This study evaulated the feasibility of using non-targeted UHPLC-HRMS (ultra high pressure liquid chromatography coupled to a high resolution mass spectrometer) fingerprints as chemical descriptors to address the classification and authentication of paprika samples. Fingerprinting data based on signal intensities as a function of m/z values and retention times were registered in negative ion mode using a q-Orbitrap high-resolution mass analyser, which targeted mainly the polyphenols and phenolic acids in the paprika. The fingerprints were subjected to unsupervised principal component analysis (PCA) and supervised partial least squares regression-discriminant analysis (PLS-DA) to study sample discrimination and classification.  A total of 105 paprika samples produced in three different regions, La Vera PDO and Murcia PDO in Spain, and the Czech Republic, were analysed. Non-targeted UHPLC-HRMS fingerprints were able to authenticate paprika production regions with 100% sample classification rates by PLS-DA. In addition, the obtained fingerprints were also able to discriminate between the different paprika taste varieties in all the studied cases, even in the case of La Vera PDO, which has sweet, bittersweet, and spicy tasting paprika, which are produced in a very small region.

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DNA  methods are available for determining the adulteration of orange juice by mandarin juice. Chinese researchers have developed a different approach by analysing the VOCs in the headspace of orange juice (not from concentrate) and manadarin juice, and using chemometrics to calculate if a sample of orange juice has mandarin juice in it. Juices were extracted from different cultivars of sweet orange and mandarin fruits. After being pasteurised, the VOCs in the juice samples were extracted using headspace solid-phase microextraction, and qualitatively and quantitatively analysed using gas chromatography–mass spectrometry. Thirty-two VOCs identified in both the sweet orange juice and mandarin juice, but at different concentrations, were used as variables, and the identification model for discriminating between the two varieties of juice was established by principal component analysis. Model juice mixtures were made up from different varieties of both mandarins and oranges. The model was able  to identify and quantify a blend of mandarin juice in orange juice at the level of 10% or above.

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Brazil’s Min­istry of Agri­cul­ture, Live­stock and Food Supply (MAPA) conducted an investigation over the past two years into the olive oil market, which revealed wide­spread mis­la­bell­ing in a country where olive oil con­sump­tion has risen sharply in recent years. MAPA ana­lysed 322,329 litres of olive oil sam­ples col­lected from 12 Brazil­ian states, and found 207,579 litres (64 per­cent) to be sub­stan­dard. The inspection team investigated 279 samples from 214 batches, and 38,7% of the batches were problematic most of which related to poor quality olive oil being sold as good quality. The high­est inci­dences of olive oil fraud occurred in those states with large num­bers of  bottling com­pa­nies, and fraud was rife among bot­tlers who import olive oil in bulk, mainly from Argentina.

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This review maps the current state of research in the area of the geographical origin of agricultural products and food. The review is divided in three parts. The first part deals with the analytical techniques applied in the food authentication, with special mention of the use of elemental analysis and multiple isotope ratios. The second section focuses on published research data for geographical origin authentication for the period 2015–2019. The third part contains the results on certification of geographical origin on specific agricultural products.drawn from the published articles in the period 2015–2019. 

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Raman spectroscopy represents an increasingingly useful technique for food authentication being a fast, reliable non-targeted method, requiring a minimum sample preparation step. However, in the case of honey, there are limitations to its application caused by sugar crystallisation effects and fluorescence in dark coloured honeys. Romanian researchers have developed a simple sample preparation of honey by a 1:1 w/v dilution in distilled water, which overcomes the limitations and gives reliable and reproductible spectra. 

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Acorns of the two main Quercus species (Q. rotundifolia and Q. suber) of the Portuguese Montado are the main food of the black Iberian pig, which are used to produce the high value Iberian ham. Portuguese and Italian researchers analysed the isotopic composition of the acorns, and found there were no differences in nitrogen or carbon (δ¹⁵N and δ¹³C) isotope ratios between the two species. However, combining the samples and testing for association with the Aridity Index (aridity gradient of the region), it was found that more arid sites lead to a ¹⁵N enrichment. Examining the morphology of the acorns with Aridity index, it was found that there was a correlation of higher weight and length only in Q. suber acorns with more arid sites. These results permitted mapping of the isotopic ratios (isoscapes), which could provide a tool to enable the provenance of Iberian pigs/ham to be verified.

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Greek researchers have developed a new paper-based DNA biosensor for milk adulteration detection, which uses the properties of gold nanoparticles for visual detection. DNA from milk-based yoghurt samples was isolated, and species-specific DNA sequences for cow, sheep and goat were amplified and identified by the biosensor using specific DNA probes. The proposed paper-based DNA biosensor offered 10 times higher detectability than other methods, good specificity and reproducibility. It has the potential to be applied to other foods.

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The global seafood supply chain handles an enormous volume of products, half of which is caught wild, and there is also widespread illegal fishing feeding into this supply chain. It is one of the most fragmented food supply chains, and transparency into its actors and the movement of products has been notoriously elusive and difficult to manage. There is now increasing pressure from consumers, retailers, government and watchdog organisations to improve transparency. This article discusses the problems of having a fragmented supply chain, and the pressures of improving it, and the consequences of not doing so.

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This review by Indian authors examines intentional economic adulteration with examples taken mainly on the Indian sub-continent. The types of adulteration are listed, as are the methods to detect them. 

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A novel method to identify animal species in complex or adulterated processed meat products has been developed by Chinese researchers, which combines a cytochrome oxidase I (COI) mini-barcode with next-generation sequencing (NGS).  A universal primer based on 140 sequences from 51 edible animal species was designed. A mixture of 12 species raw meat samples (beef, water buffalo, pork, sheep, chicken, partridge, grass carp, silver carp, blue scad, tile fish, pomfret, and prawn) were identified both with the clone sequencing and also with the mini-barcode (136 bp) sequence combined NGS method. The NGS method was superior in accuracy, sensitivity, and detection efficiency compared to the clone sequencing method, The edible animal species were identified both in the mixed raw samples and 7 heavily processed food products (different meatballs - beef, pork, fish, and shrimp), 'modified' beefsteak, sausages, and Chinese sausages. Moreover, some unlabelled species and dubious contamination were also detected as well, leading to stringent cleaning procedures.

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Quantitative DNA methods are used to detect and measure common wheat adulteration of durum wheat pasta. Italian and Argentinian researchers have validated a method for common wheat adulteration using Fourier transformed infrared spectroscopy (FT-IR) and chemomentrics. The dataset used to calibrate this infrared method was from 300 samples of both Italian and Argentinian durum wheat pasta analysed by an ELISA (enzyme-linked immunosorbent assay) method with common wheat adulteration ranging from less than 0.5% to 28%. These samples were analysed by both near- and mid-infrared spectroscopy (FT-NIR, FT-MIR) and the performance results were compared. The spectra were then analysed by two chemometric methods  - Partial-Least Squares Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA). The first LDA and PLS-DA models grouped samples into three-classes, i.e. common wheat ≤1%, from 1 to ≤5% and >5%; while the second LDA and PLS-DA models grouped samples into two-classes using a cut-off of 2% common wheat adulteration. The accuracy of the validated models were between 80 and 95% for the three-classes approach, and between 91 and 97% for the two-classes approach. The three-classes approach provided better results in the FT-NIR range, while the two-classes approach provided comparable results in both spectral ranges. These results indicate the method could provide a rapid and inexpensive way of determining common what adulteration in durum wheat pasta.

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Commercial starches such as corn starch and potato starch are widely used as ingredients by industry and consumers. In China, the most common commercial types of starch are potato, sweet potato, cassava, corn, and wheat, but cassava starch is the cheapest of all the starches. Chinese researchers have developed a rapid and accurate quantitative detection method using droplet digital PCR (ddPCR) technology to identify cassava adulteration in commercial starch products.The ddPCR analysis showed that the weight of cassava (M) and cassava-extracted DNA content had a significant linear relationship—the correlation coefficient was R² = 0.995. The developed method was tested by analysing 50 commercial starches (30 sweet potato, 12 potato, and 8 corn starch). Eleven of the 30 sweet potato starch samples were adulterated with cassava starch, and one sample has just over 37% cassava starch, also 5 out of the 12 potato starches were adulterated and 2 out of the 8 corn starches were adulterated. 

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Cibus Analytical will commercialise research completed at IGFS, Queens University Belfast, and will not only offer a laboratory testing service, but also innovative, portable testing tools to allow customers to rapidly test for food authenticity and safety across global supply chains. The £0.57 million seed funding has been led by QUBIS, the commercialisation arm of Queen’s University, and also involves a number of investors with a range of backgrounds from the food sector and support from Innovate UK. Cibus Analytical was founded by IGFS researchers Prof Chris Elliott, Dr Terry McGrath, Dr Simon Haughey and Mrs Pamela Galvin-King. 

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Chinese researchers have applied an electronic nose (E-nose) system to detect beef adulteration with pork. The E-nose system uses a colourimetric sensors, which give different colours with different volatile compounds emmitted by the meat, the resulting coloured pattern is analysed by image analysis before and after exposure to the meat sample. The resulting signals are then analysed chemometrically to predict both qualitatively and quantitatively, the adulteration of beef with pork. This system was tested using samples of raw minced beef and pork mixed at different levels from 0%  to 100%  adulteration at 20%  increments. The system was able to accurately identify adulteration and give good quantitative correlation.  Read the article and the full paper.

UK meat plants and abattoirs, like many other sectors, are suffering a shortage of personnel during the Covid-19 crisis. However, the shortage is not just for operators in the plants, but also there is insufficient supervision by official veterinarians (OVs) and meat hygiene inspectors (MHIs) because of Covid-19 related sickness. In the face of these challenges the FSA, which has responsibility for meat hygiene, has drawn up contingency plans to try and deal with this situation. Measures include transferring qualified staff from other roles back to inspection, and bringing some qualified staff out of recent retirement. There is also the possiblitity to relax certain rules on inspection to ease the burden in meat plants and abattoirs.

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Cocoa botanical and geographical origin and the primary processing applied (i.e. 'fermentation') at the beginning of the supply chain characterise the quality of cocoa beans and influence the finished chocolate.  Therefore, authenticity markers of cocoa beans could verify their origin in the supply chain, and establish a compositional link between the raw materials and finished chocolate, and enhance traceback. In this study, Dutch researchers examined the retention and loss of compositional signatures from cocoa beans to chocolate. The compositional signatures chosen were VOCs (volatile organic compounds), elemental and stable isotope signatures. These were determined in cocoa beans from 10 different origins and 11 corresponding chocolates by high sensitivity-proton transfer reaction-mass spectrometry (HS-PTR-MS), inductively coupled plasma-MS (ICP-MS) and isotope ratio-mass spectrometry (IR-MS), respectively. The volatile fingerprints provided mostly information on the origin and primary processing traits of the raw cocoa beans in the chocolates. VOCs that are relevant and robust markers include: acetic acid (m/z 61), benzene (m/z 79), pyridine (m/z 80), 2-phenylethanol (m/z 123), and maltol (m/z 127). On the other hand, the elemental and stable isotope characteristics are more indicative of the cocoa content and added ingredients, although Fe, Cr, and Cd are possible elemental markers for origin. 

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Authenticating nut and nut products is not only important to prevent adulteration, but also has safety implications for allergy sufferers. Spanish researchers have developed a method using HPLC-FLD (high performance liquid chromatography with fluorescence detection) combined with chemometrics (partial least squares discrimination - PLS-DA) to produce non-targeted fingerprints to authenticate ten species of nuts, as well as detect and quantify adulterations with hazelnut and peanut in almond-based products (almond flour and almond custard cream). A satisfactory global nut classification was achieved with PLS-DA. Paired PLS-DA models of almonds with their adulterants were also evaluated, producing a classification rate of 100%. 

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The δ¹³C and δ¹⁵N isotopic ratios  are important in the investigation of food authenticity and fraud. Previous studies in Brazil on targeted foods revealed  that many of them were adulterated; mislabelled or even fraudulent. Hence to improve future authenticity studies, Brazilian researchers have determined baseline values of δ¹³C and δ¹⁵N in 1245 food items and 374 beverages; most of them made in Brazil. The average δ¹³C and δ¹⁵N values of C3 plants, C4 plants, plant-based processed foods, meat (including beef, poultry, pork and lamb), meat products, and beverages (including beer and wine) are all given in the paper. Because cattle are grass fed, and chickens and pigs fed on soya and maize, C-C₄ constitutes a large proportion of fresh meat, dairy products, as well as meat products. Also cane sugar and maize predominate as ingedients, and hence there is a large proportion of C-C4 in plant-based processed foods.

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Karen Everstine (Descernis) and Selvarani Elahi (LGC) have written an update on the Food Authenticity Network giving a good overview of its achievements since it formation nearly 5 years ago in response to the 2013 horsemeat incident. Read the article here

Determining the geographic origin of coffee beans is more challenging with roasted coffee beans, and is useful if it can be performed on the product available to the consumer. US researchers analysed the concentrations of 44 trace elements in 53 samples of roasted Arabica coffee beans (Coffea arabica) from 21 different countries. Although trace elements are not volatilised at roasting temperatures, the absolute elemental concentrations of coffee beans vary through different degrees of roasting (from green through dark roasts). The study analysed trace element ratios to evaluate concentration-related differences among beans from different origins. By comparing the distributions of 1892 element ratios for each of the  countries, the study demonstrated that many of the world’s coffee-producing regions can be distinguished from other regions of the world on the basis of element ratios.

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