Search Results (107)

Next Generation Sequencing is a constantly evolving technology whose applicability is increasingly expanding into the field of routine food analysis. In this context, metabarcoding has proven to be a powerful tool for detecting food fraud due to its ability to taxonomically classify even highly fragmented DNA from processed products. While Illumina sequencing platforms, representing second-generation sequencing technologies, are widely used for such applications, fourth-generation sequencing devices such as Oxford Nanopore Technologies’ MinION offer advantages in terms of flexibility, scalability, and simplified handling. In this study, we evaluate the transferability of an established Illumina-based metabarcoding method for the detection of pistachio adulteration in processed foods to the MinION platform of Oxford Nanopore Technology. In more detail, we transferred the established method from Illumina on both MinION and Flongle flow cells to assess sequencing accuracy, quantification potential and practical aspects such as cost-efficiency and workflow. Our results highlight the applicability of the MinION sequencing platform as a reliable and cost-effective alternative to Illumina protocols for routine food authenticity testing, enabling faster processing and broader accessibility without significantly compromising accuracy. Full article

Dendrobium officinale is a valuable medicinal and edible homologous health food. It has immunomodulatory, antioxidant, and metabolism-regulating properties. However, its adulteration is widespread, seriously compromising product quality and safety. Traditional adulteration detection methods are complex, costly, and time-consuming, making it urgent to establish a rapid and non-destructive detection approach. This study developed a rapid identification and quantification method for adulterated D. officinale. The method combined near-infrared (NIR) spectroscopy with data-driven soft independent modeling of class analogy (DD-SIMCA) and partial least squares regression (PLSR) models. PCA, PLS-DA, and OPLS-DA were first used to visualize sample clustering and group differences. DT, SVM, ANN, and NB were used for classification. DD-SIMCA and PLSR were used for one-class modeling and quantitative analysis. Raw spectral data were preprocessed using multiplicative scatter correction (MSC), the standard normal variate (SNV), the first derivative, and Savitzky–Golay smoothing. In the identification analysis, the DD-SIMCA model achieved 100% sensitivity and 100% specificity in the validation set. Its overall accuracy in the independent test set was 99.2%, demonstrating excellent discrimination performance. In addition, SVM combined with NIR also achieved good accuracy. In the quantitative analysis of adulteration, the PLSR model predicted different adulteration levels. Most calibration and validation sets showed R² values above 0.99 and RMSE values below 0.05, indicating excellent predictive performance. The results indicate that NIR combined with DD-SIMCA and PLSR can achieve rapid identification and accurate quantification of adulterated D. officinale samples. This approach provides strong support for quality control and regulatory supervision of high-value health foods. Full article

Accurate authentication of herbal medicine Descurainiae Semen, the tiny seeds of Descurainia sophia, is challenging due to their morphological similarity to various adulterants. To develop a precise and reliable molecular identification method, we conducted comparative analyses of rDNA-ITS sequences using D. sophia and five adulterant species and subsequently developed species-specific sequence-characterized amplified region (SCAR) markers. The discriminatory power and detection limits of these markers were evaluated using serially diluted genomic DNA from each species and commercially available Descurainiae Semen, respectively. The SCAR markers developed in this study enabled the detection of adulterant contamination at levels as low as 0.01–1%. Among several potential adulterants tested using 17 herbal medicines, Erysimum macilentum was found to be the most common adulterant in commercial products, with a ratio of 88%. The SCAR-PCR assay established in this study provides a rapid and accurate tool for identifying D. sophia and illegal adulterants at the species level and at very low contamination levels, thereby supporting improved quality control and enhancing consumer confidence in the herbal medicine industry. Full article

In Algeria, honey plays an important role in nutrition, traditional medicine and sustainable food practices. However, little is known about how consumers perceive and use this product in their daily lives. This study aims to explore how honey consumption patterns among a specific segment of Algerian households, mainly young, educated, and digitally active individuals, relate to traditional practices and contribute to sustainable food systems and cultural heritage. An online survey remained open for four months, and the final number of participants was 770 individuals from 51 wilayas, using convenience sampling through academic and social media networks. As this was an exploratory study, no fixed statistical population was defined. Chi-square tests revealed significant associations between age and honey consumption frequency (χ² = 45.33, p = 0.0010), annual purchase quantity (χ² = 111.49, p < 0.0001), and buying frequency (χ² = 47.26, p < 0.0001), as well as between climatic zone and buying source (χ² = 34.90, p = 0.0097). The findings highlight honey’s multifunctional role, not only as a food product, but also as a traditional remedy and cosmetic ingredient, embedded in cultural routines and health practices. Consumer preferences are shaped by sensory attributes such as taste, type, and appearance, while purchasing decisions are strongly influenced by trust-based relationships, with most respondents favoring direct acquisition from beekeepers or known individuals. These informal sourcing habits reflect sustainable traditions that support local producers and reinforce consumer confidence. However, widespread misconceptions, such as the belief that crystallized honey is adulterated, reveal a need for targeted consumer education. To promote sustainable honey consumption, the study recommends clearer labeling, school-based programs, and public awareness campaigns. Full article

Rapeseed (Brassica napus) honey is a popular monofloral honey produced in Poland and is often suspected of pesticide-residue contamination due to the extensive use of pesticides in oilseed rape cultivation. Moreover, because of the presence of fatty acids, it can absorb hydrophobic polycyclic aromatic hydrocarbons (PAHs) that occur as environmental pollutants. Thus, the aim of the study was to assess the safety of rapeseed honey in terms of pesticide residues and PAHs contamination in relation to its functional properties, including antioxidant properties, polyphenol profile, protein content, and enzymatic activity. Local honey samples originating from Lublin (five) and Podkarpackie (five) Voivodeships were compared with five samples purchased from commercial sources. None of 58 pesticides, including carbamates, organophosphorus, organochlorines, pyrethroids, and neonicotinoids, were detected in the tested honey samples. All samples were also completely free of four major harmful PAHs legally limited in food (benzo[a]pyrene, benz[a]anthracene, chrysene, and benzo[b]fluoranthene). Among other PAH compounds, seven were detected accidentally in samples of various origins. The total phenolic content and antioxidant activity determined by DPPH^•, FRAP, and CUPRAC assays were relatively uniform among the groups studied. High-performance thin-layer chromatography (HPTLC) revealed characteristic fingerprints including kaempferol, ferulic acid, and caffeic acid, providing a specific profile that can be considered a marker of rapeseed honey authenticity and used to detect adulteration. Protein content ranged from 18 to 85 mg/100 g, remaining within the range typical for light honeys, while α-glucosidase activity was significantly reduced in commercial products, reflecting the effects of processing and storage. The study confirmed the high functional value and safety of rapeseed honey offered on the South-Eastern Poland market, which confirm the cleanliness of the bees’ habitat in terms of pesticide residues and PAHs pollution. Nevertheless, regular monitoring of pesticide residues and PAHs in honeys from agricultural areas remains advisable. Full article

Salvia miltiorrhiza Bunge (Danshen) is widely used in modern medicine, but the market faces challenges from counterfeit and mislabeled geographical indication products. To address this, we propose a novel framework combining Two-trace Two-dimensional (2T2D) correlation spectroscopy, hyperspectral imaging (HSI), transfer learning, and an enhanced deep learning model (DeiT-CBAM) to identify both authenticity and origin precisely. Hyperspectral data (873–1720 nm) were collected from six genuine and three adulterated regions and converted into synchronous 2T2D correlation spectroscopy images. We systematically evaluated five preprocessing strategies, three wavelength selection methods, three classical models, and four deep learning models. Models based on 2T2D correlation spectroscopy images consistently outperformed traditional one-dimensional spectral models. Notably, the DeiT-CBAM model, integrated with the successive projections algorithm (SPA), achieved optimal performance using only 79 wavelengths, with 100% accuracy on the training and validation sets and 99.62% on the test set, without the need for additional preprocessing. Model interpretability was further validated through layer-wise class activation mapping (layer-wise CAM). This study demonstrates that the integration of synchronous 2T2D correlation spectroscopy images with the DeiT-CBAM model offers robust discriminative performance, providing a reliable technical solution for geographical origin traceability of food, medicinal herbs, and other species. Full article

This study explores the application of Mid-Infrared (MIR) and Near-Infrared (NIR) spectroscopy combined with various multivariate calibration techniques to detect the presence of cannabis in tobacco samples and tobacco in herbal smoking products. Both MIR and NIR spectra were recorded for self-prepared samples, followed by data exploration using Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), and the calculation of binary classification models with Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares-Discriminant Analysis (PLS-DA). PCA demonstrated a clear differentiation between tobacco samples containing and not containing cannabis. On the other hand, based on PCA, only NIR was able to distinguish herbal smoking products adulterated and not adulterated with tobacco. HCA further clarified these results by revealing distinct clusters within the data. Modelling results indicated that MIR and NIR spectroscopy, particularly when paired with preprocessing techniques like Standard Normal Variate (SNV) and autoscaling, demonstrated high classification accuracy in SIMCA and PLS-DA, achieving correct classification rates of 90% to 100% for external test sets. Comparison of MIR and NIR revealed that NIR spectroscopy resulted in slightly more accurate models for the screening of tobacco samples for cannabis and herbal smoking products for tobacco. The developed approach could be useful for the initial screening of tobacco samples for cannabis, e.g., in a night life setting by law enforcement, but also for inspectors visiting shops selling tobacco and/or herbal smoking products. Full article

Molecular networking (MN) is a novel mass spectrometry data analysis method that has advanced significantly in recent years and has rapidly emerged as a popular technique. By visualizing the connections between structurally similar compounds in mass spectra, MN greatly enhances the efficiency with which harmful substances and bioactive ingredients in cosmetics are screened. In this review, we summarize the principles and main categories of MN technology and systematically synthesize its progress in cosmetic testing applications based on 83 recent studies (2020 to 2025). These applications include screening banned additives, analyzing complex matrix components, and identifying efficacy-related ingredients. We highlight MN’s successful application in detecting prohibited substances, such as synthetic dyes and adulterants, with limits of detection (LOD) as low as 0.1–1 ng/g, even in complex matrices, such as emulsions and colored products. MN-guided isolation has enabled the structural elucidation of over 40 known and novel compounds in the analysis of natural ingredients. We also discuss current challenges, such as limitations in instrument sensitivity, matrix effects, and the lack of cosmetic-specific component databases. Additionally, we outline future prospects for expanding MN’s application scope in cosmetic testing and developing it toward computer-aided intelligence. This review aims to provide valuable references for promoting innovation in cosmetic testing methods and strengthening quality control in the industry. Full article

Some protein food supplements intended for athletes may be adulterated with pharmacologically active substances, including anabolic steroids and prohormones. The addition of these substances is aimed at enabling manufacturers to achieve rapid sales growth by promising quick increases in strength and muscle mass. However, the consumption of these products will lead to a positive result in a routine anti-doping test, along with all of the consequences that will directly affect an athlete’s career and reputation. At the same time, the illicit use of anabolic steroids continues to evolve across numerous sport disciplines. Moreover, vicious cheaters try to cover up their illegal actions by using various pharmacological agents to mask detection in anti-doping tests. This narrative review focuses on two situations—the innocent athlete and the vicious cheater. The athlete involved in inadvertent doping will suffer the consequences of doping, making close collaboration with medical staff extremely important. The analytic strategies described here address anabolic steroid doping detection and cheating using masking agents. This approach, based on biochemical changes, examines how these substances interfere with the testosterone pathway, from synthesis to elimination. Using masking agents alters the steroid profile, and the modifications produced by each agent are the subject of a detailed presentation. For most honest athletes, these findings support the initiation, development, and refinement of strategies for identifying food supplements with added illegal substances. Every athlete must have access to these approaches in order to avoid becoming vulnerable to sports fraud. Full article

Halloumi cheese, a traditional Cypriot dairy product with Protected Designation of Origin (PDO) status, is renowned for its unique texture and high melting point. PDO certification is crucial for Halloumi cheese as it ensures the product’s authenticity, protects its traditional production methods and geographical origin, and safeguards consumers and producers against fraud and mislabeling. However, concerns over adulteration, particularly through the addition of skim milk powder, pose challenges to its authenticity and quality control. This study is the first to analyze Halloumi cheese using Brunauer–Emmett–Teller (BET) analysis and Fourier Transform Infrared (FTIR) spectroscopy, providing a novel approach to assessing its composition and authenticity. Furthermore, it marks the first time Halloumi samples have been examined in the context of PDO certification. Alongside PDO-certified Halloumi, two additional sample sets were produced following PDO specifications for moisture, fat, and salt content, with the controlled incorporation of skim milk powder as an adulterant at concentrations of 1% and 5%. Principal component analysis (PCA) was employed to visualize and interpret the spectral data, revealing promising results. Chemometric analysis showed that the specific surface area from BET measurements and the FTIR spectral subregion between 1650 and 1100 cm⁻¹ were key factors, and they were retained for model construction. These findings could play a crucial role in establishing official food fraud detection methodologies, particularly for the Cyprus and EU markets. While this study serves as an initial investigation, additional samples will be tested in future studies to validate these preliminary results and to assess the potential of applying these techniques in real-world food fraud detection scenarios. Full article

As a high-value product used as food, medicine, or cosmetics, honey is particularly susceptible to adulteration. Therefore, it must be regularly tested at various stages of its life cycle to ensure its quality and authenticity, especially its botanical origin. Sensory quality features play a huge role in creating the quality of products, but also in determining their authenticity. Sensory analysis helps determine the honey’s overall quality based on attributes like color, aroma, taste, and texture. Sensory evaluation of honey can reveal issues like crystallization, off-flavors, or off-odors that might indicate adulteration or spoilage. The aim of our work was therefore sensory quality assessment of 84 honey samples in order to create sensory profiles for the varietal classification of honeys. In order to obtain information on the differences in sensory features and their classification based on the assessment of honey quality descriptors, a discriminant analysis was carried out. Then, an assessment was carried out to check whether the compared varieties differ in terms of the value of the sensory feature parameter assessment. As a result, a statistical tool was constructed (canonical discriminant functions, distinguishing/classifying the varieties of honeys tested). These models will ensure the repeatability of results in the classification of sensory profiles of varietal honeys on the example of Polish honey varieties. The results indicate that the sensory analysis is a good analytical tool to differentiate honey types. The findings of this study can be applied by honey producers, suppliers, and customers to differentiate and determine honey varieties according to their sensorial attributes. Full article

Angelica sinensis radix (AS), the dried root of Angelica sinensis (Oliv.) Diels, is widely used in Chinese medicine and food products. However, after conducting market research, at least a quarter of AS on the market is commonly adulterated by Levisticum officinale W. D. J. Koch (LO), Angelica acutiloba (Sieb. et Zucc.) Kitagawa (AA), and Angelica gigas Nakai (AG), to varying degrees, which significantly affects its clinical efficacy and food safety. Therefore, there is a pressing need to establish safe and reliable methods for identifying illicit adulteration. In this study, the mass spectrometry (MS) information of AS, LO, AA, and AG was collected and converted into the data matrix for [t_R-m/z-I]. The top-n proprietary ions of AS, AG, AA, and LO were output as their molecular “matrix characteristics”. Test samples were also analyzed, transformed into data matrices, and their own matrix characteristics were matched sequentially. For matching credibility (MC) results, a significant difference was found between the MC of the four herbs compared to their own matrix characteristics, as well as between the MC of the four herbs compared with their non-self matrix characteristics. Research results showed that based on matrix characteristics, AS and its adulterations can be identified with a matching credibility (MC) ≥ 78.0%; 3% adulterations can also be identified, and two market-blind samples were identified as exhibiting adulterations. In addition, chemometrics analysis demonstrated that adulteration identification based on matrix characteristics is reasonable and reliable. The matrix characteristics of AS and its adulterants contribute to adulteration analysis. The identification method, based on matrix characteristics, is safe and reliable which is conducive to AS’s quality control and market supervision. Full article

Honey is a natural bee product with confirmed health-promoting properties, the quality and authenticity of which are of key importance from a consumer’s perspective. However, the demand for honey is affected by the problem of its adulteration. Moreover, despite its numerous taste and health benefits, honey may be an undesirable product for some groups of consumers, such as people with allergies or vegans. This work aimed to develop a sensitive molecular test enabling the unambiguous detection of honey adulteration and the identification of its trace amounts in food products. The test was based on the analysis of a fragment of the cytochrome c oxidase gene subunit I using real-time PCR with SYBR^®Green dye and melting curve analysis. The key parameter of the analysis was the melting temperature, which in the case of natural honey was within a narrow range of 74.34–75.38 °C (for its dilutions, 71.10–77.00 °C). The developed method demonstrated high repeatability and sensitivity, enabling the detection of honey presence even at a level of 0.1%. To products labelled as vegan, Tm analysis effectively distinguished samples containing trace amounts of honey from those that were truly vegan. The procedure used is simple, highly repeatable, and effective even in the case of processed products. The developed method can be successfully used to control the quality and authenticity of honey, meeting the requirements of V-Label certification. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Due to their nutritional value and sustainability, edible insect-based foods are gaining popularity in Europe. Their use is regulated by EU legislation, which defines authorised species and sets labelling requirements. Molecular tools are being developed to authenticate such products. In this study, yellow mealworm (Tenebrio molitor) larvae authorised for human consumption were fed wheat flour-based diets containing varying proportions of house cricket (Acheta domesticus) flour for 21 days. This was followed by a 48 h starvation period to assess the persistence of insect DNA in the digestive tract. Two novel, species-specific, single-copy markers were designed: ampd gene for the Acheta domesticus and MyD88 gene for the Tenebrio molitor. These were applied using qPCR and ddPCR. Both methods successfully detected cricket DNA in the guts of starved larvae. Linear regression analysis revealed a strong, statistically significant correlation between the proportion of Acheta domesticus flour in the diet and the normalised relative quantity of DNA. ddPCR proved to be more sensitive than qPCR, particularly in the detection of low DNA levels. These results suggest that the presence of DNA from undeclared insect species in edible insects may be indicative of their diet rather than contamination or adulteration. This highlights the importance of contextual interpretation in food authenticity testing. Full article