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.

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.

Features in visible and Fourier transform infrared spectra confronting aspects of meat quality and fraud

Rapid assessment of microbiological quality (i.e., Total Aerobic Counts, TAC) and authentication (i.e., fresh vs frozen/thawed) of meat was investigated using spectroscopic-based methods. Data were collected throughout storage experiments from different conditions. In total 526 spectra (Fourier transform infrared, FTIR) and 534 multispectral images (MSI) were acquired. Partial Least Squares (PLS) was applied to select/transform the variables. In the case of FTIR data 30 % of the initial features were used, while for MSI-based models all features were employed. Subsequently, Support Vector Machines (SVM) regression/classification models were developed and evaluated. The performance of the models was evaluated based on the external validation set. In both cases MSI-based models (Root Mean Square Error, RMSE: 0.48-1.08, Accuracy: 91-97 %) were slightly better compared to FTIR (RMSE: 0.83-1.31, Accuracy: 88-94 %). The most informative features of FTIR for the case of quality were mainly in 900-1700 cm-1, while for fraud the features were more dispersed.

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.

Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification

Food fraud, along with many challenges to the integrity and sustainability, threatens the prosperity of businesses and society as a whole. Tea is the second most commonly consumed non-alcoholic beverage globally. Challenges to tea authenticity require the development of highly efficient and rapid solutions to improve supply chain transparency. This study has produced an innovative workflow for black tea geographical indications (GI) discrimination based on non-targeted spectroscopic fingerprinting techniques. A total of 360 samples originating from nine GI regions worldwide were analysed by Fourier Transform Infrared (FTIR) and Near Infrared spectroscopy. Machine learning algorithms (k-nearest neighbours and support vector machine models) applied to the test data greatly improved the GI identification achieving 100% accuracy using FTIR. This workflow will provide a low-cost and user-friendly solution for on-site and real-time determination of black tea geographical origin along supply chains.

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.

Nanomolar electrochemical detection of feed additive ractopamine in meat samples using spinel zinc ferrite decorated 3-dimensional graphene nanosheets to combat food fraud in livestock industries

Accurate detection of feed additive is significant for food safety monitoring, warding off its illegal use in livestock production, safeguarding public health, and regulatory compliance. Hence, this paper presents a cost-effective and ultrasensitive electrochemical sensor for detecting commonly used animal feed additive, ractopamine to combat food frauds in meat samples. The sensor was created by embedding spinel zinc ferrite nanospheres (ZnFe2O4) on three-dimensional graphene (3DG) nanosheets using sonochemical method. ZnFe2O4 nanospheres were synthesized using solvothermal approach, and 3DG was prepared using hydrothermal method. Various characterization techniques were used for inspecting structural and morphological properties of materials, including XRD, SEM, TEM, elemental mapping, Raman spectroscopy, XPS, and EIS. The hybrid nanocomposite modified electrode showed excellent electrochemical performance, with high sensitivity of 16.97 μA μM-1 cm-2 and detection limit of 1 nM. The practicality of sensor was demonstrated by performing real sample analysis in pork, beef, and sausage which gave adequate recovery.

A method to assess industrial paraffin contamination levels in rice and its transferability analysis based on transfer component analysis

Accurate assessment of industrial paraffin contamination levels (IPCLs) in rice is critical for food safety. However, time-consuming and labor-intensive experiments to produce labels for targeted adulterated rice have hindered the development of IPCL estimation methods. In this paper, a transfer learning method (TCA-LSSVR) has been developed. The algorithm integrates transfer component analysis (TCA) with domain adaptive capabilities to produce accurate estimates. Rice from 7 different regions and 3 industrial paraffins were used to generate 4,680 samples from 9 datasets for benchmarking. The test results showed that the established algorithm achieved good estimation performance in various modelling strategies, and only 20 % of off-site samples were needed to supplement the source dataset, the average determination coefficient R2 reached 0.7045, the average RMSE reached 0.140 %, and the average RPD reached 2.023. This work highlights the prospect of rapidly developing a new generation of adulteration detection algorithms using only previous trial data.

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.

Rapid identification of counterfeited beef using deep learning-aided spectroscopy: Detecting colourant and curing agent adulteration

The adulteration of meat products using colourants and curing agents has heightened concerns over food safety, thereby necessitating the development of advanced detection methods. This study introduces a deep-learning-based spectroscopic method for swiftly identifying counterfeit beef altered to appear fresh. The experiment involved 60 beef samples, half of which were artificially adulterated using a colouring solution. Despite meticulous analysis of the beef's colour attributes, no significant differences were observed between the fresh and adulterated samples. However, our method, utilising a 344-1040 nm spectral range, achieved a classification accuracy of 98.84%. To enhance practicality, we employed gradient-weighted class activation mapping and identified the 580-600 nm range as particularly influential for classification. Remarkably, even when we narrowed the input to the model to this spectral range, a high level of classification accuracy was maintained. To further validate the model's robustness and generalisability, we allocated 70 beef samples to an external validation set. Comparative performance analysis revealed that our model outperformed traditional machine learning algorithms, such as SVM and logistic regression, by 9.3% and 28.4%, respectively. Overall, this study offers invaluable insights for detecting counterfeited beef, thereby contributing to the preservation of meat product quality and integrity within the food industry.

Detection of adulteration in Eragrostis tef (Zucc.) Trotter flour using EDXRF and ComDim-MLR data fusion

The teff cereal gained worldwide attention because it is gluten-free and rich in iron; thus, its flour is subject to fraud. This study evaluated the ability of Energy Dispersive X-Ray Fluorescence Spectroscopy (EDXRF) to identify teff flours adulterated with rice, whole wheat, oat, and rye flours. The adulteration followed a {5,4} simplex-lattice design. After smoothing and pretreatments, 15 kV and 50 kV spectra were fused by Common Dimension Analysis (ComDim). Multiple Linear Regression (MLR) models using EDXRF-ComDim scores and percentage of teff were adjusted. The best model presented four common dimensions (CD), r2prediction = 0.8534, low RMSEP (0.0564), and absence of overfitting. The obtained model was robust to quantify adulteration in teff flour even with the differences in the intensity of EDXRF spectra of different crops. Therefore, EDXRF, in tandem with ComDim data fusion, was an efficient tool for the adulteration control of teff flours.

Use of elemental profiles determined by energy-dispersive X-ray fluorescence and multivariate analyses to detect adulteration in Ceylon cinnamon

The price of Cinnamomum verum (Ceylon cinnamon) is around twice as high as that of the other cinnamon varieties commonly grouped under the name cassia cinnamon, making the former spice an attractive target for fraudsters. This work demonstrates that elemental profiles obtained by energy-dispersive X-ray fluorescence in combination with multivariate analyses can be used as a screening method to detect Ceylon cinnamon adulteration. Thirty-six elements were analysed in 52 commercially available cinnamon samples, 29 Ceylon, 8 cassia, and 15 for which no indication about variety was provided. Fifty-eight percent of the samples were either adulterated or did not meet international quality criteria. Four of the ground cinnamon samples labelled as Ceylon cinnamon were found to be pure cassia or a mixture with a high cassia content, and 26 samples were suspected of other types of adulteration including replacement of bark with other parts of the cinnamon tree. Headspace gas chromatography-mass spectrometry and ash determination by thermogravimetric analysis confirmed the conclusions reached by elemental analysis. Only one sample labelled as Ceylon cinnamon and that according to its volatile composition was cassia cinnamon was not flagged as suspicious by elemental analysis.

A multi-analyte screening method for the rapid detection of illicit adulterants in dietary supplements using a portable SERS analyzer

The popularity and number of dietary supplements on the health market have experienced an unprecedented boost in recent years. Simultaneously, their increased use has been accompanied by an increase in acute intoxication cases linked to the adulteration of these products with illicit and undeclared substances. In this study, a SERS-based screening methodology was developed to rapidly detect illegally added pharmaceutically active substances to dietary supplements. A portable analyzer and silver printed-SERS substrates were used to enhance the signal, requiring less than 20 min of sample preparation prior to the analysis. The method was successful in the qualitative identification of eleven out of twenty-three illicit adulterants in the dietary supplements; it could detect the target compounds at realistic adulteration levels (0.1-5.0% w/w), demonstrating the potential of SERS-based methodologies for forensic rapid screening applications. The developed method is quick, easy to use, requires no skilled technicians and little sample preparation, and allows in-situ analyses. For these reasons, it is suitable for quick screening to be performed by inspectors at customs. Moreover, the low specificity of spectroscopic methods, to which SERS belongs, would benefit the detection of newly synthesized analogues of the target adulterants, which would otherwise be more difficult using common mass spectrometry methods in absence of reference standards.

Variety Differentiation: Development of a CRISPR DETECTR Method for the Detection of Single Nucleotide Polymorphisms (SNPs) in Cacao (Theobroma cacao) and Almonds (Prunus dulcis)

To prevent food fraud, products can be monitored by various chemical-analytical techniques. In this study, we present a CRISPR-Cpf1 DETECTR-based assay for the differentiation of plant ingredients in sweet confectionary like fine and bulk-cocoa, or bitter and sweet almonds. To enable rapid in-field analysis, the trans-cleavage activity of the Cpf1 enzyme was used to develop a DETECTR (DNA endonuclease-targeted CRISPR trans reporter) assay for simple, highly specific fluorometric detection of single nucleotide polymorphisms (SNPs). The endonuclease Cpf1 requires the protospacer adjacent motif (PAM) 5'-TTTV-3' for activation, but the recognition sequence is freely programmable. The SNPs were selected to alter the Cpf1 specific PAM sequence. As a result, sequences that do not carry the canonical PAM sequence are not detected and thus not cut. The optimized system was used for both raw material and processed products such as cocoa masses or marzipan with a limit of detection of 3 ng template DNA. In addition, we were able to implement the system in the context of an LFA (lateral flow assay) to serve as a basis for the development of rapid test systems.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12161-023-02500-w.

Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0

Food Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. Growing interest in food traceability has led to the development of a wide range of chemical, biomolecular, isotopic, chromatographic, and spectroscopic methods with varied performance and success rates. This review will give an update on the application of Traceability 4.0 in the fruits and vegetables sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. The results show that the Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. However, due to their high implementation costs and lack of adaptability to industrial environments, most of these advanced technologies have not yet gone beyond the laboratory scale. Therefore, further research is anticipated to overcome current limitations for large-scale applications.

Evaluation of on-site testing methods with a novel 3-in-1 miniaturized spectroscopic device for cinnamon screening

"True cinnamon" is often fraudulently replaced by other varieties for economic reasons. In the powdered form, it is not possible to distinguish the varieties visually, but they differ in their sensory profile, in particular in the aromatic compound coumarin content which has also been deemed hepatotoxic in animal models. Molecular and analytical techniques exist which can be used for authentication but are expensive, time-consuming, and destructive. As an alternative, we tested three different miniaturized spectroscopic techniques namely, ultraviolet-visible (UV-Vis), near-infrared (NIR) and fluorescence (FLUO) to authenticate cinnamon samples. Out of the three, UV-Vis and NIR were superior to FLUO. The separation with UV-Vis and FLUO could be visually identified after pre-processing the spectral data and subsequently submitting it to principal component analysis (PCA). When chemometrics were applied a correct classification rate by variety of 89%, 90% and 89% for UV-Vis, NIR, and fluorescence spectroscopy, respectively, was observed. The usage of miniaturized spectrophotometers combined with PCA and classification algorithms was found promising to authenticate cinnamon.

Application of blockchain technology in shaping the future of food industry based on transparency and consumer trust

Food Industries, at this moment, are moving towards a new phase, and this phase will be governed by consumers and not by the industry leaders. The report shows that claims on sustainability, health, wellness, and transparency would govern the future trends in the food industry. Currently, there are several cases of misleading and false claims which hamper consumer trust. So, to uphold consumer trust, authentication of claims through transparency in the food supply chain is required, and blockchain technology can bring transparency at relatively low transaction costs. Once in a blockchain network, data is very difficult to manipulate, with no single point of authority to mess and collapse the system. Though we see mostly the financial systems using blockchain's decentralized functionality, there is a growing trend of innovative applications being built in the supply chain area for contracts and operations. With effort in the right direction and over time, blockchain will recast how operations and processes are done across the industry, including public sectors. The paper reviews the opportunity for the blockchain in enabling food industries for future-readiness, empowering the consumers in verifying the product claims and thus prevent themselves from food fraud. In doing so, the paper considers the future trends in the food industry, identifies current food fraud cases, and outlines the various applications in the agri-food chain and challenges associated with it.

Graphical abstract

Rapid and non-destructive cinnamon authentication by NIR-hyperspectral imaging and classification chemometrics tools

Cinnamon is a valuable aromatic spice widely used in pharmaceutical and food industry. Commonly, two-cinnamon species are available in the market, Cinnamomum verum (true cinnamon), cropped only in Sri Lanka, and Cinnamomum cassia (false cinnamon), cropped in different geographical origins. Thus, this work aimed to develop classification models based on NIR-hyperspectral imaging (NIR-HSI) coupled to chemometrics to classify C. verum and C. cassia sticks. First, principal component analysis (PCA) was applied to explore hyperspectral images. Scores surface displayed the high similarity between species supported by comparable macronutrient concentration. PC3 allowed better class differentiation compared to PC1 and PC2, with loadings exhibiting peaks related to phenolics/aromatics compounds, such as coumarin (C. cassia) or catechin (C. verum). Partial least square discriminant analysis (PLS-DA) and Support vector machine (SVM) reached similar performance to classify samples according to origin, with error = 3.3 % and accuracy = 96.7 %. A permutation test with p < 0.05 validated PLS-DA predictions have real spectral data dependency, and they are not result of chance. Pixel-wise (approach A) and sample-wise (approach B, C and D) classification maps reached a correct classification rate (CCR) of 98.3 % for C. verum and 100 % for C. cassia. NIR-HSI supported by classification chemometrics tools can be used as reliable analytical method for cinnamon authentication.

A next-generation sequencing approach for the detection of mixed species in canned tuna

Tuna cans are relevant seafood products for which mixtures of different tuna species are not allowed according to European regulations. In order to support the prevention of food fraud and mislabelling, a next-generation sequencing methodology based on mitochondrial cytochrome b and control region markers has been tested. Analyses of defined mixtures of DNA, fresh tissue and canned tissue revealed a qualitative and, to some extent, semiquantitative identification of tuna species. While the choice of the bioinformatic pipeline had no influence in the results (p = 0.71), quantitative differences occurred depending on the treatment of the sample, marker, species, and mixture (p < 0.01). The results revealed that matrix-specific calibrators or normalization models should also be used in NGS. The method represents an important step towards a semiquantitative method for routine control of this analytically challenging food matrix. Tests of commercial samples uncovered mixed species in some cans, being not in compliance with EU regulations.

A Fast and Simple DNA Mini-barcoding and RPA Assay Coupled with Lateral Flow Assay for Fresh and Canned Mackerel Authentication

Nowadays, food authentication is more and more required given its relevance in terms of quality and safety. The seafood market is heavily affected by mislabelling and fraudulent substitutions/adulterations, especially for processed food products such as canned food items, due to the loss of morphological features. This study aims to develop new assays based on DNA to identify fresh mackerel (Scomber spp.) and commercial products. A new primer pair was de novo designed on the 5S rRNA gene and non-transcribed spacer (NTS), identifying a DNA mini-barcoding region suitable for species identification of processed commercial products. Moreover, to offer a fast and low-cost analysis, a new assay based on recombinase polymerase amplification (RPA) was developed for the identification of fresh 'Sgombro' (Scomber scombrus) and 'Lanzardo o Occhione' (Scomber japonicus and Scomber colias), coupled with the lateral flow visualisation for the most expensive species (Scomber scombrus) identification. This innovative portable assay has great potential for supply chain traceability in the seafood market.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12161-022-02429-6.

Essential terminology and considerations for validation of non-targeted methods

Through their suggestive name, non-targeted methods (NTMs) do not aim at a predefined "needle in the haystack." Instead, they exploit all the constituents of the haystack. This new type of analytical method is increasingly finding applications in food and feed testing. However, the concepts, terms, and considerations related to this burgeoning field of analytical testing need to be propagated for the benefit of those associated with academic research, commercial development, or official control. This paper addresses frequently asked questions regarding terminology in connection with NTMs. The widespread development and adoption of these methods also necessitate the need to develop innovative approaches for NTM validation, i.e., evaluating the performance characteristics of a method to determine if it is fit-for-purpose. This work aims to provide a roadmap for approaching NTM validation. In doing so, the paper deliberates on the different considerations that influence the approach to validation and provides suggestions therefor.

Variable selection in the chemometric treatment of food data: A tutorial review

Food analysis covers aspects of quality and detection of possible frauds to ensure the integrity of the food. The arsenal of analytical instruments available for food analysis is broad and allows the generation of a large volume of information per sample. But this instrumental information may not yet give the desired answer; it must be processed to provide a final answer for decision making. The possibility of discarding non-informative and/or redundant signals can lead to models of better accuracy, robustness, and chemical interpretability, in line with the principle of parsimony. Thus, in this tutorial review, we cover aspects of variable selection in food analysis, including definitions, theoretical aspects of variable selection, and case studies showing the advantages of variable selection-based models concerning the use of a wide range of non-informative and redundant instrumental information in the analysis of food matrices.

Authentication of organically grown vegetables by the application of ambient mass spectrometry and inductively coupled plasma (ICP) mass spectrometry; The leek case study

Health conscious and environmentally aware consumers are purchasing more organically produced foods. They prefer organic fruits and leafy vegetables as these are much less likely to have been exposed to contaminants such as pesticides. The detection of fraudulent activity in this area is difficult to undertake, because many chemical plant protection treatments degrade very quickly or can be washed off to remove evidence of their existence. It was found that when combining DART-MS with a compact, inexpensive and robust single quadrupole mass spectrometer, it was possible to differentiate organic from conventional leeks with 93.8% to 100% accuracy. ICP-MS results showed similar performance, with an ability to differentiate conventional from organic leeks with 92.5% to 98.1% accuracy. This study has paved the way for the certification of vegetables as being organically produced. The next step is to create data libraries to support the roll out of the methodologies described.

Detection of fraud in ginger powder using an automatic sorting system based on image processing technique and deep learning

Ginger is a well-known product in the food and pharmaceutical industries. Ginger is one of the spices which are adulterated for economic gain. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have made them a good choice to be mixed with ginger in powder form and sold in the market. Demand for non-destructive methods of measuring food quality, such as machine vision and the growing need for food and spices, were the main motives to conduct this study. This study classified ginger powder images to detect fraud by improving convolutional neural networks (CNN) through a gated pooling function. The main approach to improving CNN is to use a pooling function that combines average pooling and max pooling. The Batch normalization (BN) technique is used in CNN to improve classification results. We show empirically that the combining operation used increases the accuracy of ginger powder classification compared to the baseline pooling method. For this purpose, 3360 image samples of ginger powder were prepared in 7 categories (pure ginger powder, chickpea powder, 10%, 20%, 30%, 40%, and 50% fraud in ginger powder). Moreover, MLP, Fuzzy, SVM, GBT, and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that using batch normalization based on gated pooling, the proposed CNN was able to grade the images of ginger powder with 99.70% accuracy compared to other classifiers. Therefore, it can be said that the CNN method and image processing technique effectively increase marketability, prevent ginger powder fraud, and promote traditional methods of ginger powder fraud detection.

What's for dinner this time?: DNA authentication of "wild mushrooms" in food products sold in the USA

Mushrooms have been consumed by humans for thousands of years, and while some have gastronomic and nutritional value, it has long been recognized that only select species of mushrooms are suitable for consumption. Adverse health effects of consuming poisonous mushrooms range from mild illness to death. Many valuable edible mushrooms are either impractical or unable to be grown commercially, requiring them to be harvested from the wild. In the U.S., products containing these wild-collected mushrooms are often sold with the nonspecific and undefined label “wild mushrooms,” although in some cases particular species are listed in the ingredients. However, the ambiguity of the definition of “wild mushrooms” in foods makes it impossible to know which species are involved or whether they are truly wild-collected or cultivated varieties. As a consequence, any individual adverse reactions to consuming the mushrooms in these products cannot be traced to the source due to the minimal regulations around the harvest and sale of wild mushrooms. For this study, we set out to shed light on what species of fungi are being sold as “wild mushrooms” using DNA metabarcoding to identify fungal contents of various food products acquired from locally sourced grocers and a large online retail site. Twenty-eight species of mushroom were identified across 16 food products, ranging from commonly cultivated species to wild species not represented in global DNA databases. Our results demonstrate that “wild mushroom” ingredients often consist entirely or in part of cultivated species such as the ubiquitous white and brown “button” mushrooms and portabella (Agaricus bisporus), oyster (Pleurotus spp.) and shiitake (Lentinula edodes). In other cases truly wild mushrooms were detected but they were not always consistent with the species on the label. More alarmingly, a few products with large distribution potential contained species whose edibility is at best dubious, and at worst potentially toxic.

Investigating the feasibility of ICP-MS/MS for differentiating NIST salmon reference materials through determination of Sr and S isotope ratios

Inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) was investigated for possible use in food fraud studies through the measurement of strontium and sulfur isotope ratios. Oxygen mass shift mode was applied to shift ⁸⁷Sr/⁸⁶Sr and ³⁴S/³²S isotope ratios to their respective oxides, ⁸⁷Sr¹⁶O⁺/⁸⁶Sr¹⁶O⁺ and ³⁴S¹⁶O⁺/³²S¹⁶O⁺, effecting a gas-phase chemical separation of the elements from Rb and Kr (for Sr) and molecular N and O species, along with P- and S-hydrides (for S). A total least squares regression approach was employed to generate the isotope ratio data from time-resolved analyses, and method uncertainties and accuracies were determined. The utility of the approach was shown by using the Sr and S isotope ratios together to differentiate between NIST RM 8256 Wild-Caught Coho Salmon and NIST RM 8257 Aquacultured Coho Salmon. These materials are currently under development at NIST as certified food fraud standards and method evaluation materials for comprehensive chemical analysis.

Smartphone-based competitive immunoassay for quantitative on-site detection of meat adulteration

Rapid, sensitive, and portable analytical methods for on-site inspection of food fraud are now an urgent requirement to ensure food quality and satisfy the ethnic considerations of consumers. Hence, for the first time, a colorimetric smartphone-based immunoassay was developed for the on-site detection of pork adulteration in meat. In detail, the immunoassay was based on a competitive strategy in which immobilized standard porcine IgG competed with the target porcine IgG extracted in a single step from meat samples. The parameters involved in each step of the immunoassay conception and the digital colorimetric detection were carefully investigated and optimized. Using polystyrene microplates as ready-to-use stable and portable immunoplatforms, TMB as chromogenic substrate, smartphone as signal readout, and Image J software for image processing; the developed immunoassay was able to detect as low as 0.01% of pork in meat mixtures in a total assay time of 30 min. The selectivity of the immunoassay was evaluated for different meat species, and it was shown to selectively respond only to pork. Furthermore, excellent stability of the prepared immunological platform was demonstrated under extreme temperature conditions (50 °C), which confirms its high portability potential for in situ quantification of pork, while being relatively cost effective and non-laborious. The developed method also provides great precision (RSD < 6%) and accuracy (relative error< 6%). Given the universal use of smartphones as portable and affordable devices, such format of immunoassay could be a promising approach for rapid and sensitive real-time monitoring of food fraud.

An untargeted metabolomics approach to identify markers to distinguish duck eggs that come from different poultry breeding systems by ultra high performance liquid chromatography-high resolution mass spectrometry

Untargeted metabolomics approach based on ultra high performance liquid chromatography coupled with high resolution mass spectrometry (UHPLC-HRMS) was used to investigate the differences in cage duck eggs and sea duck eggs that from different poultry breeding system, which could help to combat fraud within the egg industry. High dimensions and complex data collected by UHPLC-HRMS were analyzed by multivariate statistical analysis. Identification model of sea duck eggs based on was established. After matching with the chemical databases, four potential markers were putatively matched. Further analysis showed that three of them were confirmed by reference standards. All these three markers (n-behenoyl-d-erythro-sphingosine, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine and n-nervonoyl-d-erythro-sphingosine) have higher content in sea duck eggs. The quantitative analysis showed that the content difference of three markers in farm samples were in highly consistent with the concentration changes measured in experimental samples, which indicate that these three markers are reliable.

Honey authentication in terms of its adulteration with sugar syrups using UV-Vis spectroscopy and one-class classifiers

This work proposes the use of UV-Vis spectroscopy and one-class classifiers to authenticate honey in terms of their individual and simultaneous adulterations with corn syrup, agave syrup, and sugarcane molasses. Then, spectra of aqueous authentic (n = 73) and adulterated (n = 162) honey samples were recorded. Before the construction of OC-PLS and DD-SIMCA models, different pre-processing techniques were used to removed baseline shifts. The best result obtained by DD-SIMCA using offset correction, correctly classifying all the samples in the test set. Therefore, the proposed methodology can be used as a promising tool to authenticate honey and prevent fraudulent labeling, affording security to consumers and providing an alternative to regulatory agencies. Moreover, it avoids laborious sample preparation and additional operational costs, since the analytical information is acquired using a routine instrumental technique, without the need for any sample preparation step, other than dilution of the samples in water alone.

Non-destructive authentication of Gourmet ground roasted coffees using NIR spectroscopy and digital images

The growing demand for excellent-quality coffees allied with their symbolic aestheticization that add value to the products favor the adulteration practices and consequently economic losses. So, this work proposes the suitability of NIR spectroscopy and Digital Images (from CACHAS) coupled with one-class classification methods for the non-destructive authentication of Gourmet ground roasted coffees. For this, Gourmet coffees (n = 44) were discriminated from Traditional (n = 36) and Superior (n = 10) by directly analyzing their powder without any sample preparation. Then, OC-PLS and dd-SIMCA were used to construct the models. dd-SIMCA using offset correction for NIR and RGB histogram for CACHAS achieved the best results, correctly recognizing all the 90 samples in both the training and test sets. Therefore, the proposed methodologies can be useful for both the consumers and regulatory agencies because it confirms the elevated standards of excellence of Brazilian specialty coffees, preventing fraudulent labeling, besides following the Principles of Green Analytical Chemistry.

Identification and semi-quantification of Atlantic salmon in processed and mixed seafood products using Droplet Digital PCR (ddPCR)

Fishery products are often subject to substitution fraud, which is hard to trace due to a lack of morphologic traits when processed, gutted, or decapitated. Traditional molecular methods (DNA barcoding) fail to identify products containing multiple species and cannot estimate original weight percentages. As a proof of concept, an Atlantic salmon (Salmo salar) specific ddPCR assay was designed to authenticate mixed food products. The method proved to be specific and able to accurately quantify S. salar when using DNA extracts, even in the presence of DNA from closely related salmon species. The ddPCR estimates correlated well with the percentage of S. salar in artificially assembled tissue mixtures. The effect of common salmon processing techniques (freezing, smoking, poaching with a "Bellevue" recipe and marinating with a 'Gravad lax' recipe) on the ddPCR output was investigated and freezing and marinating appeared to lower the copies detected by the ddPCR. Finally, the assay was applied to 46 retail products containing Atlantic or Pacific salmon, and no indications of substitution fraud were detected. The method allows for a semi-quantitative evaluation of the S. salar content in processed food products and can rapidly screen Atlantic salmon products and flag potentially tampered samples for further investigation.

Role of analytical testing for food fraud risk mitigation - A commentary on implementation of analytical fraud testing: Role of analytical testing for food fraud mitigation

Food fraud is of high concern to the food industry. A multitude of analytical technologies exist to detect fraud. However, this testing is often expensive. Available databases detailing fraud occurrences were systematically examined to determine how frequently analytical testing triggered fraud detection. A conceptual framework was developed for deciding when to implement analytical testing programmes for fraud and a framework to consider the economic costs of fraud and the benefits of its early detection. Factors associated with statistical sampling for fraud detection were considered. Choice of sampling location on the overall food-chain may influence the likelihood of fraud detection.

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A conceptual framework was developed for deciding when to implement analytical testing programmes for fraud.

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A conceptual framework for the economic impact damage of food fraud is presented.

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Sampling for fraudulent activity poses several unique challenges compared to other food safety related sampling activities.

Coumarins, psoralens, and quantitative 1H-NMR spectroscopy for authentication of lemon (Citrus limon [L.] Burm.f.) and Persian lime (Citrus × latifolia [Yu.Tanaka] Tanaka) juices

Mutual adulterations of lemon and lime juices may be detected using coumarins and psoralens as markers. Poor manufacturing practices or legal but mechanically intense processing of lemons were recently suspected to lead to false accusations of deliberate adulterations with lime juices due to potentially unspecific markers. Therefore, we studied coumarin and psoralen profiles in carefully dissected flavedo, albedo, and endocarp of lime and lemon as well as in juices produced under variable mechanical stresses at laboratory and pilot plant scale. Although the marker herniarin was detectable in juices from lime and harshly extracted lemons at low levels, isopimpinellin, bergapten and the herein proposed, tentatively assigned 5-geranyloxy-8-methoxypsoralen represented unambiguously lime-specific markers. Coumarin and psoralen data also allowed differentiating juices produced at differing degrees of mechanical stress. The latter was also possible using quantitative ¹H-NMR spectroscopy, which yielded best results when combined with HPLC data on coumarins and psoralens. In the future, the reported approach may be used for establishing a robust database prior to being used in industrial practice.

Wild or Farmed Gilthead Seabream (Sparus aurata)? How To Distinguish between Them by Two-Dimensional Gel Electrophoresis

ABSTRACT

Because the world's wild fish stocks are limited and the market demand is increasing, fish farming has become an alternative food source and a way to reduce costs for consumers. The sale of farmed as wild fish is a fraudulent practice; it is, therefore, important to find new and alternative tools that can help in the fight against fraud to protect consumers and to ensure food traceability. The proteomic profiles of farmed and wild fish differ. With this study we wanted to identify liver protein markers via two-dimensional electrophoresis that would allow us to distinguish wild from farmed gilthead seabream. The liver samples from 32 gilthead seabream, wild and farmed, were stored at -80°C before protein extraction. The samples were subjected to two-dimensional electrophoresis to detect qualitative and quantitative differences. Proteomic analysis showed a protein spot (molecular weight of ∼34 kDa and isoelectric point of ∼6.9) only in the samples from the wild gilthead seabream; liquid chromatography-tandem mass spectrometry identified the spot as ubiquitin. Ubiquitin could be a valid marker to differentiate wild from farmed gilthead seabream; it could be used to ensure continuous monitoring throughout the entire commercial chain and to fight commercial fraud.

HIGHLIGHTS

Detection of food spoilage and adulteration by novel nanomaterial-based sensors

Food industry is always looking for more innovative and accurate ways to monitor the food safety and quality control of final products. Current detection techniques of analytes are costly and time-consuming, and occasionally require professional experts and specialized tools. The usage of nanomaterials in sensory systems has eliminated not only these drawbacks but also has advantages such as higher sensitivity and selectivity. This article first presents a general overview of the current studies conducted on the detection of spoilage and adulteration in foods from 2015 to 2020. Then, the sensory properties of nanomaterials including metal and magnetic nanoparticles, carbon nanostructures (nanotubes, graphene and its derivatives, and nanofibers), nanowires, and electrospun nanofibers are presented. The latest investigations and advancements in the application of nanomaterial-based sensors in detecting spoilage (food spoilage pathogens, toxins, pH changes, and gases) and adulterants (food additives, glucose, melamine, and urea) have also been discussed in the following sections. To conclude, these sensors can be applied in the smart packaging of food products to meet the demand of consumers in the new era.

Rapid detection of carmine in black tea with spectrophotometry coupled predictive modelling

Carmine is an artificial colorant commonly used by fraudulent food business participants in black tea adulteration, for purpose of gaining illegal profits. This study combined spectrophotometry with machine learning for rapid detection of carmine in black tea based on the spectral characteristics of tea infusion. The qualitative model demonstrated an accuracy rate of 100% for successful identification of the presence/absence of carmine in black tea. For quantitative analysis, the R² between carmine concentrations generated according to spectral characteristics and those determined with HPLC was 0.988 and 0.972, respectively, for black tea samples involved in the test subset and an independent dataset II. Paired t-test indicated that the difference was statistically insignificant (P values of 0.26 and 0.44, respectively). The method established in this study was rapid and reliable for detecting carmine in black tea, and thus could be used as a useful tool to identify black tea adulteration in market.

Development of two specific multiplex qPCRs to determine amounts of Pseudomonas, Enterobacteriaceae, Brochothrix thermosphacta and Staphylococcus in meat and heat-treated meat products

Culturing methods are conventionally applied to investigate the contamination of food with several microorganisms after heat processing. However, with these methods, it is not possible to evaluate whether heat-treated meat products, such as cooked sausages, contained parts of spoiled meat. Therefore, two specific multiplex qPCRs were developed in this study in order to determine the microbiological quality of the raw materials used for these products. The PCR targets focused on four bacterial groups often found on meat (family Enterobacteriaceae, genus Pseudomonas, genus Staphylococcus and species Brochothrix thermosphacta). Specificity as well as sensitivity of the developed multiplex qPCRs, validated by using 68 microbial species, were 100%. The applicability of both multiplex qPCRs compared to culturing methods was performed using 96 meat samples (fresh and naturally spoiled) and 12 inhouse-made "Lyoner" sausages containing variable ratios of spoiled meat (0%, 5%, 12% and 25%; n = 3 for each group). Both methods showed similar results by evaluating the ∆log₁₀ cfu/g, the relative accuracy and the t-test analysis (p > 0.05). Comparing qPCR results of the different sausage groups, a significant difference between sausages containing fresh meat and sausages containing spoiled meat (12% and 25%) was found only for Pseudomonas and B. thermosphacta in both raw and cooked sausages. The statistical difference between 5% vs. 12% and 25% spoiled meat in cooked sausages, was also found only for these two bacterial groups. The developed multiplex qPCRs were further applied to 30 commercially available "Bologna-type" sausages. The results showed a total of 14 sausages considered to be suspicious for Food Fraud. While the role of Staphylococcus spp. in meat spoilage remains unclear, Pseudomonas, Enterobacteriaceae and B. thermosphacta could together be used as an indicator for "spoiled meat" used in sausages. The developed qPCR systems in this study allow the detection of four relevant bacterial groups in the heated Bologna-type sausages and provide information about the hygienic quality of raw materials used. This method could thus be helpful for screening food suspected of Food Fraud.

DNA-based techniques for seafood species authentication

Global trade of seafood has increased in the last decade, leading to significant concerns associated with seafood fraud. Seafood fraud involves the intentional misrepresentation of fish or shellfish for the purpose of economic gain and includes acts such as species substitution, illegal transshipment, overtreatment/short weighting, and mislabeling country of origin or production method. These fraudulent acts have had economic, environmental, and public health consequences on a global level. DNA-based techniques for seafood authentication are utilized by regulatory agencies and can be employed as part of a food fraud risk mitigation plan. This chapter will focus specifically on the use of DNA-based methods for the detection of seafood species substitution. Various methods have been developed for DNA-based species identification of seafood, including polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), species-specific PCR, real-time PCR, Sanger sequencing, microarrays, and high-resolution melting (HRM). Emerging techniques for seafood authentication include droplet digital PCR, isothermal amplification, PCR-enzyme-linked immunosorbent assay (ELISA), and high-throughput or next-generation sequencing. Some of these DNA-based methods target specific species, such as real-time PCR and droplet digital PCR, while other methods allow for simultaneous differentiation of a wide range of fish species, including Sanger sequencing and high-throughput sequencing. This chapter will begin with an introduction on seafood fraud and species substitution, followed by an analysis of the main DNA-based authentication methods and emerging techniques for species identification.

A rapid food chain approach for authenticity screening: The development, validation and transferability of a chemometric model using two handheld near infrared spectroscopy (NIRS) devices

This study assesses the application of a handheld, near infrared spectroscopy (NIRS) device, namely the NeoSpectra Micro, for the determination of oregano authenticity. Utilising a large sample set of oregano (n = 295) and potential adulterants of oregano (n = 109), models were developed and validated using SIMCA 15 software. The models demonstrated excellent predictability for the determination of authentic oregano and adulterant samples. The optimal model resulted in a 93.0% and 97.5% correct prediction for oregano and adulterants, respectively. Different standardisation approaches were assessed to determine model transferability to a second NIRS device. In the case of the second device, the best predictions were achieved with data that had not undergone any spectral standardisation (raw). Subsequently, the optimal model was able to correctly predict 90% of authentic oregano samples and 100% of the adulterant samples on the second device. This study demonstrates the potential of the device to be used as a simple, cost effective, reliable and handheld screening tool for the determination of oregano authenticity, at various stages of the food supply chain. It is believed that such forms of monitoring could be highly beneficial in other areas of food authenticity analysis to help combat the negative economical and health implications of food fraud.

Identifying Turmeric Powder by Source and Metanil Yellow Adulteration Levels Using Near-Infrared Spectra and PCA-SIMCA Modeling

ABSTRACT

Turmeric sourced from six retailers was processed into a powder and adulterated with metanil yellow (MY) at concentrations of 0.0 to 30% (w/w). A handheld near-infrared spectrometer was used to obtain spectral scans of the samples, which were preprocessed using Savitzky-Golay first-derivative (SG1) approximation using 61 smoothing points and second-order polynomial. The preprocessed spectra were analyzed using principal component analysis (PCA) followed by classification by soft independent modeling class analogy (SIMCA) and were used to group the adulterated turmeric powder samples according to the source (i.e., processor) of adulteration. Results showed the first principal component (PC1) of PCA models was sensitive to adulteration level, but when coupled with SIMCA, unadulterated and adulterated samples could be classified according to their source despite having high levels of MY. At 5% level of significance, all of the samples were correctly classed for origin during validation. Some samples were classified under two groups, indicating possible inherent similarities. When the PCA model was built using only unadulterated samples, the PCA-SIMCA model could not classify the adulterated samples but could classify those with very low levels (≤2%, w/w) of MY, allowing for segregation of adulterated samples but not identification of sources. The combination of near-infrared and PCA-SIMCA modeling is a great tool not only to detect adulterated turmeric powder but also, potentially, to deter it in the future because the source of adulterated food can be traced back to the source of adulteration.

HIGHLIGHTS

Feeding fiction: Fraud vulnerability in the food service industry

This study examines fraud vulnerability in the food service industry; identifies underlying fraud vulnerability factors; and studies the differences in fraud vulnerability between casual dining restaurants, fine dining restaurants and mass caterers for four product groups. Vulnerability was assessed by an adapted SSAFE food fraud vulnerability assessment, tailored to the food service sector situation. The 15 food service operators rated high vulnerability for 40% of the fraud indicators. This is considerably more than food manufacturers, wholesalers and retailers did previously. In particular, more opportunities and fewer controls were noted. Overall fraud vulnerability was more determined by the type of food service operator than by the type of food product. Casual dining restaurants appeared most vulnerable, followed by fine dining restaurants. Mass caterers seemed the least vulnerable operators, because they had more adequate food fraud controls in place. Considering its high vulnerability, reinforcement of mitigation measures in the food service industry is urgently recommended.

Tools to combat food fraud - A gap analysis

A complex legal and institutional framework exists in the EU to ensure the safety of the feed-food chain, while such an integrated system for combating food fraud is under development. The European Commission (EC) Knowledge Centre for Food Fraud and Quality is charged with the provision of scientific insight for the policy making of EC services dealing with food fraud, and the creation of expert networks with the competent authorities of the EU Member States. To flag gaps in the existing infrastructure needed for effectively and efficiently fighting food fraud, the Centre together with the competent authorities and several EC services undertook a stocktaking exercise of what works well and which areas will need improvement. Out of several focus areas, (i) the development of early warning systems, (ii) the availability of compositional databases of vulnerable foods, and (iii) the creation of centres of competence were prioritised for further action.

Food authentication in real life: How to link nontargeted approaches with routine analytics?

In times of increasing globalization and the resulting complexity of trade flows, securing food quality is an increasing challenge. The development of analytical methods for checking the integrity and, thus, the safety of food is one of the central questions for actors from science, politics, and industry. Targeted methods, for the detection of a few selected analytes, still play the most important role in routine analysis. In the past 5 years, nontargeted methods that do not aim at individual analytes but on analyte profiles that are as comprehensive as possible have increasingly come into focus. Instead of investigating individual chemical structures, data patterns are collected, evaluated and, depending on the problem, fed into databases that can be used for further nontargeted approaches. Alternatively, individual markers can be extracted and transferred to targeted methods. Such an approach requires (i) the availability of authentic reference material, (ii) the corresponding high-resolution laboratory infrastructure, and (iii) extensive expertise in processing and storing very large amounts of data. Probably due to the requirements mentioned above, only a few methods have really established themselves in routine analysis. This review article focuses on the establishment of nontargeted methods in routine laboratories. Challenges are summarized and possible solutions are presented.

Identification of camel species in food products by a polymerase chain reaction-lateral flow immunoassay

With an increased demand for camel meat, camel meat-related food products are susceptible to food fraud. To effectively authenticate camel-containing foods, a novel analytical technique based on polymerase chain reaction (PCR)-lateral flow immunoassay (LFI) was developed. The camel-specific PCR primers were designed to target at the mitochondrial COI gene. Both of the in silico and in vitro tests confirmed that the PCR-LFI was specific. A limit of detection of 0.1% w/w of camel meat in beef was achieved for both the raw and cooked (i.e. boiling and deep frying) meat samples. This novel method was used to authenticate 20 processed camel-meat products purchased from local grocery stores in China and online. Two products purchased online were identified as containing no camel meat. Overall, this novel PCR-LFI method is ideal for governmental laboratories to rapidly authenticate camel-meat containing food products.

Novel Application of Near-infrared Spectroscopy and Chemometrics Approach for Detection of Lime Juice Adulteration

The aim of this study is to investigate the novel application of a ‎handheld near infra-red spectrophotometer coupled with classification methodologies as a screening approach in detection of adulterated lime juices. For this purpose, a miniaturized near infra-red spectrophotometer (Tellspec®) in the spectral range of 900-1700 nm was used. Three diffuse reflectance spectra of 31 pure lime juices were collected from Jahrom, Iran and 25 adulterated juices were acquired. Principal component analysis was almost able to generate two clusters. Partial least square discriminant analysis and k-nearest neighbors algorithms with different spectral preprocessing techniques were applied as predictive models. In the partial least squares discriminant analysis, the most accurate prediction was obtained with SNV transforming. The generated model was able to classify juices with an accuracy of 88% and the Matthew's correlation ‎coefficient ‎value of 0.75 in the external validation set. In the k-NN model, the highest accuracy and Matthew's correlation ‎coefficient in the test set (88% and 0.76, respectively) was obtained with multiplicative signal correction followed by 2nd-order derivative and 5th nearest neighbor. The results of this preliminary study provided promising evidence of the potential of the handheld near infra-red spectrometer and machine learning methods for rapid detection of lime juice adulteration. Since a limited number of the samples were used in the current study, more lime juice samples from a wider range of variability need to be analyzed in order to increase the robustness of the generated models and to confirm the promising results achieved in this study.

Design of a user-friendly and rapid DNA microarray assay for the authentication of ten important food fish species

Seafood is particularly susceptible to the substitution of species. In order to guarantee authentic seafood products, seafood processors and traders must perform self-checks on the authenticity of imported and purchased goods. However, the conventional Sanger sequencing of PCR products for the authentication of seafood species is time-consuming and requires advanced infrastructure. DNA microarrays (DNA chips) with species-specific oligonucleotide probes represent a rapid alternative to sequencing-based species authentication. So far, though, only DNA microarrays for the authentication of land vertebrate species have achieved market success. In this work, a user-friendly DNA microarray assay was developed for the authentication of ten important food fish species that can be performed in four to five hours from start to end. The assay was tested with authenticated specimens from 67 different fish species, and by comparing the probe signal patterns all target species and even closely related non-target species could be distinguished.

High-resolution melting of multiple barcode amplicons for plant species authentication

In recent years, species identification in herbs has attracted considerable attention due to several cases of fraud; hence inexpensive high-throughput authentication methods are highly welcomed. Species authentication is often performed through DNA analysis and several specific regions (barcodes) are considered suitable. Each barcode (Bar) possesses different qualities in terms of universality and discrimination power. A multiplexed format where information can be extracted simultaneously from several barcode regions is seemingly appropriate to ensure the power of both universality and discrimination. In this approach, we amplified DNA from five different barcode regions in a multiplexed PCR format followed by high-resolution melting (HRM). This multiplexed Bar-HRM approach was first applied to plants spanning the plant kingdom and then gradually narrowing down the genetic variability within the Lamiaceae and the Solanaceae families to finally reach closely related cultivars. Universality was demonstrated through distinct melting profiles obtained for species originating from 29 different families spanning the angiosperms, gymnosperm, mosses, and liverwort (Marchantiophyta). Discrimination power was retained for species, sub-species, and a few cultivars through the application of multivariate statistics to the high-resolution melting profiles. This preliminary investigation has shown the potential to discriminate a vast amount of species within the whole plant kingdom. It requires no a priori knowledge of the species' DNA sequence and occurs in a closed system within 2.5 h at a reduced cost per sample compared to other DNA based approaches.

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A DNA profiling platform for species authentication throughout the plant kingdom.

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Distinct and reproducible melting profiles were obtained for all tested species.

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Universality was demonstrated across 29 families spanning the plant kingdom.

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Specificity was demonstrated for related species, sub-species, and cultivars.

Preliminary investigation into the use of Raman spectroscopy for the verification of Australian grass and grain fed beef

Australian grass and grain-fed beef products attract premium prices at sale and several beef processors market beef underwritten by production system claims. This preliminary investigation assessed the feasibility of using Raman spectroscopy to detect differences in the chemical composition of subcutaneous fat from cattle raised in extensive and intensive production systems. Raman spectra, fatty acid composition, β-carotene composition and objective colour measurements were measured on 150 grass and 150 grain-fed cattle. Spectral differences at peaks including 1069 cm^-1, 1127 cm^-1, 1301 cm^-1 and 1445 cm^-1 suggest that Raman spectra is able to detect differences in saturated fatty acids, which were significantly higher in carcases from grain-fed cattle. Differences in spectra at 1658 cm^-1 were observed, however further research is required to investigate the cause of this spectral feature. Overall, this study indicated that Raman spectroscopy is a potential tool for the authentication of beef carcases from grass and grain-fed production systems.

Food fraud in oregano: Pesticide residues as adulteration markers

Oregano, a widely used and popular herb, is particularly vulnerable to fraud. Less valued plants, adulterants that are often used for dilution, may introduce into this commodity additional contaminants such as pesticide residues. In this study, more than 400 pesticides were screened in a representative set of 42 genuine and 34 adulterated dried oregano samples collected from various locations across Europe. The results obtained by advanced mass spectrometry-based methods, showed, that some pesticide residues could be detected in virtually all tested samples, nevertheless, on average, higher contamination was found in the adulterated oregano samples. Increased incidence of insecticides such as cyfluthrin, permethrin and cyhalothrin was typical for these samples, moreover, pyriproxyfen was detected exclusively in adulterated samples. Thus, based on a critical assessment of pesticide profiles, suspected adulterated oregano samples can be selected for follow up authenticity testing.

Rapid quantification of the adulteration of fresh coconut water by dilution and sugars using Raman spectroscopy and chemometrics

Here, for the first time, we developed Raman spectroscopy in combination with chemometrics for the quantification of adulteration of fresh coconut water by dilution, and its masking with sugars. Coconut water was extracted from young Costa Rican coconuts and heat treated to emulate pasteurization. Samples were then adulterated by dilution with water and single sugars, mixtures of sugars, and high-fructose corn syrup (HFCS). A total of 155 samples were analysed with Raman spectroscopy at 785 nm excitation and 620 spectra analysed with chemometrics. Results showed successful quantification of dilution and adulteration with single sugars between 1.9 and 2.6%, masking of dilution with mixtures of sugars at 9.8%, and masking of dilution with HFCS at 7.1%. It can be concluded that Raman spectroscopy has significant potential as a rapid accurate analytical method for the detection of adulteration in this product, with the ability to discern small abnormalities in sugar ratios within coconut water.