Using a Centroid-based approach for a reliable identification of morels (Morchella spp.): A case study for food authentication.

Food fraud is a problematic yet common phenomenon in the food industry. It impacts numerous sectors, including the market of edible mushrooms. Morel mushrooms are prized worldwide for their culinary and medicinal use. They represent a taxonomically complex group in which food fraud has already been reported. Among the methods to evaluate food fraud, some rely on comparisons of genetic sequences obtained from a sample to existing databases. However, the quality and usefulness of the results are limited by the type of comparison tool and the quality of the database used. The Centroid-based approach is applied by SmartGene in a proprietary artificial intelligence-based method for the generation of automatically curated reference databases that can be further expert curated. In this study, using sequences of the ribosomal internal transcribed spacer (ITS) of the genus Morchella (true morels), we compared this approach to the traditional pairwise alignment tool using two other databases: UNITE and Mycobank (MLST). The Centroid-based approach using an expert-curated database was more performant for the identification of 53 representative ITS sequences corresponding to validated species (83% accuracy, compared to 36% and 47% accuracy for UNITE and MLST, respectively). The Centroid method also revealed an inaccurate taxonomic annotation for sequences of commercial cultivars submitted to public databases. Combined with the web-based commercial software IDNS® available at Smartgene, the Centroid-based approach constitutes a valuable tool to ensure the quality of morel products on the market for actors of the food industry. PRACTICAL APPLICATION: The Centroid-based approach can be used by agri-food actors who need to identify true morels down to the species level without any prior taxonomical knowledge. These include routine laboratories of the food industry, food distributors, and public surveillance agencies. This is a reliable method that requires minimal skills and resources, therefore being particularly adapted for nonspecialists.

Myoglobin as a molecular biomarker for meat authentication and traceability.

The global incidence of economically motivated meat adulteration represents a crucial issue for the food industry. Undeclared addition of cheaper or low-quality species to meat products of high commercial value has become a common practice that needs to be countered with specific measures. In this framework, myoglobin (Mb) is a sarcoplasmic haemoprotein, primarily responsible for meat colour and has been successfully used in meat fraud authentication. Mb is highly soluble in water, easily monitored at 409 nm and species-specific. Knowing that various analytical DNA-based and protein-based methods, as well as spectroscopic techniques have been developed over the years for the detection of meat fraud, the aim of the present review is to take stock of the situation regarding the possible use of Mb as a molecular biomarker for the easy and rapid detection of undeclared species in meat products, avoiding the need of sophisticated or expensive equipment and specialised operators.

A Dual and Rapid RPA-CRISPR/Cas12a Method for Simultaneous Detection of Cattle and Soybean-Derived Adulteration in Goat Milk Powder.

The adulteration of goat milk powder occurs frequently; cattle-derived and soybean-derived ingredients are common adulterants in goat milk powder. However, simultaneously and rapidly detecting cattle-derived and soybean-derived components is still a challenge. An efficient, high-throughput screening method for adulteration detection is needed. In this study, a rapid method was developed to detect the adulteration of common cattle-derived and soybean-derived components simultaneously in goat milk powder by combining the CRISPR/Cas12a system with recombinant polymerase amplification (RPA). A dual DNA extraction method was employed. Primers and crRNA for dual detection were designed and screened, and a series of condition optimizations were carried out in this experiment. The optimized assay rapidly detected cattle-derived and soybean-derived components in 40 min. The detection limits of both cattle-derived and soybean-derived components were 1% (w/w) for the mixed adulteration models. The established method was applied to a blind survey of 55 commercially available goat milk powder products. The results revealed that 36.36% of the samples contained cattle-derived or soybean-derived ingredients, which revealed the noticeable adulteration situation in the goat milk powder market. This study realized a fast flow of dual extraction, dual amplification, and dual detection of cattle-derived and soybean-derived components in goat milk powder for the first time. The method developed can be used for high-throughput and high-efficiency on-site primary screening of goat milk powder adulterants, and provides a technical reference for combating food adulteration.

Gas chromatography ion mobility spectrometry (GC-IMS) dataset of honey samples with different botanical origins.

Gas chromatography ion mobility spectrometry (GC-IMS) is a robust and sensitive benchtop technique commonly used for non-target screening of volatile organic compounds. It has been applied to authenticity analysis by generating characteristic "fingerprints" of food samples, well suited for chemometric data analysis. This dataset contains headspace GC-IMS spectra from 50 monofloral honey samples from three different botanical origins, 18 acacia honeys (Robinia pseudoacacia), 19 canola honeys (Brassica napus) and 18 honeydew honeys (forest flowers). Honeys were sourced from the beekeepers directly or obtained from governmental food inspectors from Baden-Wuerttemberg, Germany. Authenticity was confirmed by pollen analysis in the framework of the official control of foodstuffs. The data was acquired using a setup based on an Agilent 6890N gas chromatograph (Agilent Technologies, Palo Alto, CA) and an OEM Standalone IMS cell from G.A.S Sensorsysteme m. b. H. (Dortmund, Germany). All samples were recorded in duplicates and spectra are presented as raw data in the .mea file format. The dataset is available on Mendeley Data: https://data.mendeley.com/datasets/jxj2r45t2x.

Prevention of food fraud and fraud emulation among companies in the supply chain based on a social Co-governance framework.

This study develops a three-party evolutionary game model among upstream raw material producers, midstream food producers, and downstream distributors in the food supply chain, and investigates food fraud and fraud emulation among companies in the same group based on a food safety social co-governance framework. Moreover, the equilibrium points are divided into four scenarios according to the number of groups of companies committing fraud in the supply chain and whether companies in the same group emulate each other's fraudulent behavior. The stability conditions of these scenarios are also discussed and verified by numerical simulation in MATLAB. The results show that the behavioral strategy choices of different groups of food companies in the supply chain are closely related to the level of social co-governance involving the government, market, and consumers. Government regulation, supervision between companies, and consumer reporting can all change companies' behavioral strategies. Although the level of fraud emulation among companies in the same group does not change their behavioral strategy choice, it affects the time it takes for their behavioral strategy to evolve to a stable state. Moreover, the level of social co-governance directly affects companies' behavioral strategy choices at different emulation levels.

cor1 Gene: A Suitable Marker for Identification of Opium Poppy (Papaver somniferum L.).

This paper discusses the development of rapid, reliable, and accurate polymerase chain reaction (PCR) assays for detecting opium poppy (Papaver somniferum L.) in food. Endpoint, quantitative, and digital PCRs were compared based on the amplification of a newly developed DNA marker targeting the NADPH-dependent codeinone reductase (COR) gene. Designed assays were shown to be highly specific and sensitive in discriminating opium poppy from other plant species, even in heat-treated and food samples. Digital PCR was the most sensitive, with a detection limit of up to 5 copies, i.e., approximately 14 pg of target DNA per reaction. Quantitative and digital PCR further allowed the quantification of opium poppy in up to 1.5 ng and 42 pg (15 copies) of target DNA in a sample, respectively. In addition, two duplex PCRs have been developed for the simultaneous detection of opium poppy DNA and representatives of (i) the Papaveraceae family or (ii) the Plantae kingdom. Finally, all designed assays were successfully applied for analysis of 15 commercial foodstuffs; two were suspected of being adulterated. The study results have an important impact on addressing food fraud and ensuring the safety and authenticity of food products. Beyond food adulteration, the study may also have significant implications for forensics and law enforcement.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review.

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Food Fraud Conceptualization: An Exploratory Study with Portuguese Consumers.

Food fraud refers to deceptive practices conducted for economic gain, and incidents of such fraud are often reported in the media and scientific literature. However, little is known about how European consumers perceive food fraud. To address this gap, a study explored Portuguese consumers' knowledge and perceptions of food fraud using qualitative methods such as free word association and semi-structured interviews. For this research, 340 participants were recruited, providing 911 valid words, classified into categories, major categories, and dimensions. Differences between consumers' previous exposure to food fraud and sociodemographic characteristics were explored. Additionally, other thirty-six participants were selected and interviewed, following a semi-structured guide. Interviews were transcribed, coded, and analyzed using a thematic analysis procedure. The results suggest that Portuguese consumers view food fraud as a morally reprehensible deception and are aware of its causes and impacts. However, not all consumers know the different forms of food fraud or the types of products vulnerable to fraud. Among the most repeated words were "deception", "expiration date", and "falsification". Despite this food fraud awareness, most consumers believed they were not exposed to food fraud and stated that they do not conduct daily practices to reduce exposure to it. Following the chi-square and Mann-Whitney tests, significant differences (p ≤ 0.05) were identified between participants exposed and not exposed to food fraud. The study also found that consumers with higher education and self-reported exposure to food fraud had a better understanding of the issue. This study provides insights for quantitative research on consumer perceptions and beliefs about food fraud to explore further vulnerable food categories and types of food fraud in real-world scenarios.

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges.

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

Parvalbumin Gene: A Valuable Marker for Pike Authentication and Allergen Risk Assessment.

Fish from the pike (Esox) genus are valued in gastronomy for their superior meat quality. However, they can cause allergic reactions in sensitive consumers. This work aimed to fill the gap in the detection of pike allergens using molecular-biological techniques. New, fast, and accurate loop-mediated isothermal amplification (LAMP) and real-time PCR (qPCR) assays were designed to detect pike DNA using the parvalbumin gene as a marker. LAMP was assessed by electrophoresis, SYBR green optical detection, and real-time fluorescence detection. The latter was the most sensitive, detecting as little as 0.78 ng of pike DNA; the qPCR detection limit was 0.1 ng. The LAMP analysis took 20-70 min, which is significantly faster than qPCR. The study provides reliable detection and quantification of the parvalbumin gene in both fresh and processed samples and further highlights the versatility of the use of the parvalbumin gene for the authentication of food products and consumer protection via refined allergen risk assessment that is independent of the type of tissue or food processing method used.

Novel foods, old issues: Metabarcoding revealed mislabeling in insect-based products sold by e-commerce on the EU market.

Insects intended for human consumption are considered Novel Foods according to EU legislation. marketed in form of powders, bars, snacks are increasingly available on the EU market, especially on e-commerce. The commercial form and the way of distribution make IBPs particularly prone to mislabeling. Literature concerning the mislabeling occurrence in IBPs is extremely scarce. In this study, 46 processed IBPs were collected on nine EU e-commerce platforms (e-CO) to be authenticated by metabarcoding. A 200 bp region from 16S rRNA gene was used as molecular target. Sequencing data were processed using DADA2 R package, and sequences were taxonomically assigned through BLAST analysis against GenBank. Procedural blanks and positive controls were included in the analysis, and threshold values were established to filter the final data. The mislabeling rate (i. e. the mismatch between the species declared on the IBP label and the species identified by metabarcoding) was calculated. Overall, a high mislabeling rate (33.3 %) was observed, although this percentage is influenced by the e-CO platform and the insect species, with A. domesticus particularly involved. The use of species not listed in authorized Novel Food (e. g. Gryllus locorojo), and/or the partial replacement of high value species with lower value species was highlighted for the first time in processed IBPs. The presence of insect pests was also detected. Metabarcoding was confirmed as an effective tool for IBPs authentication. Also, outcomes from this study can provide useful data on the main issues involving the EU IBPs' market, that can represent an incentive to reinforce both official controls and FBO's self-controls on these poorly investigated products.

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys.

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few "known" types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a 'dilute-and-shoot' approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a "dilute and shoot" method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.

Illegal synthetic dyes in spices: a Singapore case study.

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.

Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae.

Pericarpium citri reticulatae (PCR) is the dried mature fruit peel of Citrus reticulata Blanco and its cultivated varieties in the Brassicaceae family. It can be used as both food and medicine, and has the effect of relieving cough and phlegm, and promoting digestion. The smell and medicinal properties of PCR are aged over the years; only varieties with aging value can be called "Chenpi". That is to say, the storage year of PCR has a great influence on its quality. As the color and smell of PCR of different storage years are similar, some unscrupulous merchants often use PCRs of low years to pretend to be PCRs of high years, and make huge profits. Therefore, we did this study with the aim of establishing a rapid and nondestructive method to identify the counterfeiting of PCR storage year, so as to protect the legitimate rights and interests of consumers. In this study, a classification model of PCR was established by e-eye, flash GC e-nose, and Fourier transform near-infrared (FT-NIR) combined with machine learning algorithms, which can quickly and accurately distinguish PCRs of different storage years. DFA and PLS-DA models were established by flash GC e-nose to distinguish PCRs of different ages, and 8 odor components were identified, among which (+)-limonene and γ-terpinene were the key components to distinguish PCRs of different ages. In addition, the classification and calibration model of PCRs were established by the combination of FT-NIR and machine learning algorithms. The classification models included SVM, KNN, LSTM, and CNN-LSTM, while the calibration models included PLSR, LSTM, and CNN-LSTM. Among them, the CNN-LSTM model built by internal capsule had significantly better classification and calibration performance than the other models. The accuracy of the classification model was 98.21 %. The R2P of age, (+)-limonene and γ-terpinene was 0.9912, 0.9875 and 0.9891, respectively. These results showed that the combination of flash GC e-nose and FT-NIR combined with deep learning algorithm could quickly and accurately distinguish PCRs of different ages. It also provided an effective and reliable method to monitor the quality of PCR in the market.

Comparison of Multiple NIR Spectrometers for Detecting Low-Concentration Nitrogen-Based Adulteration in Protein Powders.

Protein adulteration is a common fraud in the food industry due to the high price of protein sources and their limited availability. Total nitrogen determination is the standard analytical technique for quality control, which is incapable of distinguishing between protein nitrogen and nitrogen from non-protein sources. Three benchtops and one handheld near-infrared spectrometer (NIRS) with different signal processing techniques (grating, Fourier transform, and MEM-micro-electro-mechanical system) were compared with detect adulteration in protein powders at low concentration levels. Whey, beef, and pea protein powders were mixed with a different combination and concentration of high nitrogen content compounds-namely melamine, urea, taurine, and glycine-resulting in a total of 819 samples. NIRS, combined with chemometric tools and various spectral preprocessing techniques, was used to predict adulterant concentrations, while the limit of detection (LOD) and limit of quantification (LOQ) were also assessed to further evaluate instrument performance. Out of all devices and measurement methods compared, the most accurate predictive models were built based on the dataset acquired with a grating benchtop spectrophotometer, reaching R2P values of 0.96 and proximating the 0.1% LOD for melamine and urea. Results imply the possibility of using NIRS combined with chemometrics as a generalized quality control tool for protein powders.

Database of Food Fraud Records: Summary of Data from 1980 to 2022.

Food fraud prevention and detection remains a challenging problem, despite recent developments in regulatory and auditing requirements. In 2012, the United States Pharmacopeial Convention created a database of food ingredient fraud. The objective of this research was to report on updates made to the database structure and to provide an updated analysis of food fraud records. The restructured database was relational and included four tables: ingredients, adulterants, adulteration records, and references. Four adulteration record types were created to capture the variety of information that can be found in public food fraud reports. Information was searched and extracted from the peer-reviewed scientific literature, media publications, regulatory reports, judicial records, trade association reports, and other public sources covering 1980-present. Over an almost seven-year data entry period, a total of 15,575 records were entered, sourced primarily from the peer-reviewed literature and media reports. The percentage of records that included at least one potentially hazardous adulterant ranged from 34% to 60%, depending on the record type. The ingredients with the highest number of incident and inference records included fluid cow's milk, extra virgin olive oil, honey, beef, and chili powder. The ingredient groups with the highest number of incident and inference records included Dairy Ingredients, Seafood Products, Meat and Poultry Products, Herbs, Spices, and Seasonings, Milk and Cream, and Alcoholic Beverages. This database was created to serve as a standardized source of information about publicly documented occurrences of food fraud and other information relevant to fraud risk to support food fraud vulnerability assessments, mitigation plans, and food safety plans. These data support the contention that food fraud presents a public health risk that should continue to be addressed by food safety systems worldwide.

Is the metabarcoding ripe enough to be applied to the authentication of foodstuff of animal origin? A systematic review.

Food authentication using molecular techniques is of great importance to fight food fraud. Metabarcoding, based on the next-generation sequencing (NGS) technologies, allowing large-scale taxonomic identification of complex samples via massive parallel sequencing of fragments (called DNA barcodes) simultaneously, has become increasingly popular in many scientific fields. A systematic review to answer the question "Is the metabarcoding ripe enough to be applied to the authentication of foodstuff of animal origin?" is presented. The inclusion criteria were focused on the selection of scientific papers (SPs) only applying metabarcoding to foodstuff of animal origin collected on the market. The 23 included SPs were first analyzed with respect to the metabarcoding phases: library preparation (target genes, primer pairs, and fragment length), sequencing (NGS platforms), and final data analysis (bioinformatic pipelines). Given the importance of primer selection, the taxonomic coverage of the used primers was also evaluated. In addition, the SPs were scored based on the use of quality control measures (procedural blanks, positive controls, replicates, curated databases, and thresholds to filter the data). A lack of standardized protocols, especially with respect to the target barcode/s and the universal primer/s, and the infrequent application of the quality control measures, leads to answer that metabarcoding is not ripe enough for authenticating foodstuff of animal origin. However, the observed trend of the SP quality improvement over the years is encouraging. Concluding, a proper protocol standardization would allow a wider use of metabarcoding by both official and private laboratories, enabling this method to become the primary for the authentication of foodstuffs of animal origin.

High-throughput identification of meat ingredients in adulterated foods based on centrifugal integrated purification-CRISPR array.

Rapid, accurate, and sensitive analytical methods for the detection of food fraud are now an urgent requirement in the global food industry to ensure food quality. In response to this demand, a centrifugal integrated purification-CRISPR array for meat adulteration (CIPAM) was established. In detail, CIPAM system combines microneedles for DNA extraction and RAA-CRISPR/Cas12a integrated into a centrifugal microfluidic chip for the detection of meat adulteration. The RAA-CRISPR/Cas12a reaction reagents were pre-embedded into the different reaction chambers on the microfluidic chip to achieve the streamline of operations, markedly simplifying the detection process. The whole reaction was completed within 30 min with a detection limit of 0.1 % (w/w) in pig, chicken, duck, and lamb products. Referring to the results of the standard method, CIPAM system achieved 100 % accuracy. The automatic multiplex detection process implemented in the developed CIPAM system met the needs of food regulatory authorities.

Food fraud detection and reporting by food control officers in Finland.

We studied food fraud detection and the reporting of suspected cases using a questionnaire survey and interviews with Finnish food control officers (FCOs). In total, 95 FCOs responded to the questionnaire, and 17 were interviewed. We found that even though many respondents had either suspected (69.2%) or detected (43.4%) food fraud or other food-related crime during the past five years, 46.8% thought they had no realistic chance of detecting food fraud during inspections. Challenges raised by the FCOs we interviewed included inadequate resources (8/17) and difficulties in inspecting documents or establishing their authenticity (14/17). Moreover, many interviewees highlighted difficulties in assessing whether to inform the police about a suspected case (7/17), and 62% (18/29) of respondents who had detected fraud had not reported it to the police. Training in food fraud detection, increased resources and guidelines on reporting suspected food fraud would improve food fraud detection and harmonize reporting.

Development of a method based on ATR-FTIR spectroscopy to detect maple syrup adulterated with added syrups.

BACKGROUND: Food adulteration is a global concern, whether it takes place intentionally or incidentally. In Canada, maple syrup is susceptible to being adulterated with cheaper syrups such as corn, beet, cane syrups, and many more due to its high price and economic importance. RESULTS: In this study, the use of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy was investigated to detect maple syrups adulterated with 15 different sugar syrups at different concentration levels. The spectra were collected in the range of 4000-650 cm-1 in the absorbance unit. These spectra were used to build six libraries and three models. A method that is capable of performing a qualitative library search using a similarity search, which is based on the first derivative correlation search algorithm, was developed. This method was further evaluated and proved to be able to capture adulterated and reject non-adulterated maple syrups, belonging to the color grades golden and amber maple syrups, with an accuracy of 93.9% and 92.3%, respectively. However, for the maple syrup belonging to the dark color grade, this method demonstrated low specificity of 33.3%, and for this reason it was only able to adequately detect adulterated samples from the non-adulterated ones with an accuracy of 81.4%. CONCLUSION: This simple and rapid method has strong potential for implementation in different stages of the maple syrup supply chain for early adulteration detection, particularly for golden and amber samples. Further evaluation and improvements are required for the dark color grade. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.