Search Results (293)

The identification/classification of textile fibres is essential in manufacturing, forensic science, cultural heritage preservation, and recycling. Conventional methods, including solubility tests, optical microscopy, and chromatographic techniques, are often destructive, labour-intensive, and limited in scope. Vibrational spectroscopy, particularly near-infrared (NIR), Fourier-transform infrared (FTIR), and Raman spectroscopy, has emerged as a rapid, non-destructive, and accurate alternative for fibre analysis. However, multi-composition textiles, dyes, finishing agents, and ageing effects frequently cause overlapping spectral features, hampering direct interpretation. This review examines the combined use of vibrational spectroscopy and chemometrics for textile fibre discrimination. It critically evaluates the performance of different spectroscopic techniques in classifying natural, synthetic, and blended fibres. The role of multivariate analysis methods, such as PCA, PLS, LDA, SIMCA, and machine learning algorithms, in improving spectral interpretation and classification accuracy is highlighted. Key factors affecting model robustness, including spectral pre-processing, sample heterogeneity, moisture, and colour, are also discussed. The integration of spectroscopy with chemometrics provides a robust, scalable, and sustainable solution for fibre identification, supporting quality control, fraud detection, and circular economy initiatives. This approach demonstrates significant potential for both research and industrial applications. Full article

Rhodiola species are traditionally used to mitigate hypoxia-related symptoms, but comparative evidence on their chemical bases and active constituents is limited. We implemented an integrated “qualitative analysis–pharmacological exploration–correlation analysis–molecular validation” workflow to compare Rhodiola crenulata, R. kirilowii, and R. rosea. Ultra-high-performance liquid chromatography–Q Exactive mass spectrometry (UPLC-QE-MS) profiling identified 175 metabolites across the three species, of which 161 were shared; multivariate analyses (principal component analysis, PCA; partial least squares–discriminant analysis, PLS-DA) revealed 30 differential compounds. In a normobaric hypoxia mouse model using herbal powder solutions, all three species significantly increased survival time versus control (p < 0.05), with mean survival times of 48.16 min (RR), 47.07 min (RC), and 44.82 min (RK) compared with 44.34 min for the positive control. Chemometric correlation (partial least squares regression, PLSR) combined with grey relational analysis (GRA) prioritized 14 compounds consistently associated with anti-hypoxia efficacy; six representative metabolites—epicatechin, 3-O-galloylquinic acid, salidroside, p-coumaric acid-4-O-glucoside, citric acid, and geraniol—were selected for in silico assessment. Molecular docking against hypoxia-inducible factor-1α (HIF-1α) yielded favorable binding poses (docking scores < −4.0), providing preliminary molecular-level plausibility without claiming mechanistic proof. This multi-level approach clarifies chemical–pharmacological relationships among Rhodiola species and provides prioritized candidate compounds for targeted isolation and mechanistic validation. Full article

Liver cancer continues to be a predominant cause of cancer-related mortality globally, primarily attributable to late diagnosis and a scarcity of dependable biomarkers for early identification. Raman spectroscopy has emerged as a valuable analytical instrument for liver cancer detection, providing rapid, label-free, and non-destructive molecular profiling of biological specimens. Raman-based methodologies can discern malignant from non-malignant conditions by analyzing small biochemical alterations in biofluids, including blood, urine, and exosomes, as well as in liver tissue, yielding unique spectrum fingerprints. Progress in chemometric analysis, including machine learning models and multivariate statistical methods, has significantly improved the diagnostic precision of Raman spectroscopy, attaining elevated sensitivity and specificity across numerous studies. Furthermore, the integration of complementary techniques, such as surface-enhanced Raman spectroscopy (SERS) and Raman optical activity (ROA) has broadened its prospects for clinical application. This review article elucidates the contemporary applications of Raman spectroscopy in the diagnosis of liver cancer, presents pivotal findings across various sample types, and examines the challenges and future prospects of building Raman-based platforms as dependable diagnostic instruments in oncology. Full article

The rapid and selective discrimination of microplastics (MPs) is a critical analytical challenge, particularly as current carbon quantum dot (CQD)-based sensors often rely on single-wavelength “turn-on/off” or staining mechanisms that lack polymer-specific resolution. This work addresses these limitations by presenting a mechanism-driven fluorescence sensing platform using ultra-fine polyamide-derived carbon quantum dots (PACQDs; ~1.4 nm) to identify three prevalent MPs: polyamide (PA), polypropylene (PP), and polyethylene terephthalate (PET). Excitation–emission matrix (EEM) spectroscopy reveals polymer-specific photophysical responses: PAMPs and PPMPs induce fluorescence enhancement of 11.66% and 11.43%, respectively, whereas PETMPs cause net quenching (−4.61%) alongside a distinct, red-shifted emission band. Despite a common scatter-dominated peak at 290/308 nm, quantitative discrimination is achieved via integrated intensity and red/blue emission ratios (0.0137 for PAMPs, 0.0098 for PPMPs, and 0.0072 for PETMPs). Multivariate analysis reinforces this discrimination. Parallel factor analysis (PARAFAC) resolves the EEM data into three fluorescent components representing the intrinsic CQDs core and two interaction-induced surface states with a rank 3 model reducing the relative reconstruction error from 0.1625 to 0.1285. Principal component analysis (PCA) yields clear separation of the polymer classes, with the first two principal components capturing ~88% of the total spectral variance. ATR–FTIR spectroscopy provides direct molecular evidence for the underlying mechanisms: amide–amide coupling and interfacial rigidification for PAMPs; hydrophobic interaction without spectral shifts for PPMPs; and a synergistic interaction involving hydrogen bonding and π–π stacking for PETMPs. In particular, these polymer-specific fluorescence fingerprints are largely preserved in tap water, despite elevated background intensity and partial contrast attenuation, demonstrating the resilience of the EEM–chemometric approach under realistic matrix conditions. Collectively, the strong agreement between fluorescence metrics, multivariate signatures, and interfacial chemistry establishes a robust structure–property framework and positions PACQDs as a rapid, label-free, and matrix-tolerant platform for reliable microplastic discrimination in environmental analysis. Full article

Fumonisins and deoxynivalenol (DON) are toxic secondary metabolites, produced by Fusarium species frequently contaminating maize, representing a critical challenge to food safety and human health. Conventional analytical methods, such as HPLC and ELISA, are accurate but time-consuming and require complex sample preparation. In contrast, near-infrared spectroscopy (NIR) has emerged as a rapid, non-destructive, and cost-effective alternative for mycotoxin screening. This study investigates the potential of NIR spectroscopy combined with chemometric techniques to detect and quantify fumonisins (primarily FB1 and FB2) and DON in maize. A total of 60 maize samples were analyzed with mean concentrations of 534 µg/kg for FB1, 208 µg/kg for FB2, and 130 µg/kg for DON. The highest cumulative contamination of FB1 + FB2 reached 3420 µg/kg. While 30% of the samples showed no detectable fumonisin contamination, DON was absent in 17% of the samples. The best performing predictive models were developed using second-derivative pre-processing of the NIR spectra. The NIR calibration model yielded coefficients of determination (R²) of 0.91 for FB1, 0.88 for FB2, and 0.92 for DON, with corresponding root mean square errors (RMSE) of 683, 282, and 115 µg/kg, respectively. These results demonstrate that NIR spectroscopy, particularly when integrated with multivariate analysis, is a promising tool for distinguishing contaminated from uncontaminated maize and estimating mycotoxin levels with reasonable accuracy. These findings support the application of NIR as a practical tool for routine screening and quality control in the maize supply chain. Full article

Adulteration of high-value oils such as olive and camellia oil poses serious challenges to market integrity and consumer safety. This study develops a comprehensive, model-free marker library for high-throughput detection of single and multivariate adulteration across nine vegetable oils (olive, camellia, sesame, rapeseed, flaxseed, soybean, peanut, industrial hemp seed, and sunflower seed oils) using untargeted metabolomics via UHPLC-Q-TOF-MS. We identified 34 characteristic markers, including 9 confirmed by reference standards, such as hydroxytyrosol in olive oil, camelliasaponins in camellia oil, and sesamin in sesame oil, which are uniquely present in specific oils and absent in others. The method enables reliable qualitative screening of adulteration at levels as low as 5% without dependence on chemometric models. Validation using binary and multicomponent blends confirmed its robustness and specificity. In commercial sample analysis, adulteration was detected in 16.0% of olive oils (4/25) and 12.7% of camellia oils (7/55), with results consistent with regulatory findings. This work establishes the first integrated marker library for simultaneous screening of nine vegetable oils, offering a standardized, high-throughput tool for large-scale market surveillance that bridges the gap between discovery-based omics and routine regulatory practice. Full article

The aim of this study was to evaluate, on a preliminary basis, the ability of multivariate techniques to predict the antioxidant activity of selected Stachys and Betonica species, based on chromatographic data. The methanol extracts of six Stachys species and ten Betonica species were analyzed using reversed-phase high-performance liquid chromatography (RP-HPLC) to obtain their chromatographic profiles. The phytochemical similarity of the samples was assessed using a selected chemometric method (principal component analysis (PCA) and hierarchical cluster analysis (HCA)). The antioxidant activity of the studied extracts (DPPH with 2,2-diphenyl-1-picrylhydrazyl reagent and FRAP—ferric reducing antioxidant power) was determined using a spectrophotometric technique. A multivariate PLS model was then used to predict the antioxidant activity of the methanolic extracts of Stachys and Betonica species based on their RP-HPLC fingerprints. The two obtained PLS models proved useful for predicting the biological activity of the tested extracts. High correlation coefficients (DPPH: R² = 0.9963; FRAP: R² = 0.9895) confirmed the reliability of the PLS prediction model. The results confirmed the effectiveness of combining qualitative and quantitative chromatographic fingerprinting methods with antioxidant activity testing and chemometric analysis, demonstrating that extracts from Stachys and Betonica are a rich source of bioactive substances with antioxidant properties. Full article

Maca (Lepidium meyenii Walp.), a Brassicaceae species native to the high Andes of Peru, has gained global attention as a functional food and herbal medicinal product due to its endocrine-modulating, fertility-enhancing, and neuroprotective properties. Although numerous studies have addressed its biological effects, a systematic and up-to-date summary of its chemical constituents and analytical methodologies is lacking. This review aims to provide a critical overview of the chemical constituents of L. meyenii and to evaluate analytical studies published between 2000 and 2025, focusing on recent advances in extraction strategies and qualitative and quantitative analytical techniques for quality control. Major compound classes include macamides, macaenes, glucosinolates, and alkaloids, each contributing to maca’s multifaceted activity. Ultra-(high-)performance liquid chromatography (U(H)PLC), often coupled with ultraviolet, diode array, or mass spectrometric detection, is the primary and most robust analytical platform due to its sensitivity, selectivity, and throughput, while ultrasound-assisted extraction improves efficiency and reproducibility. Emerging techniques such as metabolomics and chemometric approaches enhance quality control by enabling holistic, multivariate assessment of complex systems and early detection of variations not captured by traditional univariate methods. As such, they provide complementary, predictive, and more representative insights into maca’s phytochemical complexity. The novelty of this review lies in its integration of conventional targeted analysis with emerging approaches, comprehensive comparison of analytical workflows, and critical discussion of variability related to phenotype, geographic origin, and post-harvest processing. By emphasizing analytical standardization and quality assessment rather than biological activity alone, this review provides a framework for quality control, authentication, and safety evaluation of L. meyenii as a functional food and dietary supplement. Full article

Environmental element monitoring is essential for assessing environmental quality, identifying pollution sources, evaluating ecological risks, and understanding long-term contamination trends. Modern monitoring campaigns routinely generate large volumes of complex data that require advanced analytical strategies. This study applied chemometric techniques to analyze elements and BVOCs (biogenic volatile organic compounds) measured from Posidonia oceanica and related environmental matrices (seawater, sediment, and rhizomes) during three sampling campaigns in the Tremiti Islands (Italy). Twenty-two trace elements were quantified, and BVOC profiles were obtained from the leaf samples. The dataset was analyzed using a combination of univariate visualizations, unsupervised and supervised multivariate techniques, and multi-way methods. PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares-Discriminant Analysis) revealed distinct spatial (leaf section) and temporal (sampling period) trends, supported by consistent elemental markers. A low-level data fusion approach integrating BVOC and element data improved group discrimination and interpretability. PARAFAC (PARAllel FACtor analysis) applied to a three-way array successfully separated background trends from meaningful compositional changes, uncovering latent structures across chemical, spatial, and temporal dimensions. This work illustrates the usefulness of chemometrics in environmental monitoring and the effectiveness of combining multivariate tools and data fusion to improve the interpretability of complex environmental datasets. The methodology used in this study is fully generalizable and applicable to other environmental multi-way datasets. Full article

This study evaluates the feasibility of Near-Infrared (NIR) spectroscopy combined with chemometric modeling for monitoring twin-screw melt granulation. Lactose monohydrate was used as a model excipient and polyethylene glycol (PEG 6000) (Sistemas Analíticos S.A, Buenos Aires, Argentina) as a meltable binder. Granules were produced under different processing conditions by varying binder content, screw configuration (kneading or conveying elements), and measurement temperature. NIR spectra were acquired on-line on a conveyor belt and analyzed using Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression. The regression models showed excellent predictive performance for PEG 6000 content in lactose-based granules, with coefficients of determination higher than 0.998 for both raw and preprocessed spectral data. PCA successfully discriminated between granulated and non-granulated materials, as well as between granules produced with different screw configurations, demonstrating the sensitivity of the technique to processing conditions and granule formation mechanisms. In addition, spectral differences associated with measurement temperature were detected, with derivative-based preprocessing improving the discrimination between warm and cooled granules. Overall, the results demonstrate that NIR spectroscopy, coupled with multivariate analysis, is a robust and non-invasive tool for real-time monitoring of twin-screw melt granulation, with strong potential to enhance process understanding, control, and product consistency in continuous pharmaceutical manufacturing. Full article

Rapid and reliable analytical methods are required to support quality control and decision-making in lithium-bearing mineral processing. In this study, the application of Fourier Transform Infrared (FTIR) spectroscopy combined with Partial Least Squares (PLS) chemometric modeling is evaluated for the simultaneous prediction of lithium oxide (Li₂O) and spodumene contents in pegmatitic samples. Two independent PLS models were developed using FTIR spectra preprocessed with first derivative and/or Standard Normal Variate (SNV). Spectral regions were selected based on the vibrational response of Al–O, Si–O, and OH⁻ groups, which are indirectly influenced by lithium-bearing phases. The spectral datasets were divided into calibration and independent external test sets, and model performance was assessed using statistical metrics and Principal Component Analysis (PCA). The Li₂O model achieved an R² of 0.9934 and an RMSEP of 0.185 in external validation, with a mean absolute error below 0.15%. The spodumene model achieved an R² of 0.9961, an RMSEP of 1.79, and a mean absolute error of 2.80%. These results demonstrate that the FTIR-PLS approach enables efficient quantitative estimation of lithium-bearing minerals, with reduced analytical time, good predictive accuracy, and suitability for application in process control and mineralogical sorting environments. PCA confirmed the statistical representativeness of the test sets, with no evidence of spectral extrapolation. Full article

Herba Hyssopi is a key remedy in Uighur medicine for asthma and cough, frequently used as the monarch or minister herb in prescriptions. However, the lack of effective quality assessment methods complicates the detection of adulteration with common substitutes. In this study, UPLC-LTQ-Orbitrap-MS, network pharmacology, molecular docking, and cell experiments were employed to establish scientific and effective quality control methods to differentiate Hyssopus cuspidatus Boiss from its common adulterants. The results showed that a total of 41 chemical constituents were identified from Herba Hyssopi. Network pharmacology analysis revealed 133 potential target genes associated with its therapeutic actions, among which EGFR, MMP9, TNF, PTGS2, MAPK3, ESR1, and TP53 emerged as key targets. Cellular experiments further demonstrated that diosmin, linarin, and rosmarinic acid significantly suppressed nitric oxide (NO) generation and the release of pro-inflammatory cytokines. Based on these findings, a validated HPLC method was established for the simultaneous quantification of these three bioactive markers, providing a reliable tool for the quality assessment and authentication of Herba Hyssopi. This study offers a scientific basis for improving the standardization and quality control of Herba Hyssopi in traditional medicine applications. Full article

Humulus lupulus L. (hops) is essential in brewing due to its contributions to bitterness, flavor, and aroma. This study compared the volatile profiles of five commercially important hop varieties—Amarillo, Ariana, Cascade, Centennial, and El Dorado—grown in their main regions of origin (United States for Amarillo, Cascade, and El Dorado; Germany for Ariana; and Brazil for Centennial). Headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS) enabled the identification of 312 volatile compounds, including monoterpenes (e.g., myrcene, linalool, geraniol), sesquiterpenes (e.g., humulene, caryophyllene), esters, alcohols, aldehydes, and ketones. Amarillo showed the highest myrcene content (22.61% of the total volatile area), while Centennial was distinguished by elevated γ-muurolene (20.59%), and El Dorado by the highest level of undecan-2-one (10.47%), highlighting marked varietal differences in key aroma-active constituents. Multivariate, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), clearly discriminated the five varieties: PC1 (41.04% of the variance) separated samples enriched in fruity/floral monoterpenes and esters from those dominated by woody/resinous sesquiterpenes, whereas PC2 (25.93% of the variance) reflected variation in medium-chain esters, ketones, and waxy compounds. These chemometric patterns demonstrate that both genetic background and growing region terroir strongly shape hop volatile composition and, consequently, aroma potential, providing brewers with objective criteria for selecting hop varieties to achieve specific sensory profiles in beer. Full article

Early detection of premalignant cervical lesions is essential for improving cervical cancer outcomes; however, current screening methods frequently lack adequate sensitivity and specificity. This research introduces a diagnostic platform that integrates lectin-based biosensors with spectral and multivariate analysis. The biosensors are composed of gold nanoparticles (AuNPs) conjugated with Maackia amurensis (MAA) lectin, which selectively binds to α2,3-linked sialic acid. Validation was performed using cervical cancer cell lines (SiHa, HeLa, C33A), fibroblasts, and cervical scrapes, and specificity was verified by enzymatic removal of sialic acids. Spectral data were obtained using attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and analyzed by principal component analysis (PCA). Application of PCA to the 1600–1350 cm⁻¹ spectral region, using 99% confidence ellipses, enabled clear differentiation between samples negative and positive for intraepithelial lesions in a double-blind study of 58 patients. The MAA biosensors exhibited high sensitivity and specificity, comparable to established diagnostic methods. These results indicate that the combination of ATR-FTIR spectroscopy, MAA lectin-based biosensors, and chemometric analysis provides a robust and reliable approach for early detection of premalignant cervical lesions, with considerable potential to enhance patient outcomes. Full article

The optimization of bioactive compound extraction from medicinal herbs is critical for developing functional food ingredients with substantiated health benefits. This study employed response surface methodology (RSM) and partial least squares (PLS) regression to maximize polyphenol recovery and antioxidant capacity from five medicinal herbs (Helichrysum stoechas, Chelidonium majus, Mentha pulegium, Artemisia absinthium, and Adiantum capillus-veneris). A custom experimental design assessed the effects of herb identity, extraction technique, and solvent-to-solid ratio on total polyphenolic content (TPC), total flavonoid content (TFC), ferric reducing antioxidant power (FRAP), and DPPH radical scavenging activity. The PLS compromise optimum was identified for M. pulegium using 60% ethanol at 55 mL/g, yielding 37.54 ± 2.10 mg GAE/g dw TPC, 21.62 ± 1.15 mg RtE/g dw TFC, 334.38 ± 12.37 µmol AAE/g dw FRAP, and 262.67 ± 9.46 µmol AAE/g dw DPPH. HPLC-DAD profiling revealed 18 polyphenolic compounds (10.22 ± 0.34 mg/g dw), dominated by kaempferol-3-O-β-rutinoside, protocatechuic acid, and luteolin-7-O-glucoside. These compounds contribute complementary mechanisms: protocatechuic acid modulates oxidative and antioxidant pathways, kaempferol-3-O-β-rutinoside exerts cardioprotective and anti-inflammatory effects via VEGF-C binding, and luteolin-7-O-glucoside suppresses NF-κB-mediated inflammatory signaling. Principal component analysis (PCA) explained 84.8% of variance, clearly separating optimized from non-optimized extracts, while PLS confirmed strong correlations between specific phenolics and antioxidant indices. Overall, this integrated chemometric approach demonstrates that data-driven optimization can deliver phenolic-rich herbal extracts with robust and balanced antioxidant potential for functional food applications. Full article