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This paper (open access) introduces the workflow MEATiCode, a comprehensive proteomic liquid chromatography tandem mass spectrometry (LC-MS/MS) method for the simultaneous identification of species in meat authentication.

This novel database search approach enabled the differentiation of meat species (as demonstrated for beef, pork, chicken and lamb) in raw and cooked food products following a simple sample preparation procedure and LC-MS/MS analysis of extracted meat peptides.  Peptides and proteins were characterised from reference samples using an untargeted protocol.  The MEATiCode database was then constructed in the Mascot Server search engine, with the objective of creating artificial proteins comprising the concatenated amino acid sequences of the peptides identified as specific for each species.

The authors report that the efficacy of the MEATiCode method was demonstrated through its application to a range of meat products, achieving high sensitivity (0.5 % Limit of Detection (LoD)) and reliability in the detection of adulteration, even in highly processed or cooked meats.

In this study (purchase required) the researchers used bioinformatics methods to identify specific sequences of cattle, pig, chicken, and duck, and designed primers and probes accordingly.

They developed a method based on recombinase polymerase amplification (RPA) combined with lateral flow dipstick (LFD) for rapid visual authentication of beef and beef products. The RPA reaction was conducted at 37℃ for 20 min. The amplification products were then diluted and applied to the sample pad of the LFD. Results were visible to the naked eye within 5 minutes.

They report that the results demonstrated the method could specifically differentiate components of bovine, porcine, chicken, and duck origin, with a limit of detection (LOD) of approximately 20 copies for each species.

They applied the method to 10 commercially available beef products. Of which, five samples were detected with porcine-derived components. The results of the RPA–LFD method were verified using PCR and observed to be consistent between the methods.

The researchers conclude that this method is easy to use, requires no specialized equipment, and delivers results in about 30 min from amplification to detection, making it suitable for rapid visual detection on-site.

Authentication of mushroom commodities often relies on visual identification, including microscopy. The methods usually involve physical observation with high subjectivity, which may lead to mushroom-product fraud and mislabelling.

This review (purchase required) covers molecular methods and “chemical” methods coupled with chemometrics and/or artificial intelligence. These include DNA barcoding, which is an identification strategy based on the DNA sequence of the mushroom sample, specifically the internal transcribed spacer (ITS) region. The review discusses the advancements in the usage of both DNA barcoding and chemometrics-coupled methods in the authentication of mushrooms and their derivative products; and how these can solve some major hurdles relating to mushroom products.

Photo by Damir Omerović on Unsplash

In this study (open access) the authors made a reference dataset of comminuted meat mixtures by dicing and mixing 140 commercially-purchased steaks of beef, duck and chicken.  They built a classification model to discriminate between the three species in the mixtures.

They used a hand-held Hyperspectral Imaging (HSI) (with a Raspberry Pi controller, which has real-time image acquisition and processing covering  a spectral range from 400 nm to 800 nm) to develop a discrimination model for chicken/duck adulteration in diced beef. The portable push broom HSI was designed with the spectral resolution of 5 nm and spatial resolution of 0.1 mm. To improve generalization, a model transfer method was also developed to achieve model sharing across instruments

The authors report that their model transfer method can effectively correct the spectral differences due to instrument variation and improve the robustness of the model. The support vector machine (SVM) classifier combined with spectral space transformation (SST) achieved a best accuracy of 94.91%. Additionally, a visualization map was proposed to provide the distribution of meat adulteration.

They conclude that the portable HSI enables on-site analysis, making it an invaluable tool for various industries, including food safety and quality control.

Shrimp surimi-based products (SSPs) are composed of minced shrimp meat and are highly susceptible to fraudulent substitution by cheaper fish surimi.

This study (open access) employed a double-gene metabarcoding approach to authenticate SSPs sold in bulk (business-to-business) on Chinese e-commerce platforms. 16S rRNA and 12S rRNA genes were amplified and sequenced from 24 SSPs. Mislabeling was evaluated based on the correspondence between the ingredients (only those of animal origin) reported on the products’ labels and the molecular results.

The authors found that 21 of the 24 products were mislabeled. The replacement of Penaeus vannamei with other shrimp species was particularly noteworthy. In some samples the primary species detected in terms of sequence abundance were not shrimp but fish, pork, chicken, and cephalopods. The 12S rRNA sequencing results revealed that fish species like Gadus chalcogrammus, Evynnis tumifrons, and Priacanthus arenatus were added to some SSPs in significant proportions, with certain products relying on fish priced from “Low” to “High” levels to substitute higher-cost shrimp. Notably, many fish species in SSPs were highly vulnerable to fishing, raising sustainability concerns.

The authors conclude that the high mislabeling rate, as well as the detection of endangered fish species (Pangasianodon hypophthalmus), underscores significant quality control and supply chain integrity issues.

Photo by Fernando Andrade on Unsplash

This paper (purchase required) reports a method to differentiate pork gelatin from beef gelatin (down to 0.01% cross-contamination levels) based on the LC-MSMS analysis of 13 peptide marker ions (8 for bovine, 5 for porcine).  The authors report that their method was validated at three concentration levels and accurately identified the gelatin species in pharmaceutical capsules and gels.

LC-MSMS analysis of peptides provides an alternative approach to DNA testing, which has known difficulties in application to highly processed products like gelatin due to the low amount of viable DNA or distinctive fragments.  LC-MSMS is the approach described in a recent Defra research report which is referenced on the FAN research pages (scroll down table to FA0177).

This study (open access) investigated species substitution, mislabeling, and the sustainability of seafood products in the seafood markets of South China. 478 samples were purchased from retail markets in 11 cities across three provinces (Guangxi, Guangdong, and Hainan) between May 2021 and December 2023. Cytochrome c oxidase subunit I (COI) gene amplification was used to identify 156 fish species across 105 genera and 60 families. The researchers have published the correlation between genetic and taxonomical details.

The researchers used a combination of morphological and DNA barcoding methods to produce an atlas guide for these 156 economically important fish species.

Molecular identification revealed that 9.6 % (15/156) of fish species were mislabelled, with commercial fraud detected in three processed species: Hilsa kelee, Chelidonichthys kumu, and Argyrosomus japonicus. Some substitutions may have been unintentional.  3.8 % (6/156) of species identified were classified as threatened by the International Union for Conservation of Nature. The study also uncovered an example of illicit cross-border sales of fish products.

The authors conclude that their findings provide a technical reference for effective fish species identification and offer valuable insights into seafood market monitoring.

Photo by Dan Gold on Unsplash

Meat species identification has always been a challenge in highly processed foods, such as gelatines and stocks.

One approach is to measure proteins and protein patterns using mass spectrometry (MS).  A previous research project, under the UK Department of Environment, Food and Rural Affairs (Defra) Food Authenticity Programme, developed and in-house validated a method using proteomics.

That work has now been built upon by another 3 Defra projects to streamline the method to look for specific markers, in a format that can be used routinely by testing laboratories, and to fully validate the routine method including by interlaboratory trial.

All four research reports are now signposted on FAN’s Research pages.  Scroll through the table to find the appropriate report reference number:

• FA0166 – the original 2019 project – “Development, optimisation and validation of a non-targeted proteomics method for meat species identification”
• FA0165 – “Liquid chromatography targeted mass spectrometry method to determine the animal origin of gelatine - transfer to a high throughput, low cost platform with single lab evaluation”
• FA0177 – “Gelatine species determination, completion of method validation and determination of a quantitative method”
• FA0187 – “Interlaboratory trial of a mass spectrometry method for meat species determination”

This paper (open access) reports a survey of 62 retail samples of processed whitefish products from British, Italian and Albanian retailers (mainly high-street supermarkets).  24 samples, spanning all regions, were reported as mislabelled using the criteria below.

The researchers used Next Generation Sequencing following DNA extraction using commercial kits.  Full details of the primers are given in the paper.  They prepared in-house positive and negative controls by blending various proportions of white fish species (from whole, identifiable, fillets) that are not used in commercial fish product manufacturing into mixtures of “authentic” species.

Since commercial designations of seafood species vary greatly both across and within countries, the researchers compared the ingredients provided for each product to the official list of commercial designation of the country where the product was purchased.. If a common name was declared on the label, the relevant species name was obtained searching FishBase , while if a scientific name was provided, it was contrasted directly with the molecular results.

Using matches and mismatches between label information and DNA-based identification, the researchers classified the examined products into the following categories: (i) “green” (correctly labelled product): when the proportion of reads of the declared species was at least twice as large as the second most abundant species and constitutes the majority of the bulk; (ii) “amber” (misleading product): when the proportion of the declared species was higher than any other species, but not necessarily amounting to the majority of the bulk; (iii) “red” (mislabelled product): when the declared species was either absent or not the most abundant in the mix; (iv) “grey” (undetermined product): when the declared species couldn’t be genetically identified with certainty.

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance.

Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with
traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud.

This manuscript reviews the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions are also be discussed.

Read full paper here.