability of results, the basic step in metabolomic data processing is the selection of appropriate chromatographic peaks [7]. Other advanced techniques, such as FT-IR spectroscopy, can also be used to identify active compounds [8].

Although techniques such as high-performance liquid chromatography (HPLC) and gas chromatography (GC) offer greater precision, repeatability of results, resolution, and sensitivity, thin-layer chromatography (TLC) is characterized by simplicity and low cost [9,10,11]. No expensive equipment or complicated operation is required. Only a chromatography plate, solvents, and a developing chamber are needed. The cost of analysis (equipment and reagents) is much lower than with HPLC or GC, making it ideal for routine quality control and screening in laboratories with limited resources. The ability to analyze multiple samples simultaneously means that multiple plant extracts or samples can be compared at the same time on a single TLC plate. In contrast, HPLC and GC require a separate injection and analysis for each sample, which increases the time required for the work [10,12]. Visual and rapid detection of components is possible. TLC results can be evaluated visually after spraying with coloring reagents or under UV light. This allows for easy comparison of plant patterns (fingerprints) with a reference preparation and helps authenticate plant raw materials (e.g., detecting adulteration or species substitutes). TLC can separate compounds with different polarities, such as alkaloids, flavonoids, and essential oils. It does not require sample volatility, as in gas chromatography (GC), or complete solubility, as in high-performance liquid chromatography (HPLC). The mobile and stationary phases can be easily adapted to the type of compounds. TLC is qualitative, semi-quantitative, and quantitative method. TLC does not provide as accurate quantitative results as HPLC, but it can be used for semi-quantitative assessment of active ingredient content, e.g., by comparing spot intensity with standards. However, when TLC is combined with densitometry, quantitative analysis of ingredients present in plant extracts can be performed. TLC is very useful in screening and preliminary tests. It is an excellent method for rapid screening for the presence of specific groups of compounds (e.g., alkaloids, saponins, flavonoids). It allows for quick verification of the purity of the extract before more advanced analysis (e.g., HPLC), which prevents damage to the HPLC column [10,12]. TLC is used in the identification of phytochemical “fingerprints”. Organizations such as the WHO and the European Pharmacopoeia recognize TLC as the recommended method for the identification of herbal medicines [13]. The chromatographic profile (the so-called phytochemical fingerprint) serves as a characteristic of the raw material or preparation and for the detection of adulteration and falsification [14,15]. TLC also enables the biological activity of separated plant extract components to be tested on a plate, allowing the identification of potential applications and/or adverse effects of active compounds contained in plant material [12,16]. Furthermore, using connectors (e.g., an MS interface) enables active compounds to be identified [17,18,19]. Additionally, combining TLC with a densitometer enables the determination of active substance content [20,21].

Due to the numerous advantages of TLC, this review is based on a selection of scientific articles published between January 2022 and July 2025, which employed thin-layer chromatography to analyze plant material, preparations containing plant-derived substances and plant extracts. It highlights new applications of this technique and its potential for controlling food and herbal medicines, but also the limitations of published studies. The review also emphasizes the necessity of developing detailed protocols for each stage of quality control of plant material using TLC, from extract preparation to analysis.

2. TLC in Qualitative and Quantitative Analysis of Plant Materials and Herbal Formulations

TLC is a simple, fast, and effective method for separating mixtures of chemical com-pounds. TLC is widely used in the analysis of plant extracts and natural substances, the quality control of pharmaceuticals and food products, the purity testing of chemical compounds, and the rapid comparison of the composition of different samples [12,19,22,23,24,25,26,27]. Harvested or purchased plant material or herbal formulations must be properly crushed and extracted. Then, before extraction, grind the herbs in a mill or mortar. If using fresh herbs, grind them in a liquid nitrogen mill after thawing. Several extraction methods exist, including maceration, Soxhlet extraction, sonication, microwave extraction, supercritical CO₂ extraction, accelerated extraction, liquid–liquid extraction, and solid phase extraction. Maceration and sonication are most commonly used when preparing herbal extracts for TLC analysis. A solvent must be selected for extraction. The selection of solvents depends on the polarity of the desired compounds present in the herbs. Polar compounds (e.g., phenols, flavonoids, and sugars) are extracted using methanol, ethanol, or a methanol and water mixture. Medium-polar compounds, such as flavonoids, aglycones, and glycosides, are extracted using 70–100% ethanol or acetonitrile. Nonpolar compounds such as terpenes, lipids, and waxes are extracted using hexane, chloroform, or dichloromethane. In practice, however, stepwise extraction can also be used. First, hexane is used, followed by dichloromethane or chloroform. Then, ethanol or methanol is used, and finally, water (this is liquid–liquid fractionation). This yields fractions of varying polarity, which can be analyzed separately using TLC. After extraction, filter the extract and concentrate or dilute it if necessary. Sometimes it is necessary to replace the solvent to make the sample easier to apply to the chromatography plate. Precautions should be taken during storage of the extract to avoid degradation of the extracted components [28,29,30,31].

TLC plates coated with silica gel 60F₂₅₄ are most commonly used for the analysis of herbs. Silica gel is mainly used in the analysis of alkaloids, flavonoids, glycosides, phenolic acids, coumarins, terpenes, and saponins. Plates with inert aluminum oxide 60F₂₅₄ are used for the separation of less polar compounds (e.g., terpenes, essential oils, some flavonoid aglycones). Cellulose plates, on the other hand, are used for the analysis of hydrophilic compounds, e.g., sugars, amino acids, and some glycosides [10,22,23,24]. The durability and cost-effectiveness of chromatography plates are important when analyzing plant extracts, as these samples are often complex, rich in substances with different physicochemical properties, and can affect the quality of separation and the service life of the plate. The plates should be resistant to polar and non-polar solvents (e.g., methanol, ethanol, chloroform, toluene), acids (e.g., HCl) and bases (e.g., NH₄OH) in small amounts. The plates should be mechanically stable (they must not flake) and resistant to moisture. Higher quality plates (e.g., Merck Silica Gel 60 F₂₅₄) are more expensive, but ensure stable separation quality, which reduces the risk of having to repeat analyses. Cheaper plates can be used in screening tests (phytochemical screening), while only high-quality plates should be used in quantitative or standardization analyses. In most cases, chromatography plates are disposable, but for some compounds (e.g., in qualitative tests), aluminum plates can be used and cleaned with appropriate solvents (if the stationary phase does not degrade). Aluminum plates can be used and cleaned with appropriate solvents (if the stationary phase does not degrade). However, the most commonly used TLC plates for herb analysis are silica gel 60 F₂₅₄, which are versatile, inexpensive, and suitable for most plant compounds [10,22,23].

In thin-layer chromatography, determining the proportions of solvents in the mobile phase (eluent) is based primarily on the difference in polarity between the compounds being tested and the stationary phase. It also depends on matching the elution strength of the mobile phase to the properties of the substances being analyzed. The mobile phase is usually a mixture of polar and nonpolar solvents (e.g., diethyl ether and hexane or ethanol and chloroform), and its proportions are determined experimentally to obtain the appropriate R_F delay coefficients. Selecting the mobile phase is performed in steps. First, solvents with different polarities are chosen [12,22,24]. Then, several mixtures with different proportions are tried. The migration of the spots is observed, and the proportion that provides the best separation is chosen. Examples of mobile phases used for the analysis of plant extracts include ethyl acetate–methanol–water (100:13.5:10, v/v) for anthroaglycosides, arbutin, alkaloids, cardiac glycosides, flavonoids, and saponin; toluene–ethyl acetate (93:7, v/v) for essential oils, terpenes, and coumarin; toluene–ethyl acetate–diethyl amine (70:20:10, v/v) for alkaloids; and ethyl acetate–formic acid–acetic acid–water (100:11:11:26, v/v) for tannins, tannic acids, and flavonoids. However, these mobile phases often require modification [10].

Environmental conditions such as humidity and temperature can have a significant impact on the analysis of plant extracts using TLC and, in some cases, can be a cause for concern if not controlled. An increase in temperature reduces the viscosity and surface tension of the solvent, which can accelerate the migration of the mobile phase and change the R_F values. Higher temperatures can reduce the strength of interactions between the molecules of the separated substances and the stationary phase, making the separation of components less clear. Some plant compounds (e.g., flavonoids, alkaloids, phenols) are thermolabile—they can degrade or oxidize at excessively high temperatures. High humidity causes water adsorption on the surface of the silica gel, which changes the polarity of the stationary phase and can significantly affect the R_F values. With variable humidity, the results may be unrepeatable even for the same sample and mobile phases. High humidity can impair the resolution of polar compounds in particular, which compete with water molecules for adsorption sites. To minimize the influence of environmental factors, analyses should be performed under controlled conditions, i.e., at 20–25 °C with moderate humidity. Chromatography plates should be stored in a dry place, preferably in a desiccator. Mobile phases should be prepared immediately before use and the chromatography chamber should be closed tightly to ensure proper saturation with the vapors of the mobile phase used [10,22,23].

Visualization of spots after chromatographic separation is crucial because most organic compounds are colorless and invisible on the plate without additional detection. Substances separated by TLC can be detected by the following methods: physical—based on individual color or fluorescence under UV light; chemical—through color reactions with developing reagents; physicochemical—e.g., using isotopes as visualizing reagents, biological—using biosensors. Due to the detection mechanism, developing reagents can be divided into: conservative reagents—not destroying the separated substances, and destructive reagents—causing partial destruction or change in the structure of the separated substances. In the case of plant extract analyses, detection is carried out as follows: using UV light at 254 nm (for plates coated with a fluorescent indicator F₂₅₄, UV-absorbing compounds extinguish fluorescence and are visible as dark spots), and at 365 nm (for the observation of fluorescent compounds, e.g., flavonoids, coumarins, and certain alkaloids). Various visualizing reagents are used that react with specific groups of compounds to form colored spots. For example, vanillin–H₂SO₄ (universal, especially for terpenes and phenols)—the plate must be heated after spraying (60–110 °C)—produces various colors of spots; anisaldehyde (p-anisaldehyde + H₂SO₄ in EtOH)—very universal, produces colored spots; Dragendorff—detects alkaloids (orange/brown); ninhydrin—detects amino acids and peptides (purple/plum); ferric chloride (FeCl₃)—detects phenols (dark/blue); and NP/PEG (natural product reagent + polyethylene glycol)—detects flavonoids (fluorescence at 366 nm) [10,12,22,23].

Although TLC is more commonly used for qualitative analysis detection of compounds), it can also be used for quantitative analysis of herbal products. Quantitative analysis is possible when the intensity or area of spots on a chromatogram is densitometrically measured using a TLC scanner or densitometer. The result can then be compared to a reference curve obtained from solutions with a known concentration of the substance. Three methods of quantitative determination can be used with TLC: densitometry, which measures the absorbance or fluorescence of spots (the most accurate method); elution and spectrophotometric measurement, which involves scraping off the spot, dissolving it in a solvent, and measuring the absorbance; and image analysis, which uses a scan and software. Image analysis is a cheaper alternative to densitometry but less accurate. Thanks to these instrumental and technical solutions, herbal products can be analyzed for the determination of chemical marker content (e.g., flavonoids, alkaloids, and phenolic acids), quality control of plant extracts and raw materials, and the assessment of herbal preparation standardization in accordance with pharmacopoeial requirements (e.g., the European Pharmacopoeia allows for TLC densitometry as a quantitative method). It should be emphasized that, like any other analytical method, TLC used as a quantitative method must undergo full validation [10,12,22,23].

2.1. TLC in Qualitative Analysis

2.1.1. Profiling Plant Material Using TLC

Profiling plant material samples, also known as ‘fingerprinting’, can be helpful in both the identification process and assessing the quality of the material. This can be done using various methods, including spectroscopic and chromatographic techniques. TLC profiling is most commonly used in plant material analysis due to the simplicity of sample preparation and low solvent consumption. Fingerprints represent characteristic chromatographic bands that allow for the unambiguous identification of a sample [13,32,33]. These bands can be analyzed under white light or UV light at 254 nm or 366 nm, either without prior spraying with a derivatizing reagent or after spraying with various reagents that reveal specific groups of active compounds.

Pratiwi and Dewi [34] detected alkaloids, flavonoids, saponins and phenolic compounds in an ethanolic extract of Muntingia calabura L. leaves, for example. Depending on the group of active compounds analyzed, different mobile phases and derivatizing reagents were used to separate the extract components on a silica gel plate: chloroform-methanol (85:15, v/v); Dragendorff’s reagent (for the analysis of alkaloids); butanol-acetic acid-water (4:1:5, v/v); AlCl₃ solution in chloroform (for the analysis of flavonoids); chloroform-methanol-water (40:50:10, v/v); a mixture of anisaldehyde and sulphuric acid reagent (for the analysis of saponins); chloroform-methanol (9:1, v/v); and FeCl₃ reagent (for the analysis of phenolic compounds) [34].

TLC was used to identify samples of Operculina turpethum (L.) Silva Manso, also known as Trivrut. While the root of this plant is a common ingredient in Ayurvedic medicines, preparations containing other parts of the plant are available to purchase under the Trivrut brand. Only two of the five samples tested showed the presence of flavonoids and steroids. Only one sample’s chromatogram showed spots consistent with the standard set out in the Ayurvedic Pharmacopoeia of India. This suggests that there is botanical variability in these samples depending on their origin [35].

Kumar et al. [36] developed a chromatographic fingerprinting method for 14 plant roots from the Apocynaceae family. They used a methanol extract. The extracts of 12 species were analyzed using a mobile phase of ethyl acetate-hexane (1.5:8.5, v/v), while one sample was analyzed using a mobile phase of methanol-chloroform (0.8:9.2, v/v), and another using a 100% chloroform phase. Spots were observed after derivatization with anisaldehyde. Distinct bands with different R_F values were observed for all 14 samples. These fingerprints, when combined with microscopic analysis, can be used to identify root samples from plants in the Apocynaceae family that are often used as ingredients in traditional Indian medicines [36].

The TLC method on silica gel 60F₂₅₄, using a mobile phase of hexane, ethyl acetate and methanol in a ratio of 8:6:1, was employed to analyze the hexane, aqueous and ethyl acetate extracts of Eurya acuminata DC. leaves. After analysis, the plates were observed under UV light at λ = 254 nm, first without and then with the derivatizing reagent anisaldehyde-sulphuric acid. Ten spots were detected in the hexane fraction (one of which was a steroid and one a terpene), five in the ethyl acetate fraction (one a flavonoid and two terpenes), and three in the aqueous fraction (one a terpene and one a saponin) [37].

Hidayatullah et al. [38] developed a thin-layer chromatography (TLC)-densitometric method for the quality control of herbal medicines derived from the extracts of Plantago major L., Sonchus arvensis L., Strobilanthes crispa (L.) Blume and Orthosiphon stamineus Benth., by determining the optimal conditions for analyzing luteolin, quercetin, apigenin and sinensetin. Silica gel 60F₂₅₄ and a mobile phase of chloroform-acetone-dichloromethane-acetonitrile-formic acid (6:2:2:0.05:0.05 v/v) were used, and densitometric analysis was performed at λ = 335 nm. Due to the instability of the standard solutions and samples, these must be prepared immediately prior to analysis [38].

TLC has also been used to authenticate Pluchea indica (L.) Less, a plant used as a pharmaceutical ingredient in Indonesia. Analyses were performed using a polar mobile phase consisting of ethyl acetate, water and a mixture of formic acid and acetic acid (8.5:1.5:1:1, v/v), with gallic acid, chlorogenic acid and quercetin acting as the standards. The chromatograms were analyzed under UV 254 and UV 366, as well as under white light and after spraying with 10% H₂SO₄ and 5% FeCl₃ [39].

Bationo et al. [40] created TLC profiles of Lippia multiflora Moldenke, Lippia alba (Mill.) N.E.Br. ex Britton & P.Wilson and Ocimum basilicum L. Extracts prepared in ethanol with the addition of 2% acetic acid were examined by TLC (silica gel, ethyl acetate-acetic acid-formic acid-H₂O (100:11:11:26, v/v). Flavonoids were detected using Neu’s reagent. Quercetin and rutin were used as standards.

Guimarăes et al. [41] analyzed the TLC profiles of ethanol extracts obtained from various parts of the Passiflora edulis species at different developmental stages. Flavonoids and terpenes were found in all organs, whereas tannins were only detected in root extracts.

TLC combined with qualitative densitometric analysis was used to evaluate the presence of phenolic compounds and flavonoids in the Papuan plants Myrmecodia beccarii Hook.f., Villebrunea rubescens (Bl), Breynia cernua Muel. Arg, Bridelia spp. and Dodonaea viscosa Jacq. The analyses were performed on silica gel 60 F₂₅₄ TLC plates, using a mobile phase of toluene, ethyl acetate and formic acid (4:2:0.2, v/v). Densitometric analyses were performed at wavelengths of 254 nm and 366 nm [42].

TLC was used to separate the components of the plant extracts Kleinia longiflora DC., Berchemia discolor Hemsl., Persea americana Mill., Sansevieria hyacinthoides (L.) Druce, Dichrostachys cinerea (L.) Wright & Arn, Withania somnifera (L.) Dunal, Momordica balsamina L., Lonchocarpus capassa Rolfe, Pappea capensis Sond. & Harv., Searsia lancea (L.F.) F.A. Barkley, Peltophorum africanum Sond., Maytenus heterophylla (Eckl. & Zeyh.) N.Robson. Spot inspection was performed at 254 nm and 360 nm. Substances that were invisible under UV light were detected using a vanillin-sulphuric acid reagent. Antioxidant substances were visualized using the DPPH [43].

TLC profiles were developed for the methanol extracts of the flowers and leaves of Hypericum cordifolium Choisy. Following derivatization with FeCl₃, the separated bands on the plate revealed the presence of 11 compounds in the flower extract and eight compounds in the leaf extract. The presence of chlorogenic acid and mangiferin was detected in both extracts based on R_F values. The presence of hyperoside and quercetin was also detected in the flower extract based on R_F. The chromatograms were analyzed under UV light at 254 and 366 nm wavelengths, and under white light after spraying with DPPH [44].

Combretum zeyheri Sond. leaf extracts were analyzed using silica gel 60F₂₅₄ with hexane-ethyl acetate (90:10 to 85:15) and chloroform-methanol (90:10 to 80:20) as the mobile phases. The spots on the plates were then observed under UV light at 254 nm and 360 nm. Sulphuric acid was also used for derivatization. This method enabled the separation and detection of alkaloids, tannins, terpenes and saponins [45].

Because Scleromitrion diffusum (Willd.) R.J. Wang is often confused with Oldenlandia corymbose Willd., a TLC method was developed to analyze both herbs, allowing for their differentiation. The TLC analysis used asperuloside and scandoside methyl ester as two chemical markers [46]. TLC analyses were performed on silica gel 60F₂₅₄ plates. Three mobile phases were used, with dichloromethane-methanol-water (15:5.4:0.3, v/v) being selected as the optimal phase. After developing and drying the plates, the spots were derivatized using a solution of 10% H₂SO₄ in ethanol, and then viewed under visible light and at 355 nm. In the TLC analysis, scandoside methyl ester was either undetectable or exhibited a faint band; however, the asperuloside band was clearly visible. Therefore, TLC can distinguish the herbs Scleromitrion diffusum and Oldenlandia corymbosa using these two markers, asperuloside and scandoside methyl ester. However, asperuloside and scandoside methyl ester in the herbal samples were quantified using HPLC [46].

However, in many studies, TLC was initially employed for preliminary investigations, after which more advanced analytical techniques were used, such as spectrophotometry, HPLC, HPLC-MS, LC-MS, GC-MS, NMR and FTIR. These techniques can confirm analyses previously conducted using TLC and also identify unknown substances present in extracts. They can also provide precise quantitative analysis of components present in herbal samples. In combination with densitometry, they can also confirm previously performed TLC analyses. TLC was used as a preliminary analysis technique in the study of Nigella sativa L. herb [47], Nigella sativa seeds [48], Satureja kitaibelii Wierzb. ex Heuff. seeds [49], Alstonia scholaris (Linn) seeds [50], Mexican oak leaves (Quercus rugosa (Masam.) J.C.Liao, Q. glabrescens A.Kern. and Q. obtusata Bonpl.) [51], korzeni Berberis lycium Royle [52], above-ground parts of Trianthema decandrum L. [53], leaves and fruits of Dicerocaryum senecioides (Klotzsch) Abels and Diospyros mespiliformis Hochst. ex A.DC. [54], Euphorbia hirta L. leaves [55], Origanum vulgare L. ssp. vulgare [56], Achillea millefolium L., Calendula officinalis L., Matricaria recutita L. and Hibiscus sabdariffa L. [32], aboveground parts of Centaurea parviflora Desf. [57].

For example, de Torre et al. [56] used thin-layer chromatography (TLC), high-performance liquid chromatography-diode array detection (HPLC-DAD), and liquid chromatography-mass spectrometry (LC-MS) to characterize the chemical components present in the ethanol extract of Origanum vulgare ssp. vulgare. TLC analyses were performed on silica gel plates using two mobile phases: ethyl acetate—methanol—water (65:15:5, v/v) (Figure 1a), and ethyl acetate—glacial acetic acid—formic acid—water (100:11:11:26, v/v) (Figure 1b). In both cases, a natural products reagent designated as NP was used as the visualizing reagent. On both chromatograms (Figure 1a,b), the pink spots with the highest relative fraction (R_F) values most likely originate from chlorophylls. The blue spots on the chromatograms originate from phenolic acids. The yellow spot with a low R_F value indicates the presence of flavonoids (Figure 1a). Experience shows that the mobile phase of ethyl acetate, glacial acetic acid, formic acid, and water (100:11:11:26, v/v) is better for detecting flavonoids and phenolic acids. Using this mobile phase (Figure 1b) clearly results in better separation of the extract components than the first mobile phase. In this case, TLC was used as a qualitative technique. To quantitatively assess the content of the extract’s individual components, the authors used HPLC-DAD (Figure 1c) and LC-ESI-QTOF-MS to identify its compounds. The composition of the extract is shown in Figure 1d.

2.1.2. Application in Profiling HPTLC Plates and Other Modern Methods Improving the Separation of Components

However, in order for the fingerprints of a given plant material to be of greater value in quality analysis, the appropriate chromatographic conditions must be developed in order to separate as many of the components present in the sample as possible, with clearly defined bands. Significantly more effective separations of active compounds are achieved on HPTLC than on TLC. HPTLC is an improved version of classic TLC, which uses modern materials, precise sample application and detection systems, and automated analysis stages. Compared to traditional TLC, HPTLC offers a number of specific advantages, which are particularly important when analyzing complex mixtures such as plant extracts. The main advantages of HPTLC compared to traditional TLC: higher resolution and sensitivity, greater repeatability and precision, the possibility of quantitative analysis, an automated process and rapid analysis of multiple samples, better documentation and archiving of results, increased selectivity and the possibility of multiple detection, environmental friendliness and cost savings [10,12].

The feasibility of TLC and HPTLC for analyzing ethanol extracts of Conocarpus lancifolius Engl. was compared by Prajapati et al. [58]. Analyses were performed on TLC and HPTLC plates coated with silica gel, using a mobile phase of toluene, acetone and formic acid in a ratio of 4.5:4.5:1. Anisaldehyde reagent was used to reveal spots on the chromatographic plates. TLC revealed two spots in extracts harvested during the rainy season and in both summer and winter. HPTLC, on the other hand, enabled the separation of seven components regardless of the season in which the extract was obtained. The ethanolic extract of Tinospora cordifolia (Willd.) Hook.f. & Thomson was analyzed using HPLC and TLC techniques. HPLC enabled the quantitative determination of berberine. However, quercetin was determined using TLC (silica gel 60F₂₅₄ plate and a mobile phase of toluene, ethyl acetate and formic acid in a volume composition of 5:4:1) [59]. Ganesan et al. [60] they examined an ethanol extract of Azadirachta indica A.Juss. using TLC and HPTLC techniques, employing a mobile phase consisting of a mixture of toluene, ethyl acetate and formic acid (9.2:0.7:0.1, v/v). The spots on the plates were then assessed after derivatization with a vanillin-sulphuric acid reagent. The spots on the plates were assessed after derivatization using the vanillin-sulphuric acid reagent. Four dark spots were visible at 254 nm, nine spots (including eight blue and one red) at 366 nm, and 13 violet spots at 520 nm. Densitometric analysis detected nine spots at λ = 366 nm and up to fifteen chromatographic spots at λ = 520 nm. These chromatographic conditions can be used to test the authenticity of Azadirachta indica plant extracts/medicines [60].

Sameemabegum et al. [61] analyzed the composition of the ethanol extract of Ipomoea pes-tigridis L. Depending on the TLC conditions used (i.e., changing the mobile phase and detection conditions), different amounts of components were observed in the extract. However, HPTLC analysis showed the presence of eight chromatographic spots under UV light at 254 nm and 12 spots at 366 nm. These results can be used for the identification and quality control of raw materials originating from Pluchea indica and I. pes-tigridis [39,61].

Chromatographic profiling was used to analyze samples from the above-ground parts of Salvia aegyptiaca L. and S. verbenaca L. S. officinalis L. [62]. Two different solvents were used to extract the samples for analysis: a mixture of ethanol and water (1:1, v/v) and ethyl acetate. The ethanol and water mixture was more efficient at extracting polar compounds, such as phenolic acids, while ethyl acetate was more efficient at extracting nonpolar compounds. Separations were performed on HPTLC silica gel 60 plates, either with or without F₂₅₄. The mobile phases consisted of toluene-ethyl acetate-methanol-formic acid (6:3:0.2:0.05, v/v) for polar compounds and petroleum ether-cyclohexane-ethyl acetate (3:2:6, v/v) for nonpolar compounds. Separations were also performed on HPTLC RP-18 plates, and the mobile phase composition was modified slightly to separate polar and nonpolar compounds: 6:3:1:1 (v/v) and 5:3:2.4 (v/v), respectively. Chromatographic separations were documented under white and UV light at wavelengths of 254 and 366 nm, and after spraying with five derivatizing reagents. HPTLC RP-18 plates were used for analyses requiring long incubation times, such as genotoxicity or hormonal assays, to limit zonal diffusion during bioautographic studies [62].

Darina et al. [63] used HPTLC fingerprinting analysis to compare the effect of plant material fermentation on the phenolic profiles of extracts from selected parts of medicinal plants: leaves of Rosmarinus officinalis L., Ficus carica L., Backhousia citriodora F.Muell., Salvia officinalis, Salvia apiana Jeps. and leaves and flowers of Olea europaea L. Additionally, profiling for antioxidant properties was examined. Ethyl acetate extracts were used for analysis, and plates were separated in the mobile phase: n-hexane-ethyl acetate-acetic acid (15:9:1, v/v). HPTLC fingerprints of phenolic compounds were analyzed after spraying with iron(III) chloride, and antioxidants after spraying with 2% DPPH solution. The greatest differences in the fingerprints of fermented and unfermented extracts were observed for R_F in the range of 0.2 to 0.5, which corresponds to the R_F values of phenolic acids [63].

To improve the separation of components in plant extracts, modern techniques such as the multiple gradient method (GMD) can be used to develop HPTLC plates. This involves performing analyses using multiple mobile phases on the same TLC or HPTLC plate (dried after each development), gradually changing the composition of the mobile phase in a gradient manner to better separate mixtures of compounds with different physicochemical properties.

Spangenberg et al. [64] used the GMD-TLC technique to analyze orange peel extract, employing seven mobile phases. They also combined. HPTLC with diode array detection (DAD), enabling them to obtain ultraviolet-visible (UV-Vis) spectroscopy and fluorescence spectra directly from the plate. This combination of instruments enabled the separation of over 50 compounds found in the extract.

2.1.3. Application of TLC for Profiling Plant Formulations and Detecting Adulterations and Contaminants in Them

Traditional medicines are widely used in Asian countries as part of traditional medical systems such as Traditional Chinese Medicine, Ayurveda and Siddha. TLC profiles of herbal teas and other complex herbal preparations containing several or even dozens of ingredients are published. Typically, these articles present photographs of chromatograms and/or densitograms with chromatographic band area values, without using chemometric analysis or reference standards [65,66,67,68,69,70,71,72,73,74].

Some studies, such as the analysis of the eight-herb Shenshuaifu formulation used to treat kidney disease, were conducted in accordance with the 2020 edition of the Chinese Pharmacopoeia. The quality of the SSF was controlled using TLC analysis on GF₂₅₄ silica gel with an ethyl acetate: glacial acetic acid: methanol: water mobile phase at a ratio of 5.5:1:0.2:1 (v/v). Spot inspection was performed under ultraviolet light. Analysis of the test product’s chromatogram, in which the spots of the same colour corresponded to the reference standards’ locations, demonstrated the Shenshuaifu formulation′s stability [75].

Herbal teas may become contaminated with fungal toxins during the drying and/or storage process. It is crucial to detect their presence, as extreme cases can be fatal. Haq et al. [76] examined the presence and quantity of mycotoxins and ochratoxin A in 15 samples of medicinal herbs and shrubs using TLC. For the TLC study, they used silica gel 60F₂₅₄ plates. Mycotoxin analysis was performed using a mobile phase of xylene–acetone–chloroform (30:10:60 v/v). Mycotoxin detection was performed at 365 nm. Ochratoxin A was developed on a chromatographic plate using a mobile phase of toluene, ethyl acetate and formic acid (50:40:10 v/v). It was then detected on a chromatographic plate using 20% aluminium chloride. TLC revealed the presence of aflotoxins in four samples and ochratoxin A in two. These results were confirmed by HPLC.

TLC can also be used to detect the adulteration of herbal remedies with synthetic drugs. Herbal preparations intended for the prevention or treatment of diabetes, for example, may contain synthetic antidiabetic drugs. Purohit et al. [77] developed an HPTLC method that enables the simultaneous detection and quantification of metformin, pioglitazone, glipizide and glimepiride in herbal preparations. The chromatographic conditions are detailed in the quantification section [77].

Contamination of herbal formulations with synthetic antidiabetic drugs such as metformin and glibenclamide was investigated by Minh et al. [78]. Analyses were performed on a silica gel 60F₂₅₄ TLC plate, with n-butyl acetate, methanol and formic acid (11:2.5:1.5 v/v) acting as the mobile phase. Spot detection was performed at 254 nm. The R_F values of metformin and glibenclamide were 0.05 and 0.77, respectively. An aqueous silver colloid was incorporated into the chromatographic spots to facilitate TLC-SERS analysis. Surface-enhanced Raman spectroscopy (SERS) spectra were recorded for each substance using a LabRAM Raman CCD camera and a laser beam at λ = 633 nm. The LODs were 0.5 µg/spot for metformin and 1 µg/spot for glibenclamide. This method was used to analyze seven herbal products. Four of the preparations were found to be contaminated with either metformin or glibenclamide, and one sample was contaminated with both of these synthetic antidiabetic drugs [78].

Mwankuna et al. [79] developed the TLC methods for detecting metronidazole, trimethoprim, sulfamethoxazole, sildenafil, paracetamol, pyrimethamine, sulfadoxine, acetylsalicylic acid, ibuprofen, diclofenac, quinine, and lumefantrine in herbal products. These TLC methods can be used for preliminary screening of herbal products for the presence of adulterants, which is an important step in ensuring the quality and safety of these products.

2.1.4. TLC Coupled with Other Methods

TLC Coupled with Chemometric Methods and Image Analysis

As the detection of chromatographic signals provides a large quantity of data, chemometric methods (i.e., statistical or mathematical methods) are frequently employed for their analysis. These methods were described in a review on the use of fingerprinting in the quality control of herbal medicines, which emphasized the importance of validating chemometric methods.

Principal component analysis (PCA) or hierarchical cluster analysis (HCA) are most commonly used. They were used, for example, for analysis of Curcuma xanthorrhiza D.Dietr. [15], Cyanthillium cinereum (L.) H.Rob. [80] collected in various places as well as Fraxini cortex in order to distinguish it from other adulterating species [14]. PCA and HCA were also used for HPTLC fingerprint analysis of multi-component preparations such as Sanwujiao tablets [81] as well as Kidney Tea Granules [82], and additionally orthogonal partial least squares discriminant analysis (OPLS-DA) was used.

In addition to principal component analysis (PCA), sum-of-ranking-differences (SRD) analysis was used to analyze the chromatographic fingerprints of six commercially available Origanum vulgare L. leaves in Serbia, as well as their effect-oriented antimicrobial, antioxidant and α-amylase-inhibiting activities. The data used for the chemometric analysis were the peak areas obtained after processing the chromatograms following the effect-oriented analysis. The authors suggest that the SRD method is highly informative and can be used to distinguish between varieties, as well as to determine the theoretically ideal herbal preparation [33].

The use of chemometric methods is possible thanks to the appropriate documentation of chromatograms. This can be achieved by taking photos with a digital camera or even a mobile phone and processing the images using software such as ImageJ [33].

Alternatively, you could use a modern device such as the TLC Visualizer. The TLC visualizer was used by Kartika Dewi and Kartini [83] for analysis of Hibiscus sabdariffa L. The ethanol extract of Hibiscus sabdariffa and cyanidin 3-O-glucoside (used as a reference) were investigated using silica gel 60F₂₅₄ plates and an ethyl acetate–formic acid–glacial acetic acid–water (100:11:11:2, v/v) mixture as the mobile phase. Evaluation of the separation was performed without a visualizing reagent, with detection in visible light. The ethanol extract of Hibiscus sabdariffa, as well as chlorogenic acid and caffeic acid as references, were investigated using silica gel 60F₂₅₄ plates (Merck KGaA, Darmstadt, Germany) and a mobile phase of ethyl acetate, formic acid, glacial acetic acid and toluene in a ratio of 80:11:11:20:19 (v/v). The evaluation of the separation was performed using an NP (Sigma Aldrich, St. Louis, MO, USA)/PEG (Merck KGaA, Darmstadt, Germany) visualizing reagent with detection in UV light at 366 nm. The obtained chromatograms were documented using a TLC visualizer (Camag, Muttenz, Switzerland), which reads the R_F values from the chromatographic plate as well as the heights and areas of the chromatographic peaks (spots). This enables quantitative analysis of the tested substances [83].

Traditional thin-layer chromatography (TLC) can be combined with image analysis (IA) to automate and streamline the analysis process. TLC-IA uses digital images of TLC plates to detect and quantify the components of a mixture, thus eliminating the need for manual spot measurement and interpretation. The main objectives of IA in TLC include: determining the R_F (retardation factor) value of each spot; measuring the intensity and area of the spot, which correlates with the amount of substance; comparing samples (e.g., from different plant extracts or extraction conditions); evaluating the quality of separation—number, sharpness, and shape of spots. To apply IA in TLC, the TLC plate should be scanned or photographed, e.g., with a CCD camera, smartphone, or scanner, under uniform lighting (preferably UV 254 or 366 nm). Then, use the free ImageJ/Fiji software or a commercial program dedicated to TLC/HPTLC, i.e., JustTLC (Sweday), to process the image. These programs detect chromatographic spots on the plate background [12,84,85,86]. They can generate densitometric chromatograms and measure the intensity of spots proportional to the amount of substance, enabling quantitative analysis of the substances being measured, but their accuracy is lower than that of professional TLC densitometers.

An et al. [84] used TLC-IA to analyze five samples of Fritillaria bulbus, which is known for its antipyretic and antitussive properties. The analyses were performed on silica gel 60F₂₅₄ plates using a mobile phase consisting of ethyl acetate, petroleum ether (60–90°), methanol and ammonium hydroxide in volumes of 14:6:2:1, respectively. An anisaldehyde-sulphuric acid reagent was then used to visualize the chromatographic spots. TLC-IA enabled differences between the five Fritillaria bulbus samples to be detected, including differences in their source. This method can be used to control the quality of Fritillaria herbs.

Wróbel-Szkolak et al. [85] used hyperspectral imaging to analyze plates after their chromatographic separation (ethyl acetate-acetone-water, 8:2:2) of extracts from 70 Polish grasses. The image of the plate was obtained using a 5-megapixel microscope camera. Various combinations of illumination and filtering were used. For further analysis, were used principal component analysis and developed a new method principal component artificial coloring of images (PCACI). Hyperspectral photography it turned out effective as multi-mode densitometry. Similarly, Gadowski et al. [86] demonstrated the advantages of high dynamic range using TLC-analyzed fingerprints of Gentiana extracts.

Although TLC-IA is rarely used, it offers many advantages. Automation streamlines the analysis process by eliminating the need for manual measurements and subjective evaluations. TLC-IA results are also more precise and reproducible than those of traditional TLC analysis. TLC-IA also shortens analysis time, enabling multiple samples to be analyzed rapidly and efficiently. The analytical results are objective because the influence of subjective factors on the results is limited. Furthermore, TLC-IA provides greater accuracy in spot measurement and retention factor determination.

The Selected Methods for Identifying Components of Plant Extracts Separated by TLC

Thin-layer chromatography (TLC) is used to separate plant extracts and provides information solely on the relative retention factor (R_F) values of the substances present in the sample. Appropriately selected derivatizing reagents can be used to classify chromatographic bands into the correct compound classes. Comparison of the R_F values of the substances in the extract with those of a reference substance indicates the probability that they are the same substances. It is not possible to fully identify compounds using only TLC and HPTLC, as compounds belonging to the same class have similar spectra obtained through spectrodensitometric analysis. Therefore, other techniques such as MS, NMR and SERS are required for full identification.

The combination of TLC and MS is an analytical technique used to identify and characterize compounds separated on a TLC plate. MS enables the identification and determination of molecular masses. TLC-MS does not have a direct connection. There are several ways to combine TLC with MS, depending on how the substance is transferred from the plate to the mass spectrometer. After the substances are separated and the bands are detected, e.g., under UV light, the TLC plate is placed in the TLC-MS interface. There, an appropriate solvent extracts the compound from each band. Then, the analyte is transferred to the mass spectrometer for identification. Direct desorption DESI (Desorption Electrospray Ionization) or DART (Direct Analysis in Real Time) from the plate (TLC–DART–MS, TLC–DESI–MS) allows analysis directly from the surface of the plate. The sample does not need to be extracted—ions are formed directly from the spot. The spot in TLC–ESI–MS analysis is scraped off the plate or extracted with a solvent, and the extract is introduced into the MS using Electrospray Ionization (ESI) [10,12,22,24,25].

A quantitative analysis of metformin, a contaminant in the herbal antidiabetic formulation, was performed using HPTLC combined with densitometry. The detected metformin bands were then identified by TLC-MS. Metformin spots were eluted using a TLC-MS interface and analyzed by MS. The MS spectra were then matched with standards to confirm the presence of metformin as an adulterant in two out of eight tested antidiabetic herbal formulations [77]. Similarly, TLC-MS analysis confirmed the presence of β-caesalpin and α-caesalpin in the Caesalpinia bonduc leaf extract. After validating the method, the compounds were quantified using the TLC–densitometric method [87]. TLC-MS was also used to determine the chemical profile of the triterpenes in the Andean plant Cecropia angustifolia Trécul [88]. Bioautography TLC-MS analysis of the ingredients in the polyherbal formulation Itrifal Muqawwi Dimagh revealed scopoletin, tannic acid, ellagic acid, and catechin as potential acetylcholinesterase (AChE) inhibitors. These compounds were analyzed using silica gel 60F₂₅₄ plates and a chloroform-methanol (9:1, v/v) mobile phase. Densitometric analyses were performed before and after derivatization with 5% anisaldehyde-sulphuric acid reagent at 254 nm, 366 nm, and 540 nm [89]. HPTLC analysis was used to identify the main compounds present in the bioactive fraction of Haldina cordifolia (Roxb.) Ridsdale, a potential herbal remedy for obesity. The most active fraction contained five compounds with respective R_F values of 0.82, 0.65, 0.57, 0.44, and 0.26. HPTLC combined with multistage MS (HPTLC-MSn) identified the following compounds in this fraction: caffeine, quercetin, β-sitosterol, naringin, and ascorbic acid [90].

HPTLC-MSn was also used to study horse chestnut bee pollen samples from Slovenia and Turkey. Analyses were performed on HPTLC plates coated with silica gel 60F₂₅₄ (Merck KGaA, Darmstadt, Germany)and amine-modified gel. The plates had to be cleaned before use. The former were purified with a methanol and formic acid solution (10:3, v/v), and the latter with a methanol and formic acid solution (10:5, v/v). Bee pollen extract analyses were performed on 60F₂₅₄ silica gel plates using the mobile phases of ethyl acetate–formic acid–acetic acid–water (10:1.1:1.1:2.6, v/v) and ethyl acetate–dichloromethane–formic acid–acetic acid (10:2.5:1:1.1, v/v) using plates precoated with silica gel modified by NH₂ groups. The following twelve compounds were identified in the samples: five phenylamides, six isorhamnetin glycosides, and gluconic acid. Only the Turkish bee pollen sample contained glycosylated phenolic acid (caffeic acid hexoside) [91]. HPTLC (silica gel 60F₂₅₄, ethyl acetate–formic acid–water, 50:3:3, v/v) was used to determine the presence of antibacterial compounds in plants of the Combretaceae family. MS analysis allowed for the full identification of these compounds [92].

HPTLC-MS on silica gel 60F₂₅₄ using a solvent system of ethyl acetate, methanol, and formic acid (100:18:9, v/v) was employed to analyze the methanol inflorescence extract of Sphaeranthus indicus. Ten extract fractions with different compositions were analyzed. MS analysis made identification of the individual components possible [93].

The combination of TLC and NMR (Nuclear Magnetic Resonance) is a very useful combination of techniques in the analysis of organic compounds—especially in the synthesis and purity analysis of reaction products. TLC allows you to see if a sample contains a single component (purity) and how it moves on the plate (polarity). NMR provides information about the structure of the molecule, the arrangement of atoms, and functional groups. There are several ways to “combine” TLC with NMR. The first way is to use TLC to check whether the sample contains only one component (one spot on the TLC plate). Then, a larger amount of this fraction can be collected, dried, dissolved in a suitable deuterated solvent, and an NMR spectrum can be performed. The second method is to elute the TLC spot and NMR of a single fraction. If there are several spots on the TLC, the spot of interest can be scraped off the TLC plate (e.g., using a spatula), the adsorbent (usually silica) can be washed with a small amount of solvent (e.g., CH₂Cl₂, MeOH), evaporate the solvent, collect the dry substance, dissolve it in a deuterated solvent, and perform NMR. The third method is hybrid TLC–NMR techniques (automated systems), namely, modern research laboratories have automated TLC–NMR systems. Special plates and devices are used to transfer the spot directly from TLC to the NMR probe. This technique is called HPTLC–NMR. It requires special equipment, often coupled with HPLC–NMR [10,12]. The active compounds in the Dioscorea bulbifera tuber extract were separated using column chromatography and then purified using preparative thin-layer chromatography. Then, the purified compounds were subjected to nuclear magnetic resonance (NMR) analysis to determine their structure [94]. The identification and quantitative determination of stachydrine and choline in 39 purchased samples of herbs from the genus Citrus, including 10 samples of Zhishi, 10 samples of Qingpi, 10 samples of Chenpi, and 9 samples of Zhiqiao was performed using silica gel 60F₂₅₄ plates and a mobile phase of ethyl acetate, 95% ethanol, and formic acid (10:4:5, v/v) [95]. After development and drying, the plates underwent densitometric scanning. Meanwhile, a quantitative analysis for synephrine was established using the HPLC-UV method. The presence of stachydrine and choline in the herbal samples was confirmed by NMR and HRMS results. Preparative TLC was then used to isolate the phytoconstituent fractions from the Ficus carica leaf extracts. On the chromatogram, four bands with R_F values of 0.46, 0.55, 0.74, and 0.85 were visible. The following substances were identified within these bands by NMR and LC-MS techniques: umbelliferone, furocoumarins, such as psoralen and bergapten; various fatty acids; pentacyclic triterpenoids, such as calotropene acetate and lupeol; and stigmasterol [96]. The extracts from the Rhodiola rosea products were preliminarily examined using TLC. The composition was identified using NMR. HPLC-DAD analysis confirmed the presence of rosavin, salidroside, and p-tyrosol in the extracts [97].

The combination of TLC and SERS is a very interesting and increasingly used approach in chemical analysis, combining the separation of mixtures with highly sensitive spectroscopic detection. TLC is used to separate the components of a mixture on a plate coated with a layer of adsorbent (e.g., silica). SERS is a spectroscopic technique that uses metal nanostructures (usually Ag or Au) to increase the intensity of the Raman signal by up to 10⁶–10⁸ times. The combination of TLC and SERS involves first separating the mixture on a TLC plate. The spots are then identified (e.g., under UV light), followed by the application of a SERS substrate (e.g., silver or gold nanoparticles) to selected spots and measurement of the Raman spectrum directly on the plate. The advantages of TLC–SERS include the possibility of identification without elution; high sensitivity—SERS enables the detection of trace amounts of analyte (nM–pM); selectivity—Raman spectra are characteristic of a specific compound; speed and simplicity—fewer sample preparation steps than in LC-MS [10,12]. TLC-SERS can identify separated substances, making it an effective method for investigating the adulteration of herbal preparations with synthetic antidiabetic drugs [78].

2.2. TLC in Quantitative Analysis

In many cases, it is possible to obtain good separation of the sample components if the sample and matrix are not very complex. This can be achieved by using plates with a concentration zone, among other methods. These chromatograms can be used to quantitatively analyze the components present in the test samples. The following variants can be used for quantitative determinations of a specific component. The spots on a chromatographic plate can be analyzed densitometric using a TLC densitometer after the components have been separated. Alternatively, the chromatographic spots can be scraped off, the component eluted, and spectrophotometric analysis performed. Image analysis is also possible. Of these three methods, analysis using a TLC densitometer is the most accurate [12,22,23].

Green, light-roasted, and dark-roasted coffee infusions were prepared using immersion and pressure brewing methods. The infusions were analyzed on plates coated with 60F₂₅₄ silica gel and a concentration zone. After applying the samples to the plates and developing them with a mobile phase consisting of chloroform, ethyl acetate, methanol, and formic acid at a volume ratio of 10:6:3:1, well-separated bands of trigonelline (TG), chlorogenic acid (CGA), and caffeine (CF) became visible under UV light. Standard solutions of these three substances were subjected to a similar chromatographic analysis. HPTLC plates with a concentration zone allow for quick and easy sample application and incorporate a purification and concentration step, resulting in compact chromatographic bands. The clearer the chromatographic bands, the better the separation and the lower the theoretical plate height (H), which indicates a more efficient chromatographic system. The substances on the chromatographic plates underwent densitometric analysis to generate densitograms (Figure 2 and Figure 3). Based on the densitometric analysis of the standards, calibration curves were established, and the content of trigonelline, chlorogenic acid, and caffeine in individual coffee infusions was calculated using these curves. The R_F values of TG, CGA and CF are consistent with the R_F values of the standards for these compounds. The absorption spectra of the TG, CGA and CF standards also corresponded to the absorption spectra of these compounds in brewed and espresso coffee samples. This method is selective, precise, accurate, and sensitive; therefore, it can simultaneously determine the content of caffeine, trigonelline, and chlorogenic acid in coffee prepared using immersion and pressure brewing methods [98].

Thin-layer chromatography combined with densitometry enables the separation and subsequent determination of biologically active substances present in samples from various parts of plants and plant formulations (dietary supplements), as well as in other samples that contain biologically active substances derived from plants (Table 1). The presented tally shows that TLC combined with densitometry allows for the quantitative determination of one to six biologically active substances in samples. The largest number of publications, namely 28, concerned the quantitative determination of selected biologically active compounds found in various parts of plants. During the period under discussion, β-sitosterol (BS) [99,100,101,102,103,104], lupeol (LU) [99,100,101,102,104,105] as well as quercetin (Q) [106,107,108,109,110] were the most frequently quantified compound in various parts of plants. In the mentioned period, only ten publications were devoted to the quantitative determination of biologically active compounds of plant origin in plant formulations. Namely, in multi-component plant formulations, the following biological active substances were quantitatively determined: (α + β) boswelics acids, β-asarone, isoeugenol, 6-gingerol, and piperine [111], gallic acid and eugenol [112] gallic acid and quercetin [110], andrographolide, columbin, gallic acid, p-coumaric acid, piperine, and oleanolic acid [113], quercetin, curcumin, and ascorbic acid [114], piperine, cinnamaldehyde, and 1,8-cineole [115], alizarin [116], andrographolide [117], mahanimbine and koenimbine [118], atropine, rutin, and vanillin [119]. The use of HPTLC in the quantitative study of caffeine trigonelline and chlorogenic acid content in green, light and dark roasted coffee bean infusions has also been reported [98]. The HPTLC plates coated with silica gel 60F₂₅₄ with a concentrating zone used in this study enabled quick and easy application, purification and concentration of the sample, which made the chromatographic system more efficient and, consequently, compact chromatographic bands of caffeine, trigonelline and chlorogenic acid were obtained. Determining the content of the main active compounds found in coffee beverages is a very important factor enabling both monitoring the consumption of these compounds and determining their previously unknown beneficial and adverse effects. In addition, curcumin, piperine, and capsaicin were successfully determined using TLC in polyherbal soup and in a South Indian spice soup [120,121]. It is worth emphasizing the publication which showed that herbal antidiabetic products were adulterated with synthetic compounds that lower blood sugar levels [77]. The developed HPTLC-MS method enabled the simultaneous identification and quantification of undeclared synthetic drugs such as metformin, pioglitazone, glipizide and glimepiride in herbal and dietary supplements. Metformin was found in two of the eight products tested [77]. Elbaz et al. [122]. developed a TLC method for determining the pesticide residues of imidacloprid (IMD) and deltamethrin (DLM) in thyme and guava leaves. To achieve good separation of the assayed substances and dibutyl phthalate as an internal standard, they used silica gel 60F₂₅₄ impregnated with chitosan nanoparticles (ChTNPs) 0.5%.

Table 1 shows that the TLC technique combined with densitometry allows the development of TLC and HPTLC methods that are selective, accurate, precise, sensitive, and more economical and ecological than HPLC, because HPTLC and TLC consume much less solvents than HPLC. It should be noted that HPTLC methods are slightly more expensive than TLC, which is due to the fact that HPTLC plates are more expensive than TLC plates. However, the accuracy of the results presented in some publications may be questionable. This applies to the obtained limits of quantification of the developed method in terms of the subsequently determined calibration curves, when the LOQ is within the range of the calibration curve [77,99,101,111,120,123,124,125]. Full method validation should include assessment of method specificity, sensitivity (LOD and LOQ), intraday and interday precision, and accuracy. The US Pharmacopeia recommends additionally assessing the method’s robustness. Yang et al. [126] did not assess the sensitivity of the proposed method for determining alisol B 23-acetate (ABA), alisol A (AA), alisol B (AB) and alisol C 23-acetate (ACA) in Alisma orientale (i.e., the publication did not determine the LOD and LOQ for the individual biologically active compounds determined). The specificity of the method should be confirmed by the fact that the substances being determined are separated from their impurities or related substances under the applied chromatographic conditions. However, assessing the specificity of the method based on the consistency of the R_F retardation coefficient value and the spectrum of the biologically active substance standard with the R_F retardation coefficient value and the spectrum of the substance from the tested sample is insufficient [110,116,119,120,125,127,128,129,130]. The robustness of an analytical procedure refers to the stability of the obtained results in the face of minor changes in the measurement conditions described in the analytical procedure. The robustness of a TLC method is influenced by: the stability of the analyzed solutions, the analyte extraction time, the pH of the mobile phase, the composition of the mobile phase, the chromatographic plate activation temperature, the chromatographic plate activation time, the type of chromatographic plate (different batch number/different supplier), the wavelength at which the densitometric measurement is performed, the distance at which the chromatographic plate is developed, and the saturation time of the chromatographic chamber with mobile phase vapor. Therefore, it is recommended to select seven parameters that, in the experimenter’s opinion, will have the greatest impact on the analytical result and, based on these parameters, plan the experiment in accordance with previous scientific reports [131]. Therefore, the evaluation of the robustness of the proposed TLC methods for determining biologically active substances in plants, herbs, and plant formulations [100,105,106,108,109,132,133,134,135] based on changes in one, two, or three analysis parameters seems insufficient. An important issue in terms of method reproducibility is the precise specification of all analysis conditions. For example, determinations of quercetin (Q) in Cyperus rotundus (Linn.) [108], as well as β-sitosterol (BS) and lupeol (LU) in leaf extracts of Bauhinia vahlii Fern.-Vill. [102], will be difficult to reproduce due to the lack of information provided in the publications on the chromatographic sorbent (chromatographic plates) used.

Table 1. Plant materials quantitative analyzed by TLC.

Matrix/Plant Materials	Chromatographic Conditions	Chromatographic and Statistical Parameters	Refs.
Plants
Medicine mulberry (Morus nigra L.) Cyanidin-3-O-glicoside (C3G) Cyanidin-3-O-rutinoside (C3R)	HPTLC–densitometry λ = 550 nm Silica gel 60F254 formic acid–water–n-butanol (9.1:8.4:32.5, v/v)	Linearity (μg/spot): 0.2–1.0 (for C3G, C3R) LOD (µg/spot): 0.048 (for C3G); 0.036 (for C3R) LOQ (µg/spot): 0.15 (for C3G); 0.11 (for C3R) Intraday precision: 1.32% (for C3G); 3.62% (for C3R) Interday precision: 2.67% (for C3G); 3.04% (for C3R) Recovery: 98.09% and RSD = 3.08% (for C3G); 98.66% and RSD = 2.93% (for C3R)	[136]
Solanum xanthocarpum Schrad. & Wendl. Solasonine (SN) Solamargine (SM) Khasianine (K) Solasodine (SD) Diosgenin (D)	HPTLC–densitometry λ = 640 nm for SN, 440 nm for K and SM; 610 nm for SD, and 430 nm for D Silica gel 60 GF254 n-propanol–ethyl acetate –10% glacial acetic acid in water (4:8:3, v/v) for SN, SM, K toluene–ethyl acetate– diethylamine (6:2:0.3, v/v) for SD, D	RF = 0.15 ± 0.01, 0.22 ± 0.02, and 0.31 ± 0.02 for SN, SM,K, respectively; and 0.38 ± 0.04 and 0.50 ± 0.03 for SD and D Linearity (µg/spot): 0.2–1.0 (for SN, SM, K, SD, D) LOD (µg/spot): 0.031 (for SN), 0.009 (for SM), 0.033 (for K), 0.045 (for SD), 0.053 (for D) LOQ (µg/spot): 0.094 (for SN), 0.029 (for SM), 0.10(for K), 0.136 (for SD), 0.16 (for D) Intraday precision: 2.08–2.87% (for SN, SM, K, SD), 3.25 (for D) Interday precision: 1.58% (for SN); 2.44–2.95 (for SM, K, SD), 3.14 (for D) Recovery (%): 98.41, 98.58, 98.82, 100.34, 99.89 (for SN, SM, K, SD, D, respectively)	[137]
Hypericum species Hyperforin (HyF) Hypericin (HyP) Hyperoside (HyS)	HPTLC–densitometry λ = 366 nm Silica gel 60F254 n-hexane–ethyl acetate (8:2, v/v) (for HyF) toluene–chloroform–ethyl acetate–formic acid (8:5:35:0.6, v/v) (for HyP) ethyl acetate–formic acid–acetic acid -water (15:2:2:1, v/v) (for HyS)	Linearity (ng/band): 400–1400 (for HyF) 20–100 (for HyP) 10–100 (for HyS) LOD (ng/band): 120 (for HyF); 6 (for HyP); 3 (for HyS) LOQ (ng/band): 400 (for HyF); 20 (for HyP); 10 (for HyS) Intraday precision: <2% (for HyF, HyP, HyS) Interday precision: <2% (for HyF, HyS), <3% (for HyP) Accuracy (Recovery): RSD (%) 1.35–1.93 (for HyF), 1.14–1.61 (for HyP), 0.76–1.81 (for HyS)	[138]
Alisma orientale (Sam.) Alisol B 23-acetate (ABA) Alisol A (AA) Alisol B (AB) Alisol C 23-acetate (ACA)	HPTLC–densitometry λ = 254 nm (ACA) and 208 nm (ABA, AA, AB) Silica gel 60F254 cyclohexane–ethyl acetate (1:1, v/v)	Linearity (µg/zone): 0.125–2.0 (for ABA); 0.0834–2.0 (for AA, AB, ACA) Intraday precision: <1% (for ABA, AA, AB, ACA) Interday precision: <1% (for ABA, AA, AB, ACA) Repeability: RSD < 1% (for ABA, AB); <3% (for AA, ACA) Stability: RSD < 1% (for ABA, AB); <3% (for AA, ACA) Accuracy (Recovery): RSD (%) 3.27 (for ABA), 4.05 (for ACA), 2.07 (for AB), 2.78 (for AA)	[126]
Millettia pinnata (L.) Pierre (stem, bark) Karanjin (KR)	HPTLC–densitometry λ = 260 nm HPTLC–MS/MS for identification of isolated compound Silica gel 60F254 toluene–ethyl acetate–formic acid (7:3:0.3, v/v)	Linearity range (ng/band): 200–1200 LOD (ng/band): 21.5 LOQ (ng/band): 65.3 Repeatability (%SD): 1.88 Intraday precision (%RSD): 1.88–1.95 Interday precision (%RSD): 1.87–1.88 % Recovery: 94–104 Robustness (n = 6) (%RSD): <4%	[135]
Gum samples of Sterculia urens Roxb. Glucuronic acid (GlcUA)	HPTLC–densitometry λ = 580 nm Silica gel 60F254 1-propanol–water (7:3, v/v)	RF = 0.43 Linearity range (ng/band): 300–700 LOD (ng/band): 201.54 LOQ (ng/band): 610.74 Intraday precision (%RSD): 1.77 Interday precision (%RSD): 1.27 Accuracy: % Recovery 101.56; %RSD = 1.00 Specificity: Specific	[123]
Ziziphus mauritiana Lam. and Ziziphus nummularia (Burm.f.) Wight & Arn. Betulinic acid (BAC)	HPTLC–densitometry λ = 580 nm Silica gel 60F254 petroleum ether–ethyl acetate– toluene (7:2:1, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	Linearity (µg/spot): 2–10 LOD (µg/spot): 0.379 LOQ (µg/spot): 1.149 Intraday precision: 0.85% Interday precision: 1.22% Accuracy: 99.54%	[139]
fruits, leaves, root bark and stem bark of Dillenia indica Linn Betulinic acid (BAC) β-Sitosterol (BS) Lupeol (LU)	TLC–densitometry λ = 525 nm Silica gel 60F254 toluene–methanol–chloroform (8:1:1, v/v)	RF = 0.38 ± 0.01 for BAC, 0.54 ± 0.01 for BS and 0.65 ± 0.02 for LU Linearity (ng/band): 2000–6000; 200–1000; and 200–600 for BAC, BS, LU, respectively LOD (ng/band): 2.98, 95.36, 118.51 for BAC, BS, LU, respectively LOQ (ng/band): 9.02, 288.97, 359.12 for BAC, BS, LU, respectively Intraday precision (%RSD): 1.11, 1.73, 1.40 for BAC, BS, LU, respectively Interday precision (%RSD): 0.87, 1.81, 1.47 for BAC, BS, LU, respectively Accuracy (% Recovery) 99.19, 99.69, 100.95% for BAC, BS, LU, respectively Robustness (%RSD): <2	[99]
Flowers, fruits, root, stem bark and leaves of Cassia fistula L. β-Sitosterol (BS) Lupeol (LU)	HPTLC–densitometry λ = 525 nm Silica gel 60F254 toluene–methanol–chloroform (8:1:1, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.25 ± 0.01 for BS and 0.37 ± 0.01 for LU Linearity (ng/band): 40–120 for BS, LU LOD (ng/band): 13.86, 13.01 for BS, LU, respectively LOQ (ng/band): 41.99, 39.42 for BS, LU, respectively Intraday precision (%RSD): 1.33 for RT, 1.82–1.97 for Q Interday precision (%RSD): 1.02 for RT, 0.68 for Q Accuracy (% Recovery) 99.81% for RT, 100.97% for Q Robustness (%RSD): <2	[100]
Different plant parts of Uraria picta (Jacq.) Desv. β-Sitosterol (BS) Lupeol (LU)	HPTLC–densitometry λ = 525 nm Silica gel 60F254 toluene–methanol–chloroform (8:1:1, v/v) post-chromatographic derivatization using anisaldehyde-sulphuric reagent	RF = 0.53 ± 0.01 and 0.63 ± 0.01 (for BS and LU, respectively) Linearity range (ng/band): 200–600 for BS and LU LOD (ng/band): 129.455 for BS and 88.687 for LU LOQ (ng/band): 392.287 for BS and 268.749 for LU Intraday precision (%RSD): 1.43 for BS and 1.21 for LU Interday precision (%RSD): 1.27 for BS and 0.92 for LU Accuracy: % Recovery 99.86 for BS and 101.07 for LU %RSD 1.97 for BS and 0.64 for LU Specificity: Specific	[101]
Leaf extracts of Bauhinia vahlii Fern.-Vill. β-Sitosterol (BS) Lupeol (LU)	HPTLC–densitometry λ = 514 nm toluene–ethyl acetate–formic acid (8:2:0.2, v/v post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent for detection of BS	RF = 0.53 and 0.68 for BS and LU, respectively Linearity range (ng/band): 200–1400 for LU and 600–1300 for BS LOD (ng/band): 0.92 for LU and 1.69 for BS LOQ (ng/band): 3.07 for LU and 5.63 for BS Intraday precision (%RSD): <2 Interday precision (%RSD): <2 Accuracy (% Recovery) 106.41% for LU and 109.52% for BS %RSD of recovery: 1.038 for LU and 1.168 for BS Specificity: Specific	[102]
Tuber extract of Amorphophallus paeoniifolius (Dennst.) Nicolson Resveratrol (RV) β-Sitosterol (BS)	TLC–densitometry λ = 305 nm for RV (before derivatization) and 662 nm for BS (after derivatization) Silica gel G60 F254 toluene–ethyl acetate (7:3, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent for detection of BS	RF = 0.24 and 0.60 for RV and BS, respectively Linearity range (ng/band): 100–1000 LOD (ng/band): 10.6 for RV and 11.8 for BS LOQ (ng/band): 32.2 for RV and 35.9 for BS Intraday precision (%RSD): <2 Interday precision (%RSD): <2 Accuracy (% Recovery) 96.99–98.32% for RV and 98.31–99.27% for BS Repeatability (%RSD): 0.15 for RV and 1.01 for BS Specificity: Specific Robustness: Robust	[103]
Phyllanthus niruri L. Phyllanthin (PL)	HPTLC–densitometry λ = 279 nm Silica gel 60F254 toluene–ethyl acetate–formic acid (15:10.5:1.5, v/v)	RF = 0.67 Linearity (µg/band): 2.36–11.8 LOD (µg/band): 0.532 LOQ (µg/band): 1.612 Intraday precision (%RSD) = 8.87–9.43 Interday precision (%RSD) = 6.94 Specificity: Specific	[140]
Eight rhizomes from plants of the Zingiberaceae family Curcumin (C)	HPTLC–densitometry λ = 422 nm Silica gel 60F254 chloroform–methanol (40:1, v/v)	RF = 0.38 Linearity (ng/band): 200–1400 LOD (ng/band): 199.35 LOQ (ng/band): 604.08 Intraday precision (%RSD) = 2.94–5.03 Interday precision (%RSD) = 6.71 Accuracy (% Recovery): 91.62% and 102.42% Specificity: Specific	[124]
Leaves of Clerodendrum philippinum Schauer Hispidulin (H)	HPTLC–densitometry λ = 267 nm Silica gel 60F254 chloroform–methanol–formic acid (9:1:0.1, v/v) post-chromatographic derivatization using sulfuric acid–methanol reagent (5%)	RF = 0.53 Linearity (ng/spot): 100–500 LOD (ng/spot): 17 LOQ (ng/spot): 50 Intraday precision (%RSD): <2 Interday precision (%RSD) = <2 Accuracy (% Recovery): 97.73 Robustness: Robust	[133]
Leaf of Murraya koenigii L. Mahanimbine (MB)	HPTLC–densitometry λ = 285 nm Silica gel 60F254 hexane–ethyl acetate (7:3, v/v)	RF = 0.60 Linearity (µg/mL): 100–400 LOD (ng/spot): 45.50 LOQ (ng/spot): 77.92 Reproducibility: Reproducible	[130]
Citrus aurantium peel. Neohesperidin (NP)	TLC–densitometry λ = 254 nm Silica gel 60F254 ethyl acetate–methanol–water– formic acid (7.1:1.4:1:0.5, v/v)	RF = 0.54 ± 0.02 Linearity (ng/spot): 1000–3000 LOD (ng/spot): 290.05 LOQ (ng/spot): 878.96 Intraday precision (%RSD): <2 Interday precision (%RSD) = <2 Accuracy (% Recovery): 99.6–101.81, Robustness: %RSD < 2 Robust	[134]
Different parts of Capparis zeylanica Linn. Rutin (RT)	HPTLC–densitometry λ = 264 nm Silica gel 60F254 ethyl acetate-glacial acetic acid- formic acid–water (10:1.1:1.1:2.6, v/v)	RF = 0.418 ± 0.004 Linearity range (ng/spot): 400–1400 LOD (ng/spot): 14.10 LOQ (ng/spot): 42.73 Intraday precision (%RSD): <1 Interday precision (%RSD): <2 % Recovery 97.73–98.12 %RSD ≤ 0.01 Specificity: Specific	[116]
Herbal plants, including Ocimum basilicum L. Rutin (RT) Quercetin (Q)	HPTLC–densitometry λ = 254 nm Silica gel 60F254 toluene–ethyl acetate–methanol–formic acid (6:4:3:1, v/v)	RF = 0.25 ± 2.01 for RT and 0.80 ± 0.64 for Q Linearity (ng/band): 300–1300 for RT, Q LOD (ng/spot): 46.52, 81.79 for RT, Q, respectively LOQ (ng/spot): 140.96, 247.84 for RT, Q, respectively Intraday precision (%RSD): 1.54–1.79 for RT, 1.24–1.97 for Q Interday precision (%RSD): 1.82–2.41 for RT, 1.96–2.17 for Q Accuracy (% Recovery) 22.84–25.19% for RT, 54.00–55.29% for Q Robustness (%RSD): <2	[106]
Leaf of Annona reticulata L. Galic acid (GA) Quercetin (Q)	HPTLC–densitometry λ = 254 nm Silica gel GF254 toluene–ethyl acetate–formic acid (9:10:1.6, v/v)	Linearity (ng/spot): 200–1000 for Q and 200–1200 for GA LOD (ng/spot): 21.31 (for Q); 14.86 (for GA) LOQ (ng/spot): 64.57 (for Q); 55.04 (for GA) Intraday precision: <1% (for GA and Q) Interday precision: <2% (for GA and Q) Robustness (%, RSD): <1 (for AA, Q, C) Repeatability of measurement (%RSD): <1 (for GA and Q) Repeatability of application (%RSD): <1 (for GA and Q) Accuracy (%): 98.02–99.09 for Q and 99.28–100.26 (for GA) Specificity: Specific	[107]
Cyperus rotundus L. Quercetin (Q)	HPTLC–densitometry λ = 257 nm toluene–ethyl acetate–formic acid (3:4:2.5, v/v)	RF = 0.80 Linearity (ng/band): 100–700 LOD (ng/band): 30.08 LOQ (ng/band): 91.17 Instrument precision (n = 5): RSD = 0.94% Repeatability (n = 5): RSD = 1.05% Recovery 98–99% Specific—Q separated from rutin and catechin	[108]
Leaf extract of Manilkara hexandra Dubard Myricetin (M) Quercetin (Q)	HPTLC–densitometry λ = 254 nm Silica gel 60F254 toluene–ethyl acetate–formic acid (6:6:2.4, v/v)	RF = 0.6 for M, 0.7 for Q Linearity (µg/band): 0.5–3, 0.4–1.4 for M and Q respectively LOD (µg/band): 0.13, 0.072 for M and Q, respectively LOQ (µg/band): 0.40, 0.21 for M and Q, respectively Intraday precision (%RSD): 0.21–0.69, 0.29–0.78 for M and Q, respectively Interday precision (%RSD): 0.50–1.66, 0.58–1.44 for M and Q, respectively Accuracy (% Recovery) 99.85–100.12, 99.23–100.83 for M and Q, respectively Robustness (%RSD): <2	[109]
Desmodium oojeinensis (Roxb.) Hochr. bark and roots Betulin (BT) Stigmasterol (ST) Lupeol (LU)	TLC–densitometry λ = 520 nm Silica gel 60F254 hexane–ethyl acetate (8.5:1.5, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.42 ± 0.01, 0.27 ± 0.01, and 0.19 ± 0.01 for LU, ST and BT, respectively Linearity (ng/band): 200–600 for LU, ST and BT LOD (ng/band): 12.75, 18.02, 13.35 for LU, ST, BT, respectively LOQ (ng/band): 38.64, 54,59, 40.46 for LU, ST, BT, respectively Intraday precision (%RSD): <2 Interday precision (%RSD): <2 Accuracy (% Recovery): 97.43–97.69 for LU, 97.02–97.89 for ST, 97.68–98.18 for BT Robustness (%RSD): <2	[105]
Leaves, flowers, stems, seeds, and roots of Hygrophila schulli (Schumach.) Heine Stigmasterol (ST)	HPTLC–densitometry λ = 520 nm Silica gel 60F254 toluene—methanol (9:1, v/v) post-chromatographic derivatization using anisaldehyde—sulphuric acid reagent	RF = 0.47 ± 0.02 Linearity (ng/band): 100–500 LOD (ng/band): 6.87 LOQ (ng/band): 20.82 Precision (%RSD): <2 Accuracy (% Recovery): 98.86–99.22, %RSD < 1 Robustness: Robust	[132]
Parkia roxburghii (DC.) Merr. seed Catechin (CT)	HPTLC–densitometry λ = 302 nm Silica gel 60F254 ethyl acetate–acetic acid–formic acid–water (10:1:0.75:1, v/v)	RF = 0.61 Range of calibration curve (µg): 2–10 LOD (ng/spot): 12.32 LOQ (ng/spot): 37.23 Intraday precision (%RSD): <1 Interday precision (%RSD): <1 Accuracy (% Recovery): 99.54 Robustness: robust Specificity: specific	[128]
Gynura cusimbua S.Moore leaves Chlorogenic acid (CGA)	HPTLC–densitometry λ = 366 nm Silica gel 60F254 ethyl acetate–formic acid–acetic acid–water (100:11:11:2.6, v/v)	RF = 0.43 ± 0.01 Range of calibration curve (ng/spot): 50–250 LOD (ng/spot): 14.36 LOQ (ng/spot): 43.12 Intraday precision (%RSD): 0.63 Interday precision (%RSD): 1.78 Accuracy (% Recovery): 97.79–98.30 Specificity: specific	[127]
Different parts of Carica papaya L. Syringic acid (SA) Gallic acid (GA) p-Coumarin (PC) Caffeic acid (CFA)	TLC–densitometry λ = 302 nm for SA, 256 nm for PC, 200 nm for GA, 296 nm for CFA Silica gel 60F254 toluene–ethyl acetate–glacial acetic acid (8.5:1.5:0.1, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.51 ± 0.002, 0.62 ± 0.001, 0.29 ± 0.05, and 0.38 ± 0.01 for SA, PC, GA, and CFA, respectively Linearity (ng): 100–600 for SA, GA, PC, CFA LOD (ng): 60, 30, 40, 40 for SA, PC, GA, and CFA, respectively LOQ (µg): 200, 100, 100, 100 for SA, PC, GA, and CFA, respectively Intraday precision (%RSD): <1% Interday precision (%RSD): <2% Accuracy (% Recovery): 97.23–98.35, 97.72–98.36, 98.12–99.71, 98.26–99.13 for SA, PC, GA, and CFA, respectively Specificity: specific	[125]
Caesalpinia bonduc leaf extract β-Caesalpin (βCLP) α-Caesalpin (αCLP)	TLC–densitometry TLC-MS λ = 580 nm Silica gel 60F254 n-hexane–ethyl acetate (6:4, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.64 (for βCLP) 0.78 (for αCLP) Linearity (ng/band): 200–1200 for βCLP and αCLP LOD (µg/band): 43.34 and 22.74 for βCLP and αCLP, respectively LOQ (µg/spot): 131.36 and 68.91 for βCLP and αCLP, respectively Intraday precision: 1.52–1.78 for βCLP and 0.52–1.30 for αCLP Interday precision: 1.59–1.75 for βCLP and 0.89–1.65 for αCLP Accuracy: 95.16–99.75% for βCLP and 97.49–99.86% for αCLP Injection repeatability: 1.62 and 1.75 for βCLP and αCLP, respectively Scanning repeatability: 0.47 and 0.58 for βCLP and αCLP, respectively Robustness: Robust Specificity: Spectific	[87]
Herbal formulations
Majun Nisyan (α + β) Boswelics acids (BA) β-Asarone (A) Isoeugenol (IS) 6-Gingerol (G) Piperine (P)	HPTLC–densitometry Silica gel 60F254 toluene–ethyl acetate -chloroform–acetic acid (8:2:5:0.1, v/v)	RF = 0.102 ± 0.01 (for BA 0.982 ± 0.04 (for A), 0.850 ± 0.03 (for IS), 0.698 ± 0.03 (for G) 0.355 ± 0.01(for P) Linearity (µg/spot): 1–12 (for BA, A, IS, G, P) LOD (µg/spot): 1.060 (for BA); 1.405. (for A); 1.973 (for IS); 1.691 (for G); 2.090 (for P) LOQ (µg/spot): 3.214 (for BA); 4.258. (for A); 5.979 (for IS); 5.125 (for G); 6.334 (for P) Intraday precision: <2% (for BA, A, IS, G, P) Interday precision: <2% (for BA, A, IS, G, P) Accuracy: 86.42–105.75%	[111]
Polyherbal formulation Galic acid (GA) Eugenol (E)	HPTLC–densitometry Silica gel 60F254 Isopropyl alcohol–n-hexane–ethyl Acetate–glacial acetic acid (10:6:6:0.1, v/v)	RF = 0.608 ± 0.041 (for GA); 0.752 ± 0.035 (for E) Linearity (ng/mL): 1–10 (for GA, E) LOD (ng/mL): 7.85 (for GA); 8.78 (for E) LOQ ((ng/mL): 23.80 (for GA); 26.60 (for E) Intraday precision: <1% (for GA); <2% (for E) Interday precision: <2% (for GA); <1% (for E) Accuracy: 98.05–99.41%	[112]
Polyherbal formulations containing Terminalia species Galic acid (GA) Quercetin (Q)	HPTLC–densitometry λ = 271 nm (for GA) and 366 nm (for Q) Silica gel 60F254 toluene-isopropyl alcohol-acetic acid (7:2.5:0.5, v/v) post-chromatographic derivatization using anisaldehyde reagent for phenolic compounds; 2, 2-diphenyl-1 picrylhydrazyl reagent (DPPH) for antioxidant activity; vanilin reagent for terpenoids and phenolic compounds	Linearity (μg/mL): 5–10 (for GA); 1–6 (for Q) LOD (ppm): 800 (for GA); 5 (for Q) LOQ (ppm): 2400 (for GA); 8 (for Q) Reproducibility, RSD = 0.44–9.71%	[110]
Kapacurak Kuṭinīr Cūraṇam Andrographolide (AG) Columbin (CL) Gallic acid (GA) p-Coumaric acid (CA) Piperine (P) Oleanolic acid (OA)	HPTLC–densitometry λ = 254 nm for GA, CA, P (before derivatization) and 520 nm for AG, CL and OA (after derivatization) Silica gel 60F254 toluene: ethyl acetate: formic acid (7:3:0.5, v/v) post-chromatographic derivatization using vanillin–sulphuric acid reagent for AG, CL and OA	RF = 0.19 (for AG), 0.23 (GA), 0.28 (CL), 0.57 (CA), 0.64 (PP) and 0.66 (OA) Linearity (µg/band): 1–5 (for each compounds) LOD (ng/band): 0.0069, 0.0044, 0.0063, 0.0042, 0.0022, 0.00037 (for AG, GA, CL, CA, PP, OA, respectively) LOQ (ng/band): 0.0209, 0.0133, 0.0189, 0.0127, 0.0067, 0.00113 (for AG, GA, CL, CA, PP, OA, respectively) Intraday precision (%RSD): <3 Interday precision (%RSD): <3 Accuracy (% Recovery): 97.73 Standard stability (% RSD) <5	[113]
Herbal formulations Quercetin (Q) Curcumin (C) Ascorbic acid (ASA)	HPTLC–densitometry λ = 265 nm Silica gel 60F254 chloroform–ethyl acetate–formic acid (6:6:2.5, v/v)	Linearity (ng/spot): 500–1000 (for AA, Q, C); LOD (ng/spot): 12 (for AA); 6 (for Q); 4 (for C) LOQ (ng/spot): 36 (for AA); 18 (for Q); 13 (for C) Intraday precision: <2% (for AA); <1% (for Q, C) Interday precision: <2% (for AA); <1% (for Q, C) Robustness (%, RSD): <1 (for AA, Q, C) Repeatability (%RSD): <1 (for AA, Q, C)	[114]
Sitopaladi churna— Ayurvedic multi-herbal preparation Piperine (P) Cinnamaldehyde (CD) 1,8-Cineole (CN)	HPTLC–densitometry λ = 307 nm for PP, CD (before derivatization) and 599 for CN (after derivatization) Silica gel 60F254 toluene–methanol (9:1, v/v) post-chromatographic derivatization using vanillin– sulphuric acid reagent	RF = 0.22 ± 0.01 for PP, 0.54 ± 0.01 for CD, and 0.65 ± 0.01 for CN Linearity (ng/band): 100–500 ng/spot for PP, CD, and 600–3000 for CN LOD (ng/spot): 18, 24, 27 for PP, CD, CN, respectively LOQ (ng/spot): 54, 73, 483 for PP, CD, CN, respectively Intraday precision (%RSD): ≤2 for PP, CD and 2.4–4.1 for CN Interday precision (%RSD): <1 for PP, ≤2 for CD and 2.4–3.5 for CN Accuracy (% Recovery) 99.1–101.6 for PP, 98.8–100.7 for CD, 98.3–102.7 for CN Robustness (%RSD): <2 for PP, CD, and <5 for CN	[115]
Ayurvedic formulations Alizarin (AL)	TLC–densitometry λ = 259 nm Silica gel 60F254 toluene–ethyl acetate–formic acid (9:1.5:1, v/v)	RF = 0.50 ± 0.02 Linearity (ng/spot): 100–1000 LOD (ng/spot): 30.45 LOQ (ng/band): 92.28 Intraday precision (%RSD): <1 Interday precision (%RSD): <1 Accuracy (% Recovery) 96.75–100.43 Specificity: specific	[129]
Herbal hepatoprotective formulation Andrographolide (AD)	HPTLC–densitometry λ = 254 nm Silica gel 60F254 dichloromethane–toluene–ethyl acetate–formic acid (6:4:1:0.5, v/v)	RF = 0.69 Linearity (ng/spot): 500–3000 LOD (ng/spot): 31.50 LOQ (ng/spot): 95.48 Intraday precision (%RSD): <3 Interday precision (%RSD): <2 Accuracy (% Recovery) 99.74–99.84 Robustness (%RSD): <1 Ruggedness (%RSD): <1	[117]
Marketed herbal formulations Mahanimbine (MB) Koenimbine (KB)	HPTLC–densitometry λ = 285 nm for MB and 291 nm for KB Silica gel 60F254 hexane–ethyl acetate (7:3, v/v)	RF =0.48 and 0.60 for KB and MB, respectively Linearity range (ng/spot): 100–400 for MB and 50–450 for KB LOD (ng/spot): 32.81 for MB and 18.44 for KB LOQ (ng/spot): 72.81 for MB and 31.57 for KB Intraday precision (%RSD): <3 Interday precision (%RSD): <3 Accuracy (% Recovery) 95.1–98.4 for MB Reproducibility: Reproducible	[118]
Mansyadi Kwatha Atropine (AT) Rutin (R) Vanillin (V)	TLC–densitometry λ = 206 nm Silica gel 60F254 tetrahydrofuran–toluene– methanol–formic acid (5:3.5:2:0.5, v/v)	RF = 0.090 ± 0.0039, 0.290 ± 0.0099 and 0.679 ± 0.0056 for AT, R, and V, respectively Linearity (ng/band): 500–50000 for AT, and 500–5000 for R, and V LOD (ng/band): 1427.070, 119.559, 109.974, for AT, R, and V, respectively LOQ (µg/band): 4324.454, 362.300, 333.254 for AT, R, and V, respectively Intraday precision (%RSD): <2% Interday precision (%RSD): <2% Accuracy (% Recovery): 65.26–103.00, 98.27–104.70, 95.40–104.28 for AT, R, and V, respectively Robustness (%RSD): <2 Specificity: specific	[119]
Other samples containing the biological active substances occurring in plant
Rasam/a South Indian spice soup Piperine (P) Capsaicin (CP)	TLC–densitometry λ = 527 nm Silica gel 60F254 toluene–ethyl acetate (7:3, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.51 for PP and 0.40 for CP Linearity (µg/spot): 1–5 LOD (ng): 23.57 for PP and 7.67 for CP LOQ (ng): 76.84 for PP and 25 for CP Intraday precision (%RSD): 2% Interday precision (%RSD): <2% Accuracy (% Recovery): 97.78–99.18 for PP and 96.15–102.13 for CP	[121]
Rasam, a polyherbal soup Curcumin (CC) Piperine (P) Capsaicin (CP)	TLC–densitometry λ = 254 nm Silica gel 60F254 toluene–ethyl acetate (7:3, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.26, 0.40 and 0.47 for CC, PP, and CP, respectively Linearity (µg/spot): 2–7 LOD (µg): 3.98, 3.75, and 3.13 for CC, PP, and CP, respectively LOQ (µg): 12.05, 11.36, and 9.49 for CC, PP, and CP, respectively Instrumental precision (%RSD): 2 Intraday precision (%RSD): 1% Interday precision (%RSD): <2% Accuracy (% Recovery): 99.96–101.48 for CC, 99.93–101.48 for PP and 92.25–100.62 for CP Specificity: specific	[120]
Coffee bean infusions/ green, light and dark roasted coffee bean infusions Trigonelline (TG) Caffeine (CF) Chlorogenic acid (CGA)	TLC–densitometry λ = 270, 275 and 330 nm for TG, CF, and CGA, respectively Silica gel 60F254 with concentrating zone chloroform–ethyl acetate– methanol–formic acid, (10:6:3:1, v/v)	RF = 0.06, 0.18, 0.73 for TG, CGA, and CF, respectively Linearity (µg/band): 1.00–3.00 for TG, 1.50–5.00 for CGA and 1.00–3.00 for CF LOD (µg/band): 0.28, 0,27, 0,04 for TG, CGA, and CF, respectively LOQ (µg/band): 0.84, 0.82, 0.12 for TG, CGA, and CF, respectively Intraday precision (%CV): <3% Interday precision (%CV): <3% Accuracy (% Recovery): Coffee from an espresso machine: 95.4–101.8 for TG, 96.3–102.4 for CLA, 98.1–102.1 for CF Brewed coffee: 96.2–103.9 for TG, 98.7–104.1 for CLA, and 97.2–101.7 for CF Accuracy (% CV) Coffee from an espresso machine: 2.12, 2.33, and 1.75 for TG, CGA, and CF, respectively Brewed coffee: 2.54, 2.19, and 1.78 for TG, CGA, and CF, respectively Specificity: specific	[98]
Counterfeit herbal antidiabetic products Metformin HCl (MET) Pioglitazone HCl (PIO) Glipizide (GLP) Glimepiride (GLM)	HPTLC–densitometry and HPTLC-MS λ = 232 nm Silica gel 60F254 cyclohexane–dichloromethane– 1-propanol–saturated solution of ammonium acetate in acetic acid (7:5:2:2, v/v)	RF = 0.255, 0.461, 0.551, 0.791 (for MET, PIO, GLP, GLM, respectively) Linearity (ng/spot): 200–1200 (for MET, PIO, GLP, GLM) LOD (ng/spot): 186.39 (for MET), 191.66 (for PIO), 153.47 (for GLP), 222.34 (for GLM) LOQ (ng/spot): 564.84 (for MET), 580.77 (for PIO), 465.07 (for GLP), 673.77 (for GLM) Intraday precision: <9% Interday precision: <9% Accuracy: 97.40–105.43, 98.19–105.41, 100.25–103.13, and 98.82–104.38% (for MET, PIO, GLP, GLM, respectively) Robustness (%): <16%	[77]
Pesticide residues in thyme and guava leaves Imidacloprid (IMD) Deltamethrin (DLM) Dibutyl phthalate (internal standard—IS)	HPTLC–densitometry λ = 270.0 nm for IMD and 230.0 nm for DLM Silica gel 60F254 impregnated in chitosan nanoparticles (ChTNPs) 0.5% isopropyl alcohol for IMD and IS n–hexane–toluene–ethyl acetate (7:3:1, v/v) for DLM and IS	RF = 0.51 for of IMD and 0.89 for IS RF = 0.80 for of DLM and 0.61 for IS Linearity (µg/spot): 0.2–2.2 for IMD and 0.2–2.4 for DLM Accuracy [mean% ± SD]: 100.49 ±1.62 for IMD and 100.57 ± 0.39 DLM Intraday precision (%RSD): 1.92 for IMD, 1.39 for DLM Interday precision (%RSD): 1.92 for IMD, 1.92 for DLM LOD (µg/spot): 0.002 for IMD and 0.00116 for DLM LOQ (µg/spot): 0.0054 for IMD and 0.0035 for DLM Robustness (%RSD): <3% for IMD and DLM	[122]

Table 1. Plant materials quantitative analyzed by TLC.

Matrix/Plant Materials	Chromatographic Conditions	Chromatographic and Statistical Parameters	Refs.
Plants
Medicine mulberry (Morus nigra L.) Cyanidin-3-O-glicoside (C3G) Cyanidin-3-O-rutinoside (C3R)	HPTLC–densitometry λ = 550 nm Silica gel 60F254 formic acid–water–n-butanol (9.1:8.4:32.5, v/v)	Linearity (μg/spot): 0.2–1.0 (for C3G, C3R) LOD (µg/spot): 0.048 (for C3G); 0.036 (for C3R) LOQ (µg/spot): 0.15 (for C3G); 0.11 (for C3R) Intraday precision: 1.32% (for C3G); 3.62% (for C3R) Interday precision: 2.67% (for C3G); 3.04% (for C3R) Recovery: 98.09% and RSD = 3.08% (for C3G); 98.66% and RSD = 2.93% (for C3R)	[136]
Solanum xanthocarpum Schrad. & Wendl. Solasonine (SN) Solamargine (SM) Khasianine (K) Solasodine (SD) Diosgenin (D)	HPTLC–densitometry λ = 640 nm for SN, 440 nm for K and SM; 610 nm for SD, and 430 nm for D Silica gel 60 GF254 n-propanol–ethyl acetate –10% glacial acetic acid in water (4:8:3, v/v) for SN, SM, K toluene–ethyl acetate– diethylamine (6:2:0.3, v/v) for SD, D	RF = 0.15 ± 0.01, 0.22 ± 0.02, and 0.31 ± 0.02 for SN, SM,K, respectively; and 0.38 ± 0.04 and 0.50 ± 0.03 for SD and D Linearity (µg/spot): 0.2–1.0 (for SN, SM, K, SD, D) LOD (µg/spot): 0.031 (for SN), 0.009 (for SM), 0.033 (for K), 0.045 (for SD), 0.053 (for D) LOQ (µg/spot): 0.094 (for SN), 0.029 (for SM), 0.10(for K), 0.136 (for SD), 0.16 (for D) Intraday precision: 2.08–2.87% (for SN, SM, K, SD), 3.25 (for D) Interday precision: 1.58% (for SN); 2.44–2.95 (for SM, K, SD), 3.14 (for D) Recovery (%): 98.41, 98.58, 98.82, 100.34, 99.89 (for SN, SM, K, SD, D, respectively)	[137]
Hypericum species Hyperforin (HyF) Hypericin (HyP) Hyperoside (HyS)	HPTLC–densitometry λ = 366 nm Silica gel 60F254 n-hexane–ethyl acetate (8:2, v/v) (for HyF) toluene–chloroform–ethyl acetate–formic acid (8:5:35:0.6, v/v) (for HyP) ethyl acetate–formic acid–acetic acid -water (15:2:2:1, v/v) (for HyS)	Linearity (ng/band): 400–1400 (for HyF) 20–100 (for HyP) 10–100 (for HyS) LOD (ng/band): 120 (for HyF); 6 (for HyP); 3 (for HyS) LOQ (ng/band): 400 (for HyF); 20 (for HyP); 10 (for HyS) Intraday precision: <2% (for HyF, HyP, HyS) Interday precision: <2% (for HyF, HyS), <3% (for HyP) Accuracy (Recovery): RSD (%) 1.35–1.93 (for HyF), 1.14–1.61 (for HyP), 0.76–1.81 (for HyS)	[138]
Alisma orientale (Sam.) Alisol B 23-acetate (ABA) Alisol A (AA) Alisol B (AB) Alisol C 23-acetate (ACA)	HPTLC–densitometry λ = 254 nm (ACA) and 208 nm (ABA, AA, AB) Silica gel 60F254 cyclohexane–ethyl acetate (1:1, v/v)	Linearity (µg/zone): 0.125–2.0 (for ABA); 0.0834–2.0 (for AA, AB, ACA) Intraday precision: <1% (for ABA, AA, AB, ACA) Interday precision: <1% (for ABA, AA, AB, ACA) Repeability: RSD < 1% (for ABA, AB); <3% (for AA, ACA) Stability: RSD < 1% (for ABA, AB); <3% (for AA, ACA) Accuracy (Recovery): RSD (%) 3.27 (for ABA), 4.05 (for ACA), 2.07 (for AB), 2.78 (for AA)	[126]
Millettia pinnata (L.) Pierre (stem, bark) Karanjin (KR)	HPTLC–densitometry λ = 260 nm HPTLC–MS/MS for identification of isolated compound Silica gel 60F254 toluene–ethyl acetate–formic acid (7:3:0.3, v/v)	Linearity range (ng/band): 200–1200 LOD (ng/band): 21.5 LOQ (ng/band): 65.3 Repeatability (%SD): 1.88 Intraday precision (%RSD): 1.88–1.95 Interday precision (%RSD): 1.87–1.88 % Recovery: 94–104 Robustness (n = 6) (%RSD): <4%	[135]
Gum samples of Sterculia urens Roxb. Glucuronic acid (GlcUA)	HPTLC–densitometry λ = 580 nm Silica gel 60F254 1-propanol–water (7:3, v/v)	RF = 0.43 Linearity range (ng/band): 300–700 LOD (ng/band): 201.54 LOQ (ng/band): 610.74 Intraday precision (%RSD): 1.77 Interday precision (%RSD): 1.27 Accuracy: % Recovery 101.56; %RSD = 1.00 Specificity: Specific	[123]
Ziziphus mauritiana Lam. and Ziziphus nummularia (Burm.f.) Wight & Arn. Betulinic acid (BAC)	HPTLC–densitometry λ = 580 nm Silica gel 60F254 petroleum ether–ethyl acetate– toluene (7:2:1, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	Linearity (µg/spot): 2–10 LOD (µg/spot): 0.379 LOQ (µg/spot): 1.149 Intraday precision: 0.85% Interday precision: 1.22% Accuracy: 99.54%	[139]
fruits, leaves, root bark and stem bark of Dillenia indica Linn Betulinic acid (BAC) β-Sitosterol (BS) Lupeol (LU)	TLC–densitometry λ = 525 nm Silica gel 60F254 toluene–methanol–chloroform (8:1:1, v/v)	RF = 0.38 ± 0.01 for BAC, 0.54 ± 0.01 for BS and 0.65 ± 0.02 for LU Linearity (ng/band): 2000–6000; 200–1000; and 200–600 for BAC, BS, LU, respectively LOD (ng/band): 2.98, 95.36, 118.51 for BAC, BS, LU, respectively LOQ (ng/band): 9.02, 288.97, 359.12 for BAC, BS, LU, respectively Intraday precision (%RSD): 1.11, 1.73, 1.40 for BAC, BS, LU, respectively Interday precision (%RSD): 0.87, 1.81, 1.47 for BAC, BS, LU, respectively Accuracy (% Recovery) 99.19, 99.69, 100.95% for BAC, BS, LU, respectively Robustness (%RSD): <2	[99]
Flowers, fruits, root, stem bark and leaves of Cassia fistula L. β-Sitosterol (BS) Lupeol (LU)	HPTLC–densitometry λ = 525 nm Silica gel 60F254 toluene–methanol–chloroform (8:1:1, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.25 ± 0.01 for BS and 0.37 ± 0.01 for LU Linearity (ng/band): 40–120 for BS, LU LOD (ng/band): 13.86, 13.01 for BS, LU, respectively LOQ (ng/band): 41.99, 39.42 for BS, LU, respectively Intraday precision (%RSD): 1.33 for RT, 1.82–1.97 for Q Interday precision (%RSD): 1.02 for RT, 0.68 for Q Accuracy (% Recovery) 99.81% for RT, 100.97% for Q Robustness (%RSD): <2	[100]
Different plant parts of Uraria picta (Jacq.) Desv. β-Sitosterol (BS) Lupeol (LU)	HPTLC–densitometry λ = 525 nm Silica gel 60F254 toluene–methanol–chloroform (8:1:1, v/v) post-chromatographic derivatization using anisaldehyde-sulphuric reagent	RF = 0.53 ± 0.01 and 0.63 ± 0.01 (for BS and LU, respectively) Linearity range (ng/band): 200–600 for BS and LU LOD (ng/band): 129.455 for BS and 88.687 for LU LOQ (ng/band): 392.287 for BS and 268.749 for LU Intraday precision (%RSD): 1.43 for BS and 1.21 for LU Interday precision (%RSD): 1.27 for BS and 0.92 for LU Accuracy: % Recovery 99.86 for BS and 101.07 for LU %RSD 1.97 for BS and 0.64 for LU Specificity: Specific	[101]
Leaf extracts of Bauhinia vahlii Fern.-Vill. β-Sitosterol (BS) Lupeol (LU)	HPTLC–densitometry λ = 514 nm toluene–ethyl acetate–formic acid (8:2:0.2, v/v post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent for detection of BS	RF = 0.53 and 0.68 for BS and LU, respectively Linearity range (ng/band): 200–1400 for LU and 600–1300 for BS LOD (ng/band): 0.92 for LU and 1.69 for BS LOQ (ng/band): 3.07 for LU and 5.63 for BS Intraday precision (%RSD): <2 Interday precision (%RSD): <2 Accuracy (% Recovery) 106.41% for LU and 109.52% for BS %RSD of recovery: 1.038 for LU and 1.168 for BS Specificity: Specific	[102]
Tuber extract of Amorphophallus paeoniifolius (Dennst.) Nicolson Resveratrol (RV) β-Sitosterol (BS)	TLC–densitometry λ = 305 nm for RV (before derivatization) and 662 nm for BS (after derivatization) Silica gel G60 F254 toluene–ethyl acetate (7:3, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent for detection of BS	RF = 0.24 and 0.60 for RV and BS, respectively Linearity range (ng/band): 100–1000 LOD (ng/band): 10.6 for RV and 11.8 for BS LOQ (ng/band): 32.2 for RV and 35.9 for BS Intraday precision (%RSD): <2 Interday precision (%RSD): <2 Accuracy (% Recovery) 96.99–98.32% for RV and 98.31–99.27% for BS Repeatability (%RSD): 0.15 for RV and 1.01 for BS Specificity: Specific Robustness: Robust	[103]
Phyllanthus niruri L. Phyllanthin (PL)	HPTLC–densitometry λ = 279 nm Silica gel 60F254 toluene–ethyl acetate–formic acid (15:10.5:1.5, v/v)	RF = 0.67 Linearity (µg/band): 2.36–11.8 LOD (µg/band): 0.532 LOQ (µg/band): 1.612 Intraday precision (%RSD) = 8.87–9.43 Interday precision (%RSD) = 6.94 Specificity: Specific	[140]
Eight rhizomes from plants of the Zingiberaceae family Curcumin (C)	HPTLC–densitometry λ = 422 nm Silica gel 60F254 chloroform–methanol (40:1, v/v)	RF = 0.38 Linearity (ng/band): 200–1400 LOD (ng/band): 199.35 LOQ (ng/band): 604.08 Intraday precision (%RSD) = 2.94–5.03 Interday precision (%RSD) = 6.71 Accuracy (% Recovery): 91.62% and 102.42% Specificity: Specific	[124]
Leaves of Clerodendrum philippinum Schauer Hispidulin (H)	HPTLC–densitometry λ = 267 nm Silica gel 60F254 chloroform–methanol–formic acid (9:1:0.1, v/v) post-chromatographic derivatization using sulfuric acid–methanol reagent (5%)	RF = 0.53 Linearity (ng/spot): 100–500 LOD (ng/spot): 17 LOQ (ng/spot): 50 Intraday precision (%RSD): <2 Interday precision (%RSD) = <2 Accuracy (% Recovery): 97.73 Robustness: Robust	[133]
Leaf of Murraya koenigii L. Mahanimbine (MB)	HPTLC–densitometry λ = 285 nm Silica gel 60F254 hexane–ethyl acetate (7:3, v/v)	RF = 0.60 Linearity (µg/mL): 100–400 LOD (ng/spot): 45.50 LOQ (ng/spot): 77.92 Reproducibility: Reproducible	[130]
Citrus aurantium peel. Neohesperidin (NP)	TLC–densitometry λ = 254 nm Silica gel 60F254 ethyl acetate–methanol–water– formic acid (7.1:1.4:1:0.5, v/v)	RF = 0.54 ± 0.02 Linearity (ng/spot): 1000–3000 LOD (ng/spot): 290.05 LOQ (ng/spot): 878.96 Intraday precision (%RSD): <2 Interday precision (%RSD) = <2 Accuracy (% Recovery): 99.6–101.81, Robustness: %RSD < 2 Robust	[134]
Different parts of Capparis zeylanica Linn. Rutin (RT)	HPTLC–densitometry λ = 264 nm Silica gel 60F254 ethyl acetate-glacial acetic acid- formic acid–water (10:1.1:1.1:2.6, v/v)	RF = 0.418 ± 0.004 Linearity range (ng/spot): 400–1400 LOD (ng/spot): 14.10 LOQ (ng/spot): 42.73 Intraday precision (%RSD): <1 Interday precision (%RSD): <2 % Recovery 97.73–98.12 %RSD ≤ 0.01 Specificity: Specific	[116]
Herbal plants, including Ocimum basilicum L. Rutin (RT) Quercetin (Q)	HPTLC–densitometry λ = 254 nm Silica gel 60F254 toluene–ethyl acetate–methanol–formic acid (6:4:3:1, v/v)	RF = 0.25 ± 2.01 for RT and 0.80 ± 0.64 for Q Linearity (ng/band): 300–1300 for RT, Q LOD (ng/spot): 46.52, 81.79 for RT, Q, respectively LOQ (ng/spot): 140.96, 247.84 for RT, Q, respectively Intraday precision (%RSD): 1.54–1.79 for RT, 1.24–1.97 for Q Interday precision (%RSD): 1.82–2.41 for RT, 1.96–2.17 for Q Accuracy (% Recovery) 22.84–25.19% for RT, 54.00–55.29% for Q Robustness (%RSD): <2	[106]
Leaf of Annona reticulata L. Galic acid (GA) Quercetin (Q)	HPTLC–densitometry λ = 254 nm Silica gel GF254 toluene–ethyl acetate–formic acid (9:10:1.6, v/v)	Linearity (ng/spot): 200–1000 for Q and 200–1200 for GA LOD (ng/spot): 21.31 (for Q); 14.86 (for GA) LOQ (ng/spot): 64.57 (for Q); 55.04 (for GA) Intraday precision: <1% (for GA and Q) Interday precision: <2% (for GA and Q) Robustness (%, RSD): <1 (for AA, Q, C) Repeatability of measurement (%RSD): <1 (for GA and Q) Repeatability of application (%RSD): <1 (for GA and Q) Accuracy (%): 98.02–99.09 for Q and 99.28–100.26 (for GA) Specificity: Specific	[107]
Cyperus rotundus L. Quercetin (Q)	HPTLC–densitometry λ = 257 nm toluene–ethyl acetate–formic acid (3:4:2.5, v/v)	RF = 0.80 Linearity (ng/band): 100–700 LOD (ng/band): 30.08 LOQ (ng/band): 91.17 Instrument precision (n = 5): RSD = 0.94% Repeatability (n = 5): RSD = 1.05% Recovery 98–99% Specific—Q separated from rutin and catechin	[108]
Leaf extract of Manilkara hexandra Dubard Myricetin (M) Quercetin (Q)	HPTLC–densitometry λ = 254 nm Silica gel 60F254 toluene–ethyl acetate–formic acid (6:6:2.4, v/v)	RF = 0.6 for M, 0.7 for Q Linearity (µg/band): 0.5–3, 0.4–1.4 for M and Q respectively LOD (µg/band): 0.13, 0.072 for M and Q, respectively LOQ (µg/band): 0.40, 0.21 for M and Q, respectively Intraday precision (%RSD): 0.21–0.69, 0.29–0.78 for M and Q, respectively Interday precision (%RSD): 0.50–1.66, 0.58–1.44 for M and Q, respectively Accuracy (% Recovery) 99.85–100.12, 99.23–100.83 for M and Q, respectively Robustness (%RSD): <2	[109]
Desmodium oojeinensis (Roxb.) Hochr. bark and roots Betulin (BT) Stigmasterol (ST) Lupeol (LU)	TLC–densitometry λ = 520 nm Silica gel 60F254 hexane–ethyl acetate (8.5:1.5, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.42 ± 0.01, 0.27 ± 0.01, and 0.19 ± 0.01 for LU, ST and BT, respectively Linearity (ng/band): 200–600 for LU, ST and BT LOD (ng/band): 12.75, 18.02, 13.35 for LU, ST, BT, respectively LOQ (ng/band): 38.64, 54,59, 40.46 for LU, ST, BT, respectively Intraday precision (%RSD): <2 Interday precision (%RSD): <2 Accuracy (% Recovery): 97.43–97.69 for LU, 97.02–97.89 for ST, 97.68–98.18 for BT Robustness (%RSD): <2	[105]
Leaves, flowers, stems, seeds, and roots of Hygrophila schulli (Schumach.) Heine Stigmasterol (ST)	HPTLC–densitometry λ = 520 nm Silica gel 60F254 toluene—methanol (9:1, v/v) post-chromatographic derivatization using anisaldehyde—sulphuric acid reagent	RF = 0.47 ± 0.02 Linearity (ng/band): 100–500 LOD (ng/band): 6.87 LOQ (ng/band): 20.82 Precision (%RSD): <2 Accuracy (% Recovery): 98.86–99.22, %RSD < 1 Robustness: Robust	[132]
Parkia roxburghii (DC.) Merr. seed Catechin (CT)	HPTLC–densitometry λ = 302 nm Silica gel 60F254 ethyl acetate–acetic acid–formic acid–water (10:1:0.75:1, v/v)	RF = 0.61 Range of calibration curve (µg): 2–10 LOD (ng/spot): 12.32 LOQ (ng/spot): 37.23 Intraday precision (%RSD): <1 Interday precision (%RSD): <1 Accuracy (% Recovery): 99.54 Robustness: robust Specificity: specific	[128]
Gynura cusimbua S.Moore leaves Chlorogenic acid (CGA)	HPTLC–densitometry λ = 366 nm Silica gel 60F254 ethyl acetate–formic acid–acetic acid–water (100:11:11:2.6, v/v)	RF = 0.43 ± 0.01 Range of calibration curve (ng/spot): 50–250 LOD (ng/spot): 14.36 LOQ (ng/spot): 43.12 Intraday precision (%RSD): 0.63 Interday precision (%RSD): 1.78 Accuracy (% Recovery): 97.79–98.30 Specificity: specific	[127]
Different parts of Carica papaya L. Syringic acid (SA) Gallic acid (GA) p-Coumarin (PC) Caffeic acid (CFA)	TLC–densitometry λ = 302 nm for SA, 256 nm for PC, 200 nm for GA, 296 nm for CFA Silica gel 60F254 toluene–ethyl acetate–glacial acetic acid (8.5:1.5:0.1, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.51 ± 0.002, 0.62 ± 0.001, 0.29 ± 0.05, and 0.38 ± 0.01 for SA, PC, GA, and CFA, respectively Linearity (ng): 100–600 for SA, GA, PC, CFA LOD (ng): 60, 30, 40, 40 for SA, PC, GA, and CFA, respectively LOQ (µg): 200, 100, 100, 100 for SA, PC, GA, and CFA, respectively Intraday precision (%RSD): <1% Interday precision (%RSD): <2% Accuracy (% Recovery): 97.23–98.35, 97.72–98.36, 98.12–99.71, 98.26–99.13 for SA, PC, GA, and CFA, respectively Specificity: specific	[125]
Caesalpinia bonduc leaf extract β-Caesalpin (βCLP) α-Caesalpin (αCLP)	TLC–densitometry TLC-MS λ = 580 nm Silica gel 60F254 n-hexane–ethyl acetate (6:4, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.64 (for βCLP) 0.78 (for αCLP) Linearity (ng/band): 200–1200 for βCLP and αCLP LOD (µg/band): 43.34 and 22.74 for βCLP and αCLP, respectively LOQ (µg/spot): 131.36 and 68.91 for βCLP and αCLP, respectively Intraday precision: 1.52–1.78 for βCLP and 0.52–1.30 for αCLP Interday precision: 1.59–1.75 for βCLP and 0.89–1.65 for αCLP Accuracy: 95.16–99.75% for βCLP and 97.49–99.86% for αCLP Injection repeatability: 1.62 and 1.75 for βCLP and αCLP, respectively Scanning repeatability: 0.47 and 0.58 for βCLP and αCLP, respectively Robustness: Robust Specificity: Spectific	[87]
Herbal formulations
Majun Nisyan (α + β) Boswelics acids (BA) β-Asarone (A) Isoeugenol (IS) 6-Gingerol (G) Piperine (P)	HPTLC–densitometry Silica gel 60F254 toluene–ethyl acetate -chloroform–acetic acid (8:2:5:0.1, v/v)	RF = 0.102 ± 0.01 (for BA 0.982 ± 0.04 (for A), 0.850 ± 0.03 (for IS), 0.698 ± 0.03 (for G) 0.355 ± 0.01(for P) Linearity (µg/spot): 1–12 (for BA, A, IS, G, P) LOD (µg/spot): 1.060 (for BA); 1.405. (for A); 1.973 (for IS); 1.691 (for G); 2.090 (for P) LOQ (µg/spot): 3.214 (for BA); 4.258. (for A); 5.979 (for IS); 5.125 (for G); 6.334 (for P) Intraday precision: <2% (for BA, A, IS, G, P) Interday precision: <2% (for BA, A, IS, G, P) Accuracy: 86.42–105.75%	[111]
Polyherbal formulation Galic acid (GA) Eugenol (E)	HPTLC–densitometry Silica gel 60F254 Isopropyl alcohol–n-hexane–ethyl Acetate–glacial acetic acid (10:6:6:0.1, v/v)	RF = 0.608 ± 0.041 (for GA); 0.752 ± 0.035 (for E) Linearity (ng/mL): 1–10 (for GA, E) LOD (ng/mL): 7.85 (for GA); 8.78 (for E) LOQ ((ng/mL): 23.80 (for GA); 26.60 (for E) Intraday precision: <1% (for GA); <2% (for E) Interday precision: <2% (for GA); <1% (for E) Accuracy: 98.05–99.41%	[112]
Polyherbal formulations containing Terminalia species Galic acid (GA) Quercetin (Q)	HPTLC–densitometry λ = 271 nm (for GA) and 366 nm (for Q) Silica gel 60F254 toluene-isopropyl alcohol-acetic acid (7:2.5:0.5, v/v) post-chromatographic derivatization using anisaldehyde reagent for phenolic compounds; 2, 2-diphenyl-1 picrylhydrazyl reagent (DPPH) for antioxidant activity; vanilin reagent for terpenoids and phenolic compounds	Linearity (μg/mL): 5–10 (for GA); 1–6 (for Q) LOD (ppm): 800 (for GA); 5 (for Q) LOQ (ppm): 2400 (for GA); 8 (for Q) Reproducibility, RSD = 0.44–9.71%	[110]
Kapacurak Kuṭinīr Cūraṇam Andrographolide (AG) Columbin (CL) Gallic acid (GA) p-Coumaric acid (CA) Piperine (P) Oleanolic acid (OA)	HPTLC–densitometry λ = 254 nm for GA, CA, P (before derivatization) and 520 nm for AG, CL and OA (after derivatization) Silica gel 60F254 toluene: ethyl acetate: formic acid (7:3:0.5, v/v) post-chromatographic derivatization using vanillin–sulphuric acid reagent for AG, CL and OA	RF = 0.19 (for AG), 0.23 (GA), 0.28 (CL), 0.57 (CA), 0.64 (PP) and 0.66 (OA) Linearity (µg/band): 1–5 (for each compounds) LOD (ng/band): 0.0069, 0.0044, 0.0063, 0.0042, 0.0022, 0.00037 (for AG, GA, CL, CA, PP, OA, respectively) LOQ (ng/band): 0.0209, 0.0133, 0.0189, 0.0127, 0.0067, 0.00113 (for AG, GA, CL, CA, PP, OA, respectively) Intraday precision (%RSD): <3 Interday precision (%RSD): <3 Accuracy (% Recovery): 97.73 Standard stability (% RSD) <5	[113]
Herbal formulations Quercetin (Q) Curcumin (C) Ascorbic acid (ASA)	HPTLC–densitometry λ = 265 nm Silica gel 60F254 chloroform–ethyl acetate–formic acid (6:6:2.5, v/v)	Linearity (ng/spot): 500–1000 (for AA, Q, C); LOD (ng/spot): 12 (for AA); 6 (for Q); 4 (for C) LOQ (ng/spot): 36 (for AA); 18 (for Q); 13 (for C) Intraday precision: <2% (for AA); <1% (for Q, C) Interday precision: <2% (for AA); <1% (for Q, C) Robustness (%, RSD): <1 (for AA, Q, C) Repeatability (%RSD): <1 (for AA, Q, C)	[114]
Sitopaladi churna— Ayurvedic multi-herbal preparation Piperine (P) Cinnamaldehyde (CD) 1,8-Cineole (CN)	HPTLC–densitometry λ = 307 nm for PP, CD (before derivatization) and 599 for CN (after derivatization) Silica gel 60F254 toluene–methanol (9:1, v/v) post-chromatographic derivatization using vanillin– sulphuric acid reagent	RF = 0.22 ± 0.01 for PP, 0.54 ± 0.01 for CD, and 0.65 ± 0.01 for CN Linearity (ng/band): 100–500 ng/spot for PP, CD, and 600–3000 for CN LOD (ng/spot): 18, 24, 27 for PP, CD, CN, respectively LOQ (ng/spot): 54, 73, 483 for PP, CD, CN, respectively Intraday precision (%RSD): ≤2 for PP, CD and 2.4–4.1 for CN Interday precision (%RSD): <1 for PP, ≤2 for CD and 2.4–3.5 for CN Accuracy (% Recovery) 99.1–101.6 for PP, 98.8–100.7 for CD, 98.3–102.7 for CN Robustness (%RSD): <2 for PP, CD, and <5 for CN	[115]
Ayurvedic formulations Alizarin (AL)	TLC–densitometry λ = 259 nm Silica gel 60F254 toluene–ethyl acetate–formic acid (9:1.5:1, v/v)	RF = 0.50 ± 0.02 Linearity (ng/spot): 100–1000 LOD (ng/spot): 30.45 LOQ (ng/band): 92.28 Intraday precision (%RSD): <1 Interday precision (%RSD): <1 Accuracy (% Recovery) 96.75–100.43 Specificity: specific	[129]
Herbal hepatoprotective formulation Andrographolide (AD)	HPTLC–densitometry λ = 254 nm Silica gel 60F254 dichloromethane–toluene–ethyl acetate–formic acid (6:4:1:0.5, v/v)	RF = 0.69 Linearity (ng/spot): 500–3000 LOD (ng/spot): 31.50 LOQ (ng/spot): 95.48 Intraday precision (%RSD): <3 Interday precision (%RSD): <2 Accuracy (% Recovery) 99.74–99.84 Robustness (%RSD): <1 Ruggedness (%RSD): <1	[117]
Marketed herbal formulations Mahanimbine (MB) Koenimbine (KB)	HPTLC–densitometry λ = 285 nm for MB and 291 nm for KB Silica gel 60F254 hexane–ethyl acetate (7:3, v/v)	RF =0.48 and 0.60 for KB and MB, respectively Linearity range (ng/spot): 100–400 for MB and 50–450 for KB LOD (ng/spot): 32.81 for MB and 18.44 for KB LOQ (ng/spot): 72.81 for MB and 31.57 for KB Intraday precision (%RSD): <3 Interday precision (%RSD): <3 Accuracy (% Recovery) 95.1–98.4 for MB Reproducibility: Reproducible	[118]
Mansyadi Kwatha Atropine (AT) Rutin (R) Vanillin (V)	TLC–densitometry λ = 206 nm Silica gel 60F254 tetrahydrofuran–toluene– methanol–formic acid (5:3.5:2:0.5, v/v)	RF = 0.090 ± 0.0039, 0.290 ± 0.0099 and 0.679 ± 0.0056 for AT, R, and V, respectively Linearity (ng/band): 500–50000 for AT, and 500–5000 for R, and V LOD (ng/band): 1427.070, 119.559, 109.974, for AT, R, and V, respectively LOQ (µg/band): 4324.454, 362.300, 333.254 for AT, R, and V, respectively Intraday precision (%RSD): <2% Interday precision (%RSD): <2% Accuracy (% Recovery): 65.26–103.00, 98.27–104.70, 95.40–104.28 for AT, R, and V, respectively Robustness (%RSD): <2 Specificity: specific	[119]
Other samples containing the biological active substances occurring in plant
Rasam/a South Indian spice soup Piperine (P) Capsaicin (CP)	TLC–densitometry λ = 527 nm Silica gel 60F254 toluene–ethyl acetate (7:3, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.51 for PP and 0.40 for CP Linearity (µg/spot): 1–5 LOD (ng): 23.57 for PP and 7.67 for CP LOQ (ng): 76.84 for PP and 25 for CP Intraday precision (%RSD): 2% Interday precision (%RSD): <2% Accuracy (% Recovery): 97.78–99.18 for PP and 96.15–102.13 for CP	[121]
Rasam, a polyherbal soup Curcumin (CC) Piperine (P) Capsaicin (CP)	TLC–densitometry λ = 254 nm Silica gel 60F254 toluene–ethyl acetate (7:3, v/v) post-chromatographic derivatization using anisaldehyde–sulphuric acid reagent	RF = 0.26, 0.40 and 0.47 for CC, PP, and CP, respectively Linearity (µg/spot): 2–7 LOD (µg): 3.98, 3.75, and 3.13 for CC, PP, and CP, respectively LOQ (µg): 12.05, 11.36, and 9.49 for CC, PP, and CP, respectively Instrumental precision (%RSD): 2 Intraday precision (%RSD): 1% Interday precision (%RSD): <2% Accuracy (% Recovery): 99.96–101.48 for CC, 99.93–101.48 for PP and 92.25–100.62 for CP Specificity: specific	[120]
Coffee bean infusions/ green, light and dark roasted coffee bean infusions Trigonelline (TG) Caffeine (CF) Chlorogenic acid (CGA)	TLC–densitometry λ = 270, 275 and 330 nm for TG, CF, and CGA, respectively Silica gel 60F254 with concentrating zone chloroform–ethyl acetate– methanol–formic acid, (10:6:3:1, v/v)	RF = 0.06, 0.18, 0.73 for TG, CGA, and CF, respectively Linearity (µg/band): 1.00–3.00 for TG, 1.50–5.00 for CGA and 1.00–3.00 for CF LOD (µg/band): 0.28, 0,27, 0,04 for TG, CGA, and CF, respectively LOQ (µg/band): 0.84, 0.82, 0.12 for TG, CGA, and CF, respectively Intraday precision (%CV): <3% Interday precision (%CV): <3% Accuracy (% Recovery): Coffee from an espresso machine: 95.4–101.8 for TG, 96.3–102.4 for CLA, 98.1–102.1 for CF Brewed coffee: 96.2–103.9 for TG, 98.7–104.1 for CLA, and 97.2–101.7 for CF Accuracy (% CV) Coffee from an espresso machine: 2.12, 2.33, and 1.75 for TG, CGA, and CF, respectively Brewed coffee: 2.54, 2.19, and 1.78 for TG, CGA, and CF, respectively Specificity: specific	[98]
Counterfeit herbal antidiabetic products Metformin HCl (MET) Pioglitazone HCl (PIO) Glipizide (GLP) Glimepiride (GLM)	HPTLC–densitometry and HPTLC-MS λ = 232 nm Silica gel 60F254 cyclohexane–dichloromethane– 1-propanol–saturated solution of ammonium acetate in acetic acid (7:5:2:2, v/v)	RF = 0.255, 0.461, 0.551, 0.791 (for MET, PIO, GLP, GLM, respectively) Linearity (ng/spot): 200–1200 (for MET, PIO, GLP, GLM) LOD (ng/spot): 186.39 (for MET), 191.66 (for PIO), 153.47 (for GLP), 222.34 (for GLM) LOQ (ng/spot): 564.84 (for MET), 580.77 (for PIO), 465.07 (for GLP), 673.77 (for GLM) Intraday precision: <9% Interday precision: <9% Accuracy: 97.40–105.43, 98.19–105.41, 100.25–103.13, and 98.82–104.38% (for MET, PIO, GLP, GLM, respectively) Robustness (%): <16%	[77]
Pesticide residues in thyme and guava leaves Imidacloprid (IMD) Deltamethrin (DLM) Dibutyl phthalate (internal standard—IS)	HPTLC–densitometry λ = 270.0 nm for IMD and 230.0 nm for DLM Silica gel 60F254 impregnated in chitosan nanoparticles (ChTNPs) 0.5% isopropyl alcohol for IMD and IS n–hexane–toluene–ethyl acetate (7:3:1, v/v) for DLM and IS	RF = 0.51 for of IMD and 0.89 for IS RF = 0.80 for of DLM and 0.61 for IS Linearity (µg/spot): 0.2–2.2 for IMD and 0.2–2.4 for DLM Accuracy [mean% ± SD]: 100.49 ±1.62 for IMD and 100.57 ± 0.39 DLM Intraday precision (%RSD): 1.92 for IMD, 1.39 for DLM Interday precision (%RSD): 1.92 for IMD, 1.92 for DLM LOD (µg/spot): 0.002 for IMD and 0.00116 for DLM LOQ (µg/spot): 0.0054 for IMD and 0.0035 for DLM Robustness (%RSD): <3% for IMD and DLM	[122]

3. Thin Layer Chromatography with Effect-Oriented Analysis

Properly developed TLC profiles enable the combination of TLC with the detection of the biological properties of separated substances. This method is known as TLC effect-oriented analysis, TLC bioprofiling, or TLC–bioautography. First, the substances in the sample (e.g., a plant extract) are separated on a TLC plate (often an HPTLC plate) using an appropriate mobile phase. After evaporating the mobile phase residue, the plate undergoes biological testing. Various tests can be performed on the plate, including the detection of substances with antimicrobial or antioxidant activity, enzyme inhibitor substances, hormone-disrupting substances, and genotoxic and cytostatic compounds.

Effect-based assays using TLC are extremely valuable because they allow for the detection of specific bands demonstrating biological activity. They therefore offer an advantage over conventional analyses in cuvettes or microtiter plates, which allow for the assessment of the combined activity of all sample components. Furthermore, TLC–bioautography allows for the study of the biological activity of a large number of compounds present in a sample and the simultaneous analysis of multiple samples, making it faster and less expensive.

3.1. Detection of Antimicrobial Substances

A dramatic increase in antibiotic resistance is currently being observed, which is leading to a decline in the effectiveness of antibiotics for treating infections [141]. This phenomenon poses a significant challenge to modern medicine. Therefore, natural substances with antimicrobial activity, including plant materials, are being sought [142]. Because of the vast number of untested plants and the multitude of active ingredients they contain, TLC–bioautography is an optimal method for screening antimicrobial properties. There are three ways to test the antimicrobial activity of substances separated on a TLC plate: direct bioautography, contact bioautography, and overlay bioautography [143,144].

For direct bioautography, after separating the components of a plant extract, the TLC plate is sprayed with a medium containing the appropriate microorganism, or immersed in the medium, and incubated under the proper conditions. After the appropriate incubation period, depending on the microorganism used, zones of inhibition of microorganism growth are observed on the plate [143,144].

In contact bioautography, also known as agar diffusion, a TLC plate containing separated extract substances is placed on an agar layer containing the appropriate microorganism. This ensures that the stationary phase adheres to the agar. The separated compounds then diffuse into the agar medium, which may affect the growth of the present microorganism. In the presence of antimicrobial substances, zones of growth inhibition are observed. These zones correspond to the location of the substances on the chromatography plate [143,144].

In overlay bioautography, also known as immersion bioautography, a TLC plate containing the separated components of a plant extract is immersed in a liquid agar medium containing the appropriate microorganism. The agar is then allowed to solidify, and the plate is incubated. If the extract’s compounds inhibit the microorganism’s growth, zones of inhibition are observed [143,144].

Areas of microbial growth inhibition on a TLC plate are typically visualized using tetrazolium salts, such as MTT (3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide), TTC (2,3,5-triphenyltetrazolium chloride), or INT (2-(4-iodophenyl)-3-(4-nitrophenyl)-5-phenyl-2H-tetrazolium chloride). These salts are converted into colored compounds, including formazan, by the influence of the dehydrogenases found in living microorganisms. Antimicrobial substances appear as light-colored spots on a purple, purple-blue, or red-pink background, depending on the tetrazolium salt used and the subjective assessment of color.

An appropriate reader is required for analysis if the microorganism being tested for the activity of compounds contained in a plant extract is the luminescent bacterium Aliivibrio fischeri, and a lack of luminescence is visible in areas of growth inhibition. Aliivibrio fischeri is a bacterium commonly used to study non-specific toxicity, including water toxicity [12,144,145]. However, it is worth noting that in bioautographic studies of plant extracts, the inhibition of growth by an extract component is not interpreted as toxicity, but rather as activity against Aliivibrio fischeri [62,146].

Table 2. Plant materials analyzed by TLC–bioautography for antimicrobial properties.

Matrix/Plant Materials	Microorganism/ Microorganisms	Bioautography Method/Way of Visualization	Refs.
Leaf of Stereospermum kunthianum Cham.	Bacillus subtilis *, Staphylococcus aureus *, Salmonella typhi *, Pseudomonas aeruginosa *, Aspergillus niger **, Candida albicans **	Overlay bioautography/ MTT	[147]
Leaf of Garcinia latissima Miq.	Bacillus subtilis *	Contact bioautography	[148]
Origanum vulgare L.	Bacillus subtilis *, Micrococcus lysodeikticus *, Escherichia coli *	Direct bioautography/ MTT	[33]
Inflorescence of Sphaeranthus indicus L.	Staphylococcus aureus *, Klebsiella pneumonia *	Overlay bioautography/ TTC	[93]
Various hop varieties	Bacillus subtilis *	Direct bioautography/ MTT	[142]
Dioscorea bulbifera L.	Staphylococcus aureus *, methicillin-resistant Staphylococcus aureus *, Staphylococcus epidermidis *, Pseudomonas aeruginosa *	Overlay bioautography/ MTT	[94]
Leaves and stems of Dicerocaryum senecioides (Klotzsch) Byng & Christenh. and fresh fruits of Diospyros mespiliformis Hochst. ex A.DC.	Candida albicans **, Trichophyton rubrum **, Epidermophyton floccosum **	Overlay bioautography/ INT	[54]
15 selected plant species from four genera: Combretum, Pteleopsis, Quisqualis, Terminalia	Staphylococcus aureus *, Bacillus cereus *, Escherichia coli *, Salmonella typhimurium *	Overlay bioautography/ INT	[92]
Leaf of Sempervivum tectorum L.	Bacillus subtilis *, Micrococcus lysodeikticus *, methicillin-resistant Staphylococcus aureus *, Staphylococcus aureus *, Escherichia coli *, Klebsiella pneumoniae *	Direct bioautography/ MTT	[149]
Aerial parts of Salvia aegyptiaca L., S. verbenaca L. and the leaves of S. officinalis L.	Aliivibrio fischeri *, Bacillus subtilis *, Rhodococcus fascians *, Bipolaris sorokiniana **, Fusarium avenaceum **	Direct bioautography/MTT, luminescence (for Aliivibrio fischeri)	[62]
Ligusticum chuanxiong Hort. rhizome essential oil	Candida albicans **	Overlay bioautography/ MTT	[150]
Artemisia argyi H.Lév. & Vaniot essential oil	Staphylococcus aureus *, Escherichia coli *	Direct bioautography/ MTT	[151]
Thymus vulgaris L. essential oil	Haemophilus spp. * (Haemophilus influenzae and H. parainfluenzae), Pseudomonas aeruginosa *	Direct bioautography/ MTT	[152]
Nineteen medicinal plants purchased as herbal teas in Belgrade	Escherichia coli *, Staphylococcus aureus *	Direct bioautography/ MTT	[153]
Fifteen golden root (Rhodiola rosea L.) products	Bacillus subtilis * Aliivibrio fischeri *	Direct bioautography/ MTT, luminescence (for Aliivibrio fischeri)	[146]
Akebia quinata D. leaves or fruits, and Clitoria ternatea L. flowers	Bacillus subtilis * Aliivibrio fischeri *	Direct bioautography/ MTT, luminescence (for Aliivibrio fischeri)	[154]
Schisandra rubriflora (Franch.) Rehd. et Wils fruit and leaf	Bacillus subtilis *	Direct bioautography/ MTT	[155]
Leaves of Schisandra chinensis (Turcz.) Baill., S. rubriflora Rehder & E.H.Wilson, S. sphenanthera Rehder & E.H.Wilson, S. henryi C.B.Clarke and Kadsura japonica (L.) Dunal	Bacillus subtilis *	Direct bioautography/ MTT	[156]
African leafy vegetables	Bacillus subtilis * Aliivibrio fischeri *	Direct bioautography/ MTT, luminescence (for Aliivibrio fischeri)	[157]
Leaves of Ficus carica L.	Enterococcus faecalis *	Direct bioautography/ MTT	[96]
Different peppermint products	Bacillus subtilis * Aliivibrio fischeri *	Direct bioautography/ MTT, luminescence (for Aliivibrio fischeri)	[158]

* bacterial species; ** fungal species. MTT: 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide, TTC: 2,3,5-triphenyltetrazolium chloride, INT: 2-(4-iodophenyl)-3-(4-nitrophenyl)-5-phenyl-2H-tetrazolium chloride.

presents the selected papers describing the antimicrobial activity of substances contained in plant extracts or essential oils whose activity was demonstrated using TLC–bioautography.

As shown in Table 2, TLC–bioautography either direct or overlay bioautography, is most commonly used to test for antibacterial and antifungal activity. The most commonly used microorganism is Bacillus subtilis. MTT is used to visualize active zones. TLC–bioautography is a valuable method for screening substances with antimicrobial activity. However, it is important to note that the activity of substances detected through bioautography must be confirmed using conventional methods, such as in vitro dilution and diffusion [149]. Jankov et al. observed that the substances in Sempervivum tectorum leaf extract exhibited greater activity against E. coli and K. pneumoniae (both Gram-negative bacteria) than against Gram-positive bacteria in bioautography. However, the agar diffusion method revealed that the extracts did not inhibit the growth of Gram-negative bacteria, though they exhibited antibacterial activity against Gram-positive bacteria [149].

A new and unique test analyzes activity against the protozoan Leishmania infantum, as developed by Hilaire et al. [159]. The Leishmania genus includes species that cause leishmaniasis, a tropical parasitic disease. Since drugs for treating leishmaniasis are scarce and expensive, seeking substances active against Leishmania species is important. This test is also unique because it was developed for two parasite life cycles: promastigotes and amastigotes. This is important because parasites have different life requirements depending on their developmental stage. Direct bioautography was used for the promastigote stage and overlay bioautography for the amastigote stage. For this test, the authors used a Leishmania strain that contains a gene that encodes the luciferase enzyme, which produces luminescence. In the absence of activity of the compounds separated on the plate against Leishmania, strong luminescence is observed. In contrast, a decrease in luminescence is visible in the case of activity against Leishmania [159].

3.2. Detection of Substances with Antioxidant Activity

Oxidative stress is primarily caused by the build-up of reactive oxygen species (ROS), including free radicals, in the body. This can result in damage to macromolecules, cells, tissues and organs, and can lead to diseases such as cancer, neurodegenerative conditions, diabetes, atherosclerosis and cataracts. Inhibiting the overproduction of ROS is important for delaying the onset of diseases resulting from oxidative stress. Therefore, it is crucial to search for safe, natural substances with antioxidant properties that could be used to prevent or limit the development of such diseases [63,160]. The most common radicals used to analyze the antioxidant properties of plant extracts are 1,1-diphenyl-2-picrylhydrazyl (DPPH) and the cation radical ABTS, which is formed from 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) in the presence of potassium persulfate.

Bioautography is used to assess antioxidant properties. It involves spraying a TLC plate containing separated substances from plant extracts after the mobile phase has evaporated. Then, an ABTS^●+ or DPPH^● solution is sprayed on the plate, or the plate is immersed in solutions of these radicals for a few seconds. After the appropriate time, the color change resulting from the scavenging of radicals by antioxidants is analyzed. When using a DPPH^● solution, bright zones on a purple background are observed in the presence of antioxidants. When using an ABTS^●+ solution, brightening on a blue-turquoise background is observed. Another method of assessing antioxidant properties is the ferric reducing antioxidant power (FRAP) method, which is based on antioxidants reducing trivalent iron ions (Fe(III)) to divalent iron (Fe(II)). This method evaluates changes in the absorbance of complexes formed with iron ions (Fe²⁺). This method was recently implemented in a planar format [16,156]. Antioxidants appear as blue and green zones on a white background. The reaction mixture used to visualize antioxidant compounds on a TLC plate is ten times more concentrated than that used in conventional spectrophotometric analysis with the FRAP method [156].

Gu et al. [161] developed a bioautography method to detect antioxidants that inhibit lipid peroxidation. In the analysis, linoleic acid is first irradiated with UV radiation at a wavelength of 254 nm. This causes the formation of lipid peroxides, one of which is malondialdehyde. The malondialdehyde then combines with thiobarbituric acid (TBA) to produce a red color with a maximum absorption wavelength of 535 nm. Bioautography of the compounds that inhibit lipid peroxidation is performed by immersing an HPTLC plate in a linoleic acid solution in n-hexane, drying the plate, and exposing it to UV radiation at 254 nm. Next, the plate was immersed in a TBA solution and heated. Compounds that inhibit lipid peroxidation are visible as white spots on a pink background. In a study of lipid peroxidation inhibition using bioautography, the authors conducted the analysis on an extract of Perilla frutescens (L.) Britton fruit. They used the proposed method to analyze the separated compounds contained in the extract [161]. TLC densitometry can be used to analyze bioautograms containing zones that exhibit antioxidant properties. When using DPPH, the plates are scanned at 516 nm; when using ABTS, at 744 nm; and when using FRAP, at 593 nm. However, measuring the area of antioxidant zones and obtaining precise results is sometimes impossible due to difficulties establishing a baseline or background noise [156]. When using a method to detect compounds that inhibit lipid peroxidation, the plates can be scanned at 535 nm [161].

Recently, several review papers have been published that demonstrate the importance and advantages of TLC–bioautography for assessing antioxidant properties, as well as its disadvantages [16,162]. The authors of these papers point out that the DPPH radical is more stable than the ABTS cation radical [16,162]. This may explain why DPPH-based bioautography is most commonly used to screen antioxidant substances.

Table 3. Plant material analyzed by TLC–bioautography for antioxidant properties using DPPH.

Matrix/Plant Materials	Type of Stationary Phase	DPPH Solution Concentration and Solvent Type	Time from Spraying the Plate to Analysis	Refs.
Leaves of Abrus precatorius L.	Silica gel 60F254 TLC	0.2%, methanol	30 min	[163]
Leaves and flowers of Nyctanthes arbor-tristis L.	No information available	0.02%, methanol	overnight	[164]
Heartwood of Pterocarpus marsupium Roxb.	Silica gel 60F254 TLC	5 mM, methanol	No information available	[165]
Neptunia prostrata Baill.	Silica gel 60F254 TLC	2%, unknown solvent	30 min	[166]
Leaves of Morus alba L.	Silica gel 60F254 TLC	5 mM, methanol	No information available	[167]
Origanum vulgare L.	Silica gel 60 HPTLC	0.1%, methanol	30 min	[33]
Leaf buds of two species and two hybrids of the genus Populus	Silica gel 60F254 TLC	0.05%, methanol	30 min	[168]
Aerial parts of Salvia aegyptiaca and Salvia verbenaca and the leaves of Salvia officinalis	Silica gel 60F254 HPTLC	0.02%, methanol	No information available	[62]
Hedera helix L. flower pollen	Silica gel 60F254 HPTLC	0.1%, unknown solvent	30 min	[169]
Inflorescence of Sphaeranthus indicus L.	Silica gel 60F254 TLC	50 µM, methanol	10 min	[93]
Ligusticum chuanxiong S.H.Qiu, Y.Q.Zeng, K.Y.Pan, Y.C.Tang & J.M.Xu essential oil	Silica gel 60F254 HPTLC	0.092 mg in mL methanol	40 min	[150]
Schisandra chinensis (Turcz.) Baill., S. rubriflora Rehder & E.H.Wilson, S. sphenanthera Rehder & E.H.Wilson, S. henryi C.B.Clarke and Kadsura japonica (L.) Dunal	Silica gel 60F254 TLC	0.2%, methanol	immediately after spraying	[156]
Fruits of Terminalia bellirica (Gaertn.) Roxb. and T. chebula Retz.	Silica gel 60F254 TLC	8 mg in 200 mL ethanol	10–15 min	[110]
Herb of Marrubium vulgare L.	Silica gel 60F254 HPTLC	6 × 10−5 M, methanol	10 min	[170]
Nineteen medicinal plants purchased as herbal teas in Belgrade	Silica gel 60F254 HPTLC	0.25%, methanol	30 min	[153]
Flowers, leaves, roots and aerial of Cichorium intybus L.	Silica gel 60F254 HPTLC	0.03%, ethanol	30 min	[171]
The essential oil of Lavandula sp. and Origanum sp.	Silica gel 60F254 TLC	0.2%, unknown solvent	30 min	[172]
Syzygium aromaticum (L.) Merr. & L.M.Perry, Fomitopsis pinicola and Hypholoma fasciculare	Silica gel 60F254 HPTLC	0.1% (m/v), methanol	No information available	[173]
Leaves of Rosmarinus officinalis, Ficus carica, Backhousia citriodora, Salvia officinalis, Salvia apiana, leaves and flowers of Olea europaea L.	Silica gel 60F254 HPTLC	0.2%, methanol	30 min	[63]

presents a selection of studies in which the antioxidant properties of extracts were analyzed using TLC-DPPH bioautography.

Table 3 highlights the solvent used to dissolve DPPH and spray or immerse the plates, the concentration of the DPPH solution, and the time between treating the plate with the DPPH solution and documentation. As shown in Table 3, silica gel-coated plates were used for bioautography in recently published studies, while a DPPH solution prepared in methanol was used for spraying, with a few exceptions where DPPH was dissolved in ethanol. DPPH solutions were used at various concentrations (from 0.02% to 2%), and the time between spraying/immersing the plates in the DPPH solution and analyzing and documenting them also varied. In extreme cases, analysis was conducted immediately after spraying or after several hours. Standardization of the analysis procedure is essential to compare test results between different laboratories. Only then will the method be suitable for testing the quality of plant materials used in the pharmaceutical and food industries.

3.3. Detection of Enzyme-Inhibiting Substances

Legerská et al. [174] published a thorough review of the use of TLC–bioautography for detecting substances that inhibit enzyme activity. The authors discussed the principles of methods developed to detect specific enzymes on TLC plates, including oxidoreductases (e.g., glucose-6-phosphate dehydrogenase, peroxidases, monoamine oxidases, tyrosinases, and xanthine oxidase); hydrolases (e.g., lipase, acetylcholinesterase, butyrylcholinesterase, glycosidases, α-amylase, β-glucuronidase, dipeptidyl peptidase IV, α-chymotrypsin, and arginase); and isomerases (e.g., glucose-6-phosphate isomerase). The authors drew attention to the advantages and disadvantages of the described methods as well as the directions and prospects of their development [174]. Other review papers [16,144,162,175] also discuss TLC–bioautography with the purpose of searching for enzyme inhibitors, including those in plant materials.

This article reviews original papers published since 2022 that use TLC to study the effect of separated components of plant extracts on enzyme activity. The analysis shows that recently, the most frequently searched inhibitors in plant material using the TLC–bioautography method are: α-amylase [33,146,154,165,176,177], α-glucosidase [62,155,156,167,176,177], lipase [62,155,156,178], acetylcholinesterase [62,89,155,156,179,180,181], tyrosinase [62,97,146,154,155,156,182,183,184].

α-Amylase, α-glucosidase, and lipase are digestive enzymes whose inhibition may prevent diabetes and obesity. α-Amylase breaks down starch by hydrolyzing α-(1,4)-D-glycosidic bonds. α-Glucosidase breaks down disaccharides into monosaccharides, and pancreatic lipase releases free fatty acids from triglycerides. Recently, some α-amylase-inhibiting compounds present in plant material have been detected using TLC–bioautography, a method that uses a starch solution as the substrate and an iodine solution for visualization. α-Amylase inhibitors appear as dark zones on a white background. Differences in enzyme detection methods among researchers result from varying incubation times and reagent concentrations. For example, the starch solutions used in analyzed studies range from 0.1% [177] to 2% [146,154]. Different concentrations of reagents are also used when detecting α-glucosidase inhibitors [155,176,177]. Lipase inhibitors have recently been detected primarily using two substrates: 1-naphthyl acetate [62,155,156] or 2-naphthyl myristate [178]. Fast Blue B salt is used for visualization. Lipase-inhibiting compounds appear as bright zones on a purple background.

Acetylcholinesterase (AChE) catalyzes the degradation of, among others, the neurotransmitter acetylcholine. Its high activity is associated with the development of neurodegenerative diseases, including Alzheimer’s disease [174,175]. This is why scientists use TLC–bioautography to search for compounds that would inhibit the activity of this enzyme. This method typically involves reacting 1-naphthyl acetate or 2-naphthyl acetate with the enzyme to form 1-naphthol or 2-naphthol. The 1-naphthol/2-naphthol is visualized using a Fast Blue B salt solution, which produces a purple dye. AChE inhibitors appear on the plate as bright spots against the purple background [62,89,155,156,179,180]. However, this method is considered to be unprofitable due to the high consumption of expensive enzymes [16,174] and the observed false results [97]. Therefore, Nikolaichuk et al. [146,154] used indoxyl acetate as a substrate. AChE-inhibiting compounds were then observed on the plate as light spots on an indigo background. Another method for visualizing AChE inhibitors involves using Ellmann’ s reagent, 5,5′-dithiobis(2-nitrobenzoic acid), which reacts with tricholine formed from acetylcholine. The reaction of tricholine with 5,5′-dithiobis(2-nitrobenzoic acid) produces a yellow dye. AChE inhibitors are visible on the plate as bright spots against a yellow background. This method was used, for example, by Nagae et al. [181] to detect AChE inhibitors in Cladonia portentosa. It is believed that this method may be difficult to perform a proper analysis due to the poor contrast of enzyme inhibition zones with the plate background [16].

Tyrosinase is involved in the browning of food and in the melanogenesis process in mammals. Melanin in the human body serves a protective function, shielding the skin from the harmful effects of UV radiation. On the other hand, excessive tyrosinase activity causes increased melanin synthesis, leading to dermatological conditions manifested by hyperpigmentation. Therefore, natural substances that inhibit tyrosinase activity are sought, which could prevent the development of skin discolorations. TLC–bioautography is used to screen plant material for this purpose. Tyrosine [182,183] or levodopa [62,97,146,154,155,156,184] are used as substrates. Tyrosinase oxidizes the substrates to dopaquinone, from which dopachrome is formed during non-enzymatic reactions such as cyclization and oxidation. Dopachrome is measured spectrophotometrically in cuvette tests and is also visible on TLC plates as a gray background. Tyrosinase-inhibiting compounds appear as light spots on a gray background. However, it has been reported that the spectrophotometric method for determining tyrosinase inhibitors is unreliable when samples contain substances with strong reducing properties, such as flavonoids, which may lead to interpretation as tyrosinase inhibition, which might be interpreted as tyrosinase inhibition, which in turn, can lead to misleading results [185]. Due to the fact that plant extracts may contain numerous compounds with reducing properties, the reports of the detection of tyrosinase-inhibiting compounds by means of TLC–bioautography using tyrosine or levodopa as substrates should be treated with caution. These substances should be identified, and their effect on enzyme activity should be confirmed using other methods.

The previously mentioned reviews [16,162,174,175] did not mention the possibility of detecting aromatase inhibitors using TLC–bioautography. Such a test was recently developed by Dawood et al. [186]. Aromatase is an enzyme that catalyzes the conversion of androgens to estrogens, and drugs which lower aromatase activity are used, among other things, in the treatment of breast cancer in postmenopausal women. The method for detecting and measuring aromatase in plant extracts separated on a chromatographic plate uses dibenzylfluorescein (DBF), a fluorescent dye. It is a substrate for some cytochrome P450 isoforms, including aromatase. DBF is dealkylated by aromatase, forming fluorescein benzyl ester. This ester is then hydrolyzed to fluorescein using NaOH. TLC–bioautography of aromatase inhibitors present in separated extracts measures the inhibition of fluorescein production by inhibiting the aromatase enzyme. Aromatase inhibitors appear as dark spots against a blue fluorescent background. The degree of fluorescence reduction indicates the strength of aromatase inhibition. The authors validated this method and used it to detect aromatase inhibitors in 14 plants. The strongest aromatase-inhibiting activity was noted in extracts from Artemisia annua Pall., Vitex agnus castus L., Zingiber officinale Roscoe, Annona muricata L., Cicer arientinum L. and Artemisia monosperma Delile [186].

Recently, certain bioautographic assays have been developed to detect substances with anti-inflammatory activity that are inhibitors of the cyclooxygenase (COX) enzyme isoforms: COX-1 and COX-2. New anti-inflammatory drugs are being sought in plant materials, hoping that they will cause fewer side effects than the currently commonly used nonsteroidal anti-inflammatory drugs such as diclofenac, ibuprofen, and celecoxib, whose use may lead to adverse reactions. Because cyclooxygenase (COX) is actively involved in the inflammatory process, bioautographic assays for screening active compounds with anti-inflammatory activity detect substances that are inhibitors of the COX isoforms: COX-1 and COX-2 [96,153,173,187]. Their detection methods are based on the conversion of arachidonic acid to PGH2 during a two-step reaction catalyzed by cyclooxygenase. In the first step, arachidonic acid is oxidized to PGG2, while in the next step, PGH2 is formed by reduction from PGG2. Therefore, the authors used arachidonic acid as a substrate and N,N,N′,N′ -tetramethyl-p-phenylenediamine (synonym: Wurster’s blue) as a co-substrate. In order to detect COX-1 or COX-2 in TLC–bioautography, the developed TLC plates are sprayed with an appropriately prepared COX-1 or COX-2 enzyme solution and incubated under appropriate conditions. The enzyme solutions contain hematin to enhance enzyme activity and stability. The plate is then sprayed with arachidonic acid solution and Wurster’s blue to initiate the color reaction. The final step is to analyze the color reaction on the plate. COX-1 and COX-2 isoform inhibitors are visible after 5–10 min as white zones on a lilac-blue/purple background [96,153,173,187]. The color of the plate may fade after an hour [187].

COX-1 inhibitors were detected by Agatonovic-Kustrin et al. [96], in Ficus carica L. leaves. On the other hand, Jovic et al. [153] searched for COX-1 inhibitors among 19 medicinal plants commercially available in Belgrade, Serbia, as herbal teas. The greatest COX-1 inhibiting potential was demonstrated by green tea, greater celandine, and fumitory, while weaker activity was observed in walnut leaf and St. John’ s wort. The authors drew these conclusions based on the number and intensity of light spots on the plate. By comparing the R_F of active bands with other chromatograms the conclusion it was concluded that flavonoids were likely the compounds with COX-1 inhibitory activity [153].

Urbain et al. [173] developed a method for detecting COX-2 inhibitors. First, they optimized the concentrations of the reagents: enzyme and substrates, and then validated the method. For validation, they used pure compounds, both those known to be COX-2 inhibitors and those without COX-inhibiting activity. Antioxidants were also used to exclude false positive results. Finally, the researchers applied the test to detect inhibitors in mixtures of complex compositions, such as ethyl acetate extracts of Syzygium aromaticum (L.) Merr. & L.M.Perry, Fomitopsis pinicola (Sw.) P. Karst. and Hypholoma fasciculare (Huds.) P. Kumm. [173].

Oyarzún et al. [187] also developed a method for detecting COX-2 on HPTLC plates. They focused on creating a test which would be the most cost-effective. The principle of this test was the same as previously described [96,153,173]. Due to the fact that the most expensive reagent in these analyses is the enzyme, the authors tested various concentrations of the remaining reagents to minimize the amount of enzyme required for the test. Oyarzún et al. obtained adequate contrast in visualizing COX inhibition zones on the plate using 3 mL of 0.15 U/mL enzyme solution, whereas in previous studies, the enzyme (COX-1 or COX-2) was used at 1 U/mL [96,153,173]. The method proposed by Oyarzún et al. was characterized by similar detection capacity compared to the method developed by Urbain et al. [173]. Oyarzún et al. used the developed method to detect COX-2 inhibitors in Chiloe’s giant garlic [187].

3.4. Detecting Substances Affecting Endocrine Management

The compounds that affect the body’s endocrine system include, among others, compounds with affinity for estrogen or androgen receptors. The estrogenic activity is exhibited, among others, by phytoestrogens, among and may be helpful in alleviating menopausal symptoms. Other compounds affecting the endocrine system, especially those delivered accidentally or unintentionally, can adversely affect the body’s functioning. They may be present in plant material as secondary metabolites, or they may be contaminants caused by the use of plant protection products. Therefore, planar bioassays are very useful for detecting hormonally active compounds [188]. In these tests, a developed plate is sprayed with a suspension of genetically modified yeast cells containing human receptors, e.g., estrogen (planar Yeast Estrogen Screen—pYES test) or androgen (planar Yeast Androgen Screen—pYAS test). Yeast cells also contain the lac-Z gene encoding β-galactosidase. When a substance from the separated sample on the plate binds to an estrogen or androgen receptor, β-galactosidase activity increases, metabolizing the substrate used, which is usually 4-methylumbelliferyl-β-d-galactopyranoside. The presence of β-galactosidase produces a fluorescent blue compound, 4-methylumbelliferone, which is observed at 366 nm [188,189].

Ronzheimer et al. [189] attempted to improve the pYES test, while Schreiner et al. [188] improved the pYAS one. The improvement involved limiting the zonal diffusion on normal phase plates and expanding the bioassay/biotest to allow for the testing of other biological activities. Using the polymer coating fixed the separated substances on the plate and ensured appropriate sharpness of the zones. This approach enabled the detection of substances with estrogenic/androgenic and antiestrogenic/antiandrogenic activity, as well as the detection of false positives results and synergistic substances. Among the 68 botanicals analyzed with this multiplex assay, 17 compounds interacting with the estrogen receptor were initially detected [189]. It is worth emphasizing that the pYAS bioassay allowed for the first time the detection of antiandrogenic compounds in artichoke, ginger, eucalyptus, cola, garlic, star anise, cinnamon, and chicory [188]. Multiplex assays for the detection of estrogens and androgens were used to study various samples of Rhodiola rosea L. root [146] Akebia quinata D. leaves/fruits, Clitoria ternatea L. flowers [154] Mentha × piperita L. leaves [158], and Tunisian sage species [62].

3.5. Detection of Genotoxic Substances

A team led by Prof. Morlock developed a planar assay for the analysis of genotoxicity in complex samples such as food and dietary supplements. The researchers demonstrated that the planar assay for detecting genotoxic substances is suitable for analyzing complex mixtures after separation on an HPTLC plate and can additionally be used to study the detoxification of the detected genotoxic substances using the S9 microsomal fraction from rat liver [190].

The proposed planar bioassay, called the SOS-Umu-C assay, was used to detect genotoxic compounds in oils, including various vegetable oils, taking into account their storage conditions. The HPTLC plate containing the separated oil components was sprayed with a suspension of Salmonella thyphimurium TA1535 and incubated under appropriate conditions. The Salmonella thyphimurium strain used in this analysis contains the Umu-C gene associated with the lac-Z reporter gene. DNA damaging factors have the ability to induce Umu-C gene expression, which results in the activation of the SOS repair response. Since the Umu-C gene is linked to the lac-Z gene, an increased expression of this gene results in increased β-galactosidase activity. For the purpose of detection, the compounds degraded by β-galactosidase, e.g., fluorescein di-β-D-galactopyranoside (FDG), are used. Genotoxic compounds were visible as light green fluorescein spots on a green background observed at 254 nm, because initiation of the SOS reaction leads to increased β-galactosidase activity, resulting in the release of fluorescein from FDG. The authors demonstrated that in commonly used oils, considered healthy, the oxidation of unsaturated fatty acids leads to the formation of, among others, epoxidized forms, which are potentially genotoxic. They also noted that the amount of genotoxic compounds in oils increases and depends on storage time and exposure to air [190].

The SOS-Umu-C planar biotest was also used to detect genotoxic substances in commercial dietary supplements from Rhodiola rosea root [146] and Akebia quinata D. leaves or fruits, in Clitoria ternatea flowers [154] and Mentha × piperita leaves from different cultivars [158], aerial parts of sage species collected in Tunisia [62] and African leafy vegetables [157]. Surprisingly, a genotoxic compound was found in two samples of 15 commercial Rhodiola rosea root products [146]. Bands indicating the presence of genotoxic substances were also observed in three of four Akebia quinata leaf/fruit samples. The plant material from Akebia quinata differed in origin. In contrast, no genotoxic substances were detected in Clitoria ternatea flowers, peppermint leaves, and aerial parts of sage [62,154,158]. A few faint spots indicating the presence of genotoxic substances were detected in several African leafy vegetables, but these results require confirmation.

Scientists suggest that, in order to ensure consumer safety, genotoxicity testing should be implemented using planar screening tests for unknown genotoxins which may be present in food, dietary supplements, and medicinal plants [190]. It is worth noting, however, that the above-cited studies were performed in planar chromatography laboratories with automated equipment [146,190]. Windisch et al. [191] demonstrated that planar SOS-Umu-C tests for genotoxin detection can be performed in laboratories without costly equipment. Satisfactory bioautograms were obtained by manually applying samples to chromatographic plates using manual spraying of the chromatograms. In contrast to previously cited works [62,146,154,157,158,190] resorufin-β-D-galactopyranoside was used as a substrate, from which resorufin is released under the influence of β-galactosidase, activated by the action of genotoxic compounds on Salmonella thyphimurium TA1535. Genotoxins are observed under white light illumination as pink resorufin spots on a light background or at 366 nm as orange fluorescent spots on a dark, orange fluorescent background. Obtaining satisfactory chromatographic separations and signals indicating the presence of genotoxins in conditions without access to an automated HPTLC laboratory is important information for researchers who do not have significant financial resources but plan to perform planar bioassays [191].

Schmidtmann et al. [192] developed a planar test for detecting mutagenic substances based on the Ames test. This test detects substances causing point mutations or shifts in the open reading frame and is a complement to the SOS-Umu-C test [192]. In the developed planar Ames Salmonella typhimurium TA100 and TA98 strains were used as test strains. Separating mutagens from other compounds present in the sample using HPTLC enabled the detection of individual mutagenic compounds. A plate with the mobile phase removed was sprayed with a suspension of the test microorganism in a solution of bromocresol purple (an indicator). Mutagenic substances are visible under white light as yellow spots on a purple background and faintly blue-fluorescent spots at 366 nm. Due to zonal diffusion, the incubation time was shortened to 5 h in the planar test compared to 48 h incubation in the conventional Ames test. It was probably the reduced incubation that reduced sensitivity; therefore, it is necessary to improve the planar Ames test, including its sensitivity and selectivity. The authors already have a plan to improve these parameters; to prevent zonal diffusion with extended incubation time, using zonal fixation is suggested. To improve selectivity, they propose using other visualization methods based on fluorescence or luminescence instead of the bromocresol purple indicator [192]. This method has been tested on known mutagenic compounds, but according to the authors, it is suitable for the analysis of complex samples, including plant samples.

3.6. Detection of Cytotoxic Substances

Mügge et al. [193] developed planar tests for the detection of cytotoxic substances. The search for such compounds is important due to the need to discover new drugs that could be candidates (could be applied to treat) for the treatment of cancer. Planar bioassays for the detection of cytotoxic substances use human adherent cells. Genetically modified cells derived from cervical cancer (HeLa) and human embryonic kidney (HEK293) are used. The modification involves the introduction of a luciferase gene. The test is based on culturing cells on the surface of an HPTLC plate after previous separating of complex samples, such as plant extracts. Cytotoxic compounds inhibit cell growth and, due to the presence of luciferase, are visible as zones with a reduced bioluminescence signal. The authors emphasize that the cell lines used do not require a license, which ensures the wide application of the assay. However, they suggest maintaining sterile conditions during the procedure. This test was used to assess the cytotoxicity of Saussurea costus (Falc.) Lipsch. roots and, at the same time, to confirm the effectiveness of the test. This plant material is known to contain cytotoxic compounds [193].

A planar assay for detecting cytotoxic compounds using genetically modified HEK293 cells was used to analyze 11 pink pepper samples. After separating extracts from the tested samples and performing a bioassay, zones of reduced bioluminescence were observed on an HPTLC plate [194].

4. Conclusions

In summary, since January 2022, numerous studies have used thin-layer chromatography (TLC) and high-performance thin-layer chromatography (HPTLC) techniques to analyze plant material. TLC/HPTLC has been employed for the qualitative and quantitative analysis of active compounds in singleplant materials and multi-component herbal preparations. These techniques have also been combined with the determination of the biological activity of bands separated on a plate (TLC–bioautography) as well as with advanced techniques such as mass spectrometry (MS), nuclear magnetic resonance (NMR), and surface-enhanced Raman spectroscopy (SERS) for identification purposes. Recently, new bioautography analysis methods have been developed, including antioxidant detection through lipid peroxidation inhibition, aromatase inhibitor detection, and COX isoform detection. New planar bioassays based on genetically modified human adherent cells have been developed for cytotoxicity testing. Recently, new image analysis methods have also been implemented.

However, there are certain limitations to using TLC to analyze plant material and herbal formulations that may affect the quality, reproducibility, and interpretation of results. Plant extracts contain hundreds of different compounds that vary widely in polarity, molecular weight, and chemical reactivity. Consequently, overlapping spots may appear on the plate, hindering the separation and detection of individual components. Additionally, there may be difficulties analyzing unstable compounds sensitive to light or temperature. TLC/HPTLC has lower resolution than modern techniques, such as LC-MS. The method’s sensitivity often does not allow for the detection of components present in low concentrations. The thickness and unevenness of the stationary phase layer can blur spots. Environmental conditions such as humidity, temperature, solvent type, and development time have a significant impact on the results. Even slight changes can cause differences in R_F values, making it difficult to compare results between laboratories. TLC is primarily a qualitative or semi-quantitative method; accurate quantification of active compounds requires densitometry or image analysis (TLC densitometry or TLC image analysis).

Because TLC/HPTLC is inexpensive and has a high throughput, it has great potential for testing the quality of plant material and herbal formulations, including the detection of adulteration. This is especially important today because herbs and herbal preparations are commonly used to treat various conditions. Their chemical composition, particularly the content of biologically active substances responsible for therapeutic effects, must be monitored. All properly validated TLC methods described in Table 1 can therefore be used by companies producing herbal teas or pharmaceutical preparations to quantitatively determine the presence of specific components in the material. Supervisory authorities can also use the results of these studies to control the content of these biologically active substances in various types of food and pharmaceutical products. Methods that describe the possibility of testing and detecting the adulteration of herbal preparations with synthetic substances are extremely important for potential applications. Bioautographic methods, on the other hand, may contribute to the discovery of new natural compounds with pharmacological potential. Detecting substances with adverse effects, such as genotoxicity, in food and plant preparations would help ensure consumer safety.

However, for the TLC method to be used for testing the quality of plant material in a given country, detailed protocols must be developed for conducting such analyses. These protocols should include information on extract preparation, including the solvent used, extraction time, and conditions; separation and derivatization conditions; documentation; and chemometric analysis. All planned testing methods must be fully validated in accordance with applicable standards.

Further studies of plant material using TLC/HPTLC should involve the increased use of chromatographic plates with a concentration zone, which will enable sample concentration, retain contaminants in the concentration zone, and obtain compact chromatographic bands after separation. Furthermore, researchers should more frequently identify biologically active compounds present in samples derived from plant material/herbal formulations by combining TLC/HPTLC with, for example, MS or NMR. The detection of biological activity of plant extract components separated by TLC/HPTLC shows great promise. Researchers should focus on developing new tests and improving the sensitivity and specificity of existing bioautographic tests. Additionally, it is important to reduce the cost of performing these tests so they can be used more widely in plant material research.