Search Results (128)

Tocopherols, major lipid-soluble components of vitamin E, are essential natural products with significant nutritional and pharmacological value. Their structural diversity and uneven distribution across vegetable oils require accurate analytical strategies for compositional profiling, quality control, and authenticity verification, amid concerns over food fraud and regulatory demands. Analytical challenges, such as matrix effects in complex oils and the cost trade-offs of green extraction methods, complicate these processes. This review examines recent advances in tocopherol analysis, focusing on extraction and detection techniques. Green methods like supercritical fluid extraction and deep eutectic solvents offer selectivity and sustainability, though they are costlier than traditional approaches. On the analytical side, hyphenated techniques such as supercritical fluid chromatography-mass spectrometry (SFC-MS) achieve detection limits as low as 0.05 ng/mL, improving sensitivity in complex matrices. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) provides robust analysis, while spectroscopic and electrochemical sensors offer rapid, cost-effective alternatives for high-throughput screening. The integration of chemometric tools and miniaturized systems supports scalable workflows. Looking ahead, the incorporation of Artificial Intelligence (AI) in oil authentication has the potential to enhance the accuracy and efficiency of future analyses. These innovations could improve our understanding of tocopherol compositions in vegetable oils, supporting more reliable assessments of nutritional value and product authenticity. Full article

Milk is a nutritionally rich food and a frequent target of economically motivated adulteration, particularly through substitution with lower-cost milk types. Over the past decade, significant progress has been made in the authentication of milk using advanced proteomic and chemometric approaches, with a focus on the discovery and application of protein and peptide biomarkers for species differentiation and fraud detection. Recent innovations in both top-down and bottom-up proteomics have markedly improved the sensitivity and specificity of detecting key molecular targets, including caseins and whey proteins. Peptide-based methods are especially valuable in processed dairy products due to their thermal stability and resilience to harsh treatment, although their species specificity may be limited when sequences are conserved across related species. Robust chemometric approaches are increasingly integrated with proteomic pipelines to handle high-dimensional datasets and enhance classification performance. Multivariate techniques, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are frequently employed to extract discriminatory features and model adulteration scenarios. Despite these advances, key challenges persist, including the lack of standardized protocols, variability in sample preparation, and the need for broader validation across breeds, geographies, and production systems. Future progress will depend on the convergence of high-resolution proteomics with multi-omics integration, structured data fusion, and machine learning frameworks, enabling scalable, specific, and robust solutions for milk authentication in increasingly complex food systems. Full article

The consumption and farming of insects are gaining global attention as sustainable alternatives to conventional protein sources. Industrial processing of insects into powders or pastes complicates species identification, raising concerns about product authenticity, food safety, and potential fraud. In Western countries, particularly in Europe, the sector is expanding under a stringent regulatory framework, especially regarding rearing substrates, which hinders economic development. This study aimed to assess the species authenticity of commercial insect-based food and feed products and detect the presence of animal-derived DNA from unauthorized substrates. A total of 119 samples (pure insect meals and processed products) were collected from various origins. Species-specific real-time PCR assays targeted Tenebrio molitor, Hermetia illucens, Alphitobius diaperinus, Acheta domesticus, Bombyx mori, and Gryllodes sigillatus, alongside assays for ruminant, porcine, and poultry DNA. High-throughput sequencing (HTS) using metabarcoding confirmed and broadened species detection. Most samples contained the declared species; however, cases of mislabeling, substitution, and cross-contamination were observed. A few insect meals contained animal DNA which could suggest potential use of prohibited substrates. These findings highlight the urgent need for standardized authentication methods and improved transparency to ensure regulatory compliance, consumer trust, and sustainable development of the insect-based sector. Full article

Mass spectrometric (MS) fingerprinting coupled with chemometrics for the detection of plants in plant mixtures is sparsely researched. This paper aims to check its value for herbal adulteration concerning plants with slimming as an indication. Moreover, it is among the first to exploit the full three-dimensional dataset (i.e., time × intensity × mass) obtained with liquid chromatography hyphenated with MS for herbal fingerprinting purposes. The MS parameters were optimized to achieve highly specific fingerprints. Trituration’s (total 55), blanks (total 11) and reference plants were injected in the MS system to generate the dataset. The dataset was complex and humongous, necessitating the application of compression techniques. After compression, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to generate models validated for accuracy using cross-validation and an external test set. Confusion matrices were constructed to provide insight into the modeling predictions. A complimentary evaluation between data obtained using a previously developed Diode Array Detection (DAD) method and the MS data was performed by data fusion techniques and newly generated models. The fused dataset models were comparable to MS models. For ease of application, MS modeling was deemed to be superior. The future market studies would adopt MS modeling as the preferred choice. A proof of concept was carried out on 10 real-life samples obtained from illegal sources. The results indicated the need for stronger monitoring of (illegal) plant food supplements entering the market, especially via the internet. Full article

The quality and authenticity of honey are of crucial importance for food safety and consumer confidence. Given the increasing interest in enriched honey and potential fraud, rapid and non-destructive analytical methods for quality assessment, such as Near-Infrared Spectroscopy (NIRS), are needed. Therefore, the aim of this work was to investigate the applicability of NIR spectroscopy coupled with chemometric methods to assess the quality change in honey from three different countries, after addition of five different aromatic plants (lavender, rosemary, oregano, sage, and white pine oil) in three different concentrations (0.5%, 0.8% and 1%). Measurements of basic physicochemical properties, color, antioxidant activity, and NIR spectra were performed for all samples (pure honey and honey with added aromatic plants). Chemometric models, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression, were applied to analyze spectral data, correlate spectra with physicochemical properties, color and antioxidant activity measurements, and develop classification and prediction models. Spectral changes in the NIR region, as expected, showed the ability to distinguish samples depending on the type and concentration of added aromatic plants. Chemometric models enabled efficient discrimination between pure and enriched honey samples, as well as assessment of the influence of different additives on antioxidant activity and color. The results highlight the potential of NIRS as a rapid, non-destructive and environmentally friendly method for quality monitoring and detection of specific additives in honey, offering technical support for quality control and food safety regulation. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

In 2023, the Italian Bracigliano sweet cherries were awarded the important European label PGI. However, reliable information on the compositional and nutraceutical quality of this product is still relatively undefined and fragmented. Therefore, we investigated fresh Bracigliano PGI cherries (Pallaccia, Spernocchia, and Principe varieties) via HRMAS NMR spectroscopy in the semi-solid state, even though it represents an innovative and powerful technique that is still drastically unexplored. We demonstrated the HRMAS NMR suitability for this fruit type as well as identified the primary metabolome of studied Bracigliano PGI types. Moreover, chemometric techniques (ANOVA, PCA, and PLS-DA) permitted the significant definition of a variety-specific compositional fingerprint. HRMAS data were associated with the assessment of chemical and nutraceutical quality parameters. Importantly, in all studied varieties, a relatively high content of total phenols and antioxidant agents was detected, with Pallaccia cherries resulting as the healthiest ones. The heatmap clusterization revealed interesting correlations between HRMAS-NMR data and important quality parameters. Our results confirm the role of HRMAS in food chemistry and invite the creation of a spectral database of Bracigliano sweet cherries, useful to conduct traceability studies, protect consumers from frauds, and bolster the producers in promoting and certifying the quality of their products. Full article

Salvia officinalis (common or medicinal sage) is a highly valued member of the genus Salvia. Due to its wide range of applications in various fields, including medicine, pharmacy, cosmetics, and food, S. officinalis is a common target for economic fraud. It is imperative to implement rigorous quality control measures to ensure that fraudulent practices are prevented. Such measures should include fast and simple diagnostic tools that can also be used in the field. The objective of the present study was to ascertain the true plant composition of several Salvia-based products. This was accomplished by using PCR-RFLP and LAMP assays. In both procedures, the chloroplast trnL (UAA)-trnF (GAA) intergenic spacer served as the target analyte. The findings demonstrated the reliability and validity of the two DNA-based methods for the unequivocal identification of S. officinalis as the principal component in various sage products, as well as for the detection of irregularities (mainly the presence of adulterating species) in the production and marketing of some of these products. Nonetheless, the LAMP assay offers a more straightforward, rapid, efficient, and cost-effective approach that facilitates the authentication process for sage. The adoption of this method by quality control laboratories could then ensure safety and protect consumers from potential health risks associated with adulterated sage products. Full article

Saffron is a high-cost spice due to the specific conditions for optimal growth and because of being harvested by hand. The massive income from commercializing saffron substituted with other plant parts or low-cost spices makes this spice the main target of fraudsters. Background: Different methods have been developed for detecting saffron adulteration. Most of them are time consuming and complex, and in some types of analysis, the whole untargeted dataset is combined with advanced chemometric tools to differentiate authentic from non-authentic saffron. The official method, combining UV–vis spectroscopy and LC to determine the colour strength and the crocin content, is unable to detect saffron adulterants (safflower, marigold, or turmeric) added at a level lower than 20% (w/w). As a result, innovative approaches based on rapid, high-throughput methods for the identification of adulterated saffron samples are urgently demanded to counteract food frauds. Methods: This paper describes, for the first time, the development of a method combining Direct Analysis in Real Time (DART) with the triple quadrupole MS EVOQ based on the detection of specific MS/MS transitions, promoting a rapid, robust and chromatography-free method capable of monitoring safflower and turmeric adulteration in saffron. Results: The method proved to reach low LODs, allowing the determination of tiny amounts of turmeric and safflower powder in saffron as low as 3% and 5%, respectively, speeding up the whole analytical workflow and enabling us to perform 20 analyses in 10 min. Finally, the greenness of the method was also assessed according to the 0.88 score achieved by submitting it to the greenness calculator AGREE. Conclusions: Given its speed, simplicity, and robustness, this method stands out as a strong candidate for routine implementation in testing laboratories as a rapid screening tool to detect saffron adulteration with safflower or turmeric. Full article

Artificial intelligence (AI) tends to be extensively used to develop reliable, fast, and inexpensive tools for authenticity control. Initially applied for food differentiation as an alternative to statistical methods, AI tools opened a new dimension in adulteration identification based on images. This comprehensive review aims to emphasize the main pillars for applying AI for food authentication: (i) food classification; (ii) detection of subtle adulteration through extraneous ingredient addition/substitution; and (iii) fast recognition tools development based on image processing. As opposed to statistical methods, AI proves to be a valuable tool for quality and authenticity assessment, especially for input data represented by digital images. This review highlights the successful application of AI on data obtained through laborious, highly sensitive analytical methods up to very easy-to-record data by non-experimented personnel (i.e., image acquisition). The enhanced capability of AI can substitute the need for expensive and time-consuming analysis to generate the same conclusion. Full article

Metal oxide semiconductor (MOX) sensors are emerging as a groundbreaking technology due to their remarkable features: high sensitivity, rapid response time, low cost, and potential for miniaturization. Their ability to detect volatile organic compounds (VOCs) in real time makes them ideal tools for applications across various fields, including environmental monitoring, medicine, and the food industry. This paper explores the evolution and growing utilization of MOX sensors, with a particular focus on atmospheric pollution monitoring, non-invasive disease diagnostics through the analysis of volatile compounds emitted by the human body, and food quality assessment. The crucial role of MOX sensors in monitoring the freshness of food and water, detecting chemical and biological contamination, and identifying food fraud is specifically examined. The rapid advancement of this technology offers new opportunities to improve quality of life, food safety, and public health, positioning MOX sensors as a key tool to address future challenges in these vital sectors. Full article

The global demand for animal protein presents significant challenges in the production of nutritionally rich foods, such as milk and meat. Traditionally, the quality of these products is assessed using physicochemical, microbiological, and sensory methods. Although effective, these techniques are constrained by time limiting their widespread application. Furthermore, growing concerns regarding sustainability, animal welfare, and transparency have driven the development of technologies to enhance the rapid and precise assessment of food quality. In this context, omics technologies have transformed the characterization of animal-origin food by providing in-depth molecular understanding of their composition and quality. These tools enable the identification of biomarkers, adulteration detection, optimization of nutritional profiles, and enhancement of authentication and traceability, facilitating the development of functional foods. Despite their potential, several barriers persist, including high implementation cost, the need for specialized infrastructure, and the complexity of integrating multi-omics data. The main aim of this review was to provide information on advances in the application of omics technologies in dairy and meat production systems and studies that use them in food quality, authentication, and sustainability. It also outlines opportunities in areas such as fraud prevention and functional product development to support the transition to safer, healthier, and more transparent food systems. Full article

Turmeric powder has gained widespread popularity due to its culinary and medicinal value and has become a target for economically motivated fraud. The history and exportation of turmeric in Africa were reviewed, and the safety issues of some toxic adulterants were discussed. Priority adulterants were determined from global food safety alerts. A systematic bibliographic search on Scopus, PubMed, Google Scholar, and Web of Science was performed to identify appropriate methods and techniques for authentication and safety. The quality of each study was assessed according to PRISMA guidelines/protocol. African turmeric exportation is on the rise due to recent insights into the suitability of local cultivars, soil and climate for growing high-quality turmeric, with curcumin levels >3%. There are limited data on turmeric adulteration for domestic consumption and export markets. This is important when considering that some turmeric adulterants may serve as risk factors for cancer following exposure. Global alert databases revealed lead chromate as the top hazard identified of all adulterants. Current techniques to detect adulterants are laboratory-based, and while efficient, there is a need for more rapid, field-friendly, non-destructive analytical tools for turmeric fraud/authenticity testing. This enables on-the-spot decision-making to inform rapid alerts. Portable technologies, such as portable X-ray fluorescence, were highlighted as showing potential as a Tier 1 screening tool within a “Food Fortress” systems approach for food safety, combined with validation from mass spectrometry-based Tier 2 testing. Full article

Seafood is a valuable commodity with increasing demand, traded for billions of USD each year. The volatility in supply chains and fluctuating prices contribute to the susceptibility of the seafood market to food fraud. Analytical methods are required to identify seafood in processed foods to ensure food authenticity and compliance with European laws. To address this need, we developed and validated a DNA metabarcoding method for the authentication of crustaceans and cephalopods in processed food samples, as both are prone to food fraud, especially in mixed products. A ~200 bp barcode of the mitochondrial 16S rDNA was selected as the marker for identification and sequenced on Illumina platforms. The DNA metabarcoding method utilizes two primer systems, one for the amplification of crustacean DNA and another for cephalopods. The crustacean primer system comprises two forward and two reverse primers, while the cephalopod primer system includes three forward and one reverse primer. DNA extracts from reference materials, model foods, processed foodstuffs, and DNA extract mixtures were investigated. Even species with a close phylogenetic relationship were successfully identified and differentiated in commercial samples, while single species were detected at amounts as low as 0.003% in model foods. However, false-negative results were obtained for certain species in DNA extract mixtures, which are most likely due to degraded or low-quality DNA and can best be prevented by optimized DNA extraction procedures. Our DNA metabarcoding method demonstrates strong potential as a qualitative screening tool in combination with other in-house DNA metabarcoding methods for food authentication in routine analysis. Full article

Food fraud and adulteration have been persistent issues affecting food supply chains throughout history. They intensify in parallel with the continuous growth in the global food market. Plant-based proteins, which are recognized as sustainable alternatives, face increased food fraud risks because of disparities in the cost of raw materials and complex processing methods. Despite these challenges, most efforts toward preventing food fraud and developing detection technologies have largely focused on animal-based products, with limited attention given to plant-based proteins. This comprehensive review systematically examines the characteristics of major plant protein sources and explores documented instances of food fraud (e.g., ingredient substitution, adulteration with lower-cost alternatives, and mislabeling) within this sector. Furthermore, we discuss key analytical techniques in detecting food fraud, including chromatography, DNA analysis, spectroscopy, and imaging-based approaches, examining their applications and effectiveness. A systematic literature review was conducted using structured search strategies across Scopus, Web of Science, and PubMed, covering publications from 2010 to 2025 and incorporating keywords related to plant-based proteins, food fraud, adulteration, and authentication, thereby ensuring methodological rigor and comprehensive coverage. This study provides a foundational framework to strengthen food fraud prevention strategies and uphold the integrity of the expanding plant-based protein market. Full article