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Authenticity tests for coffee tend to focus on the variety (Arabica vs Rustica) or adulteration of roasted ground coffee (e.g. with chicory).  There has been relatively little focus on authenticating the origin of green beans, for example to underpin Fair Trade traceability.

Proteomics has previously shown differences among cultivars.  This paper (subscription required) built on previous studies that had showed that long-term adaptation to a distinct climate (associated with the geographical location), are likely to significantly affect various metabolic processes and thus protein profiles.  Most proteins in beans are likely to be enzymes, such as oxidases and peroxidases. Previous researchers had identified 531 proteins in C. arabica cultivars in high-altitude African and low-altitude South American samples. Further analysis pointed out that only a few proteins were significantly different between them, plausibly corresponding to the concentration of certain compounds (e.g., flavonoids) alongside the adaptation to the environmental niches (e.g., colder climate or predominant pathogens). Post-harvest processing modifies proteomic profile.

This study used a combination of proteomic profiling with linear discriminant analysis for the classification of the geographical origin of green specialty coffee beans from well-known harvesting regions in Central America, South America, Africa, and Asia. Out of 1596 identified proteins, the authors selected the top 30 target markers ranked by ANOVA. They report that the model's prediction performance using leave-one-out cross-validation reached 85.3 %, with the lowest accuracy in the prediction rate for Asian samples. Model performance and prediction sensitivity to random states were tested using 5-fold cross-validation. After 20 iterations, the model performance slightly decreased to 84.0 %. Specificity and sensitivity confirmed that the model appears to be reliable at distinguishing Asian and African samples.

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This study (purchase required) reports a targeted proteomics workflow that identified two peptide markers that could be used to identify chickpea protein in plant-based meat substitute food products.  Chickpea protein has been reported as a commodity with increasing supply and demand pressures as global demand increases.

The authors developed a high-resolution, targeted proteomics workflow for authenticating chickpea protein concentrates using LC-QTOF-MS/MS. Unlike broader spectral fingerprinting approaches such as spectroscopy techniques or nitrogen quantification, this method enables peptide-level specificity, allowing for robust detection in complex food matrices. The workflow used both in-gel and in-solution trypsin digestions

They report the discovery of two chickpea-specific legumin-derived peptides that were consistently detectable and unique among common plant, dairy, and other adulterant sources. To the best of the knowledge of the authors, these are the first peptides suggested for use of chickpea adulteration detection by any proteomics techniques.

They report that detection remained reliable even in commercial chickpea pasta samples containing about 20% total protein. 

Photo by Karyna Panchenko on Unsplash

The authors of this study developed a targeted proteomics approach using LC–MS/MS and cross-species marker peptides with the potential to quantify meat in vegan and vegetarian foods. The method is designed to achieve the threshold of 0.1% w/w that is commonly applied for unintended cross-contamination.

 Protein extraction and digestion were optimized for rapid, simplified, and highly efficient sample preparation. Three matrix calibrations (0.1–5.0% w/w meat, each) were applied to vegan sausages and burger patties spiked with pork, chicken, or beef meat. The four markers DFNMPLTISR, DLEEATLQHEATAAALR, IQLVEEELDR, and LDEAEQLALK showed the highest accuracies for the determination of meat contents (recovery rates of 80–120%).

Although purchase is required for the full paper (here) the work builds upon previous publications and this supporting information is available free of charge (following the same link).  This includes detailed description of the statistical analysis; meat marker peptides before and after their re-evaluation; pea marker peptides; details of the LC runs; base materials and further ingredients for the vegan sausages and vegan burger patties; defatting/dehydration efficiencies of PLE and in-tube defatting/dehydration; comparison of extraction buffers and trypsin concentrations (matrix: vegan sausage with chicken meat); properties and comparison of different trypsins; chromatograms of the meat marker peptides from different matrixes; linear regressions derived from the quantifiers of the meat marker peptides in different matrixes; trueness and precision; mean signal-to-noise ratios at given meat contents.

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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.

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”

High performance liquid chromatography and high-resolution mass spectrometry (HPLC-MS/MS) was used to identify gelatin from seven commercial cyprinid fishes;, black carp, grass carp, silver carp, bighead carp, common carp, crucian carp, and Wuchang bream.

By comparison with theoretical mammalian collagen (bovine and porcine collagen), the common and unique theoretical peptides were found in the collagen of grass carp, silver carp, and crucian carp, respectively.  Seven common characteristic peptides were obtained from the fish gelatins. Moreover, 44, 36, and 42 unique characteristic peptides were detected in the gelatins of grass carp, silver carp, and crucian carp, respectively.

The researchers concluded that the combined use of common and unique characteristic peptides could verify fish gelatin in comparison with mammalian gelatin.

Read abstract.