The need for a sensitive molecular method to detect specific species of insect contaminants in food products remains a significant challenge in the food industry. This study evaluated the detection limit of a multiplex end-point PCR assay for detecting insects in food. The assay amplifies two fragments of the cytochrome oxidase subunit I gene (COI-Fa and COI-Fb) and one fragment of the protein-coding wingless (wg) gene found in insects. Five insect species, comprising three vectors of foodborne pathogens (the housefly, Musca domestica, the American cockroach, Periplaneta americana, and the pharaoh ant, Monomorium pharaonis) and two storage insect pests (the red flour beetle, Tribolium castaneum and the Indian meal moth, Plodia interpunctella), were spiked separately and in combination at levels of 1, 0.1, 0.01, and 0.001% in whole wheat flour. At spike levels greater than 0.01%, amplicon bands of expected sizes were seen in 100% of samples containing fragments from distinct insect species. At least 25% of spiked samples at the lowest spike level had amplicon bands, except for samples spiked with M. domestica. Results showed an 18.9% probability (with 11.3% and 30% lower and upper confidence limits, respectively) of detecting insect fragments at the lowest spike level (0.001%, corresponding to 3-22 fragments), which is far below the FDA\'s regulatory level of less than 75 fragments per 50 g of wheat flour. The intensity of amplicon bands in the gel images was higher at higher spike levels. However, this method is not quantitative enough to extrapolate the intensity of the amplicon bands to the number of insect fragments present in a sample. This multiplex assay was also evaluated in a variety of market food samples derived from plants and animals, showing its potential use in various food types. Overall, the sensitivity and specificity of this molecular approach suggest that it could be used in the future as a screening tool for detecting insect contaminants in food.

Butter is among the most popular and commercially valuable dairy products. Its high commercial value makes it a major target for adulteration, which aims to reduce production costs by using lower-quality fats and oils from other sources. The annual global market is around USD 30 billion (2023), expected to reach USD 36 billion in 2028, which also justifies the enormous interest in adulteration. In this work, a confirmed case of butter adulteration was studied by Nuclear Magnetic Resonance (NMR) and Stable Carbon Isotopic Ratio Analysis (SCIRA) techniques, employed to detect the inclusion in butter production of vegetable oils, such as soybean and palm oils. A total of 21 samples seized by the Brazilian Federal Police were analysed by NMR and SCIR, and compared to original butter obtained from commercial sources. The composition of all the seized samples was a mixture of butter (dairy fat of animal origin) with fat of vegetable origin (soybean and palm oil) and did not contain milk as a major component. While NMR was an unequivocal choice to discriminate the chemical composition of food samples, identifying the short-chain saturated fatty acids present in milk fat, including the butyryl alkyl chain, SCIRA was able to discriminate the origin of fat present in the butter samples as C3 sources, such as palm vegetable oils.

Some synthetic dyes are fraudulently added into spices to appeal visually to consumers. Food regulations in several countries, including the United States, Australia, Japan and the European Union, strictly prohibit the use of unauthorised synthetic dyes in food. Nevertheless, illegal practices persist, where spices contaminated with potentially carcinogenic dyes have been documented, posing potential health risks to consumers. In the present study, 14 synthetic dyes were investigated through liquid chromatography/tandem mass spectrometry in 252 commercially available spices in the Singapore market. In 18 out of these (7.1%) at least 1 illegal dye was detected at concentrations ranging from 0.010 to 114 mg/kg. Besides potential health risks, presence of these adulterants also reflects the economic motivations behind their fraudulent use. Findings in the present study further emphasise the need for increased public awareness, stricter enforcement, and continuous monitoring of illegal synthetic dyes in spices to ensure Singapore\'s food safety.

The deliberate pork adulteration with lymph nodes is a common adulteration phenomenon, and it poses a serious threat to public health and food safety. An untargeted metabolomics and lipidomics approach based on ultrahigh performance liquid chromatography coupled with linear ion trap quadrupole-Orbitrap high resolution mass spectrometry (MS) was used to distinguish lymph nodes from minced pork. The principal component analysis and orthogonal projection to latent structures discriminant analysis models were established with the good of fitness and predictivity. The results showed that there were significant differences in metabolites and lipids between lymph nodes and pork. A total of 16 significantly differentiated metabolites were identified, of which 1-palmitoylglycerophosphocholine, 12,13-dihydroxy-9-octadecenoic acid, and prostaglandin E2 (PGE2) were positively correlated with lymph node content and were identified as potential markers of lymph nodes. These three markers were combined to create a binary logistic regression model, and a combined-factor exceeding 0.75 was ultimately identified as a marker for pork adulteration with lymph nodes. The desorption electrospray ionization-MS images showed that PGE2 had a higher relative abundance in the lymph node region than in adjacent non-lymph node regions, indicating that PGE2 was a marker that contributed significantly for identifying lymph nodes adulteration into pork. Our results provide a theoretical basis for identifying lymph node adulteration, which will contribute to combating fraud in the meat industry.

Classifying big data in hyperspectral imaging (HSI) can be challenging when minor (low-concentrated) compounds are present in actual samples, as for chemical additives and adulterants in food matrix. Herein, we propose a new strategy to classify HSI data for the identification of adulterants in food material for the first time. This strategy is based on the selection of essential spectral pixels of full HSI data followed by the feature space construction using uniform manifold approximation and projection as well as the data clustering utilizing hierarchical clustering analysis on the reduced data (named ESPs-UMAP-HCA). We apply our approach to analyze two real NIR datasets and four new Raman datasets. Compared with non-ESPs UMAP-HCA and t-distributed stochastic neighbor embedding combined with ESPs and HCA (ESPs-t-SNE-HCA), the developed strategy provides well-separated clusters for major and minor compounds in food matrix. Finally, the adulterants as minor compounds are accurately identified, which is confirmed by the fact that the extracted spectra of them perfectly match with their pure spectra. In addition, their locations are found in the contribution map even though they are present in a few pixels. What\'s more, the proposed strategy does not need any a priori knowledge of the data structure and the class memberships and therefore reduced the studied difficulty and confirmation bias in the analysis of big HSI datasets. Overall, the proposed ESPs-UMAP-HCA method could be a potential approach for food adulteration detection.

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.

Every living thing requires food to survive. Clean, fresh, and healthy foods are important to human health. Today, food is affected by various counterfeits. Adulteration of food is the intentional deterioration of the quality of food offered for sale by either the addition or substitution of an inferior substance or by the omission of a valuable ingredient. Economically motivated adulteration is the intentional adulteration of food for financial gain, and has enormous public health implications, making it an important issue in food science. Almost every food, including milk and dairy products, fats and oils, fruits and vegetables, grain foods, coffee, tea, honey, etc., is susceptible to adulteration. It is difficult to find food that is free from adulteration. Consumption of adulterated food contributes to numerous diseases in society, ranging from mild to life threatening. Therefore, detection of adulteration in food is essential to ensure the safety of the food we consume. To provide consumers with food that is free of adulterants, various detection methods such as physical, chemical, biochemical, and molecular techniques are used to identify adulterants in food. This review aims to provide up-to-date information on food adulteration, its impact on health, and the analytical techniques used to detect adulteration in food.

In recent years, consumer interest in meat authenticity has increased. Fraudulent claims are most likely to be regarding meat origin, meat substitution, meat processing treatment, and non-meat ingredient additions. This study focuses on the substitution of meat species in processed kebab-like food sales in Poland. The growing popularity of kebab-like foods and the limited number of official inspections of this type of food make this topic interesting. In this study, the results reveal that 60% of the foods analyzed contain an undeclared ingredient or the substitution of an expensive ingredient with a cheaper option.

Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.

With the current advancement in mass spectrometry (MS)-based lipidomics, the knowledge of lipidomes and their diverse roles has greatly increased, enabling a deeper understanding of the action of bioactive lipid molecules in plant- and animal-based foods. This review provides in-depth information on the practical use of MS techniques in lipidomics, including lipid extraction, adduct formation, MS analysis, data processing, statistical analysis, and bioinformatics. Moreover, this contribution demonstrates the effectiveness of MS-based lipidomics for identifying and quantifying diverse lipid species, especially triacylglycerols and phospholipids, in foods. Further, it summarizes the wide applications of MS-based lipidomics in food science, such as for assessing food processing methods, detecting food adulteration, and measuring lipid oxidation in foods. Thus, MS-based lipidomics may be a useful method for identifying the action of individual lipid species in foods.