Consumers have the right to know what is in the food they are eating. Accordingly, European and global food regulations require that the provenance of the food can be guaranteed from farm to fork. Many different instrumental techniques have been proposed for food authentication. Although traditional methods are still being used, new approaches such as genomics, proteomics, and metabolomics are helping to complement existing methodologies for verifying the claims made about certain food products. During the last decade, proteomics (the large-scale analysis of proteins in a particular biological system at a particular time) has been applied to different research areas within food technology. Since proteins can be used as markers for many properties of a food, even indicating processes to which the food has been subjected, they can provide further evidence of the foods labeling claim. This review is a comprehensive and updated overview of the applications, drawbacks, advantages, and challenges of proteomics for food authentication in the assessment of the foods compliance with labeling regulations and policies. This review paper provides a comprehensive and critical overview of the application of proteomics approaches to determine the authenticity of several food products updating the performances and current limitations of the applied techniques in both laboratory and industrial environments.

Increasing demand for organic products and their premium prices make them an attractive target for fraudulent malpractices. In this study, a large-scale comparative metabolomics approach was applied to investigate the effect of the agronomic production system on the metabolite composition of carrots and to build statistical models for prediction purposes. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully to predict the origin of the agricultural system of the harvested carrots on the basis of features determined by liquid chromatography-mass spectrometry. When the training set used to build the OPLS-DA models contained samples representative of each harvest year, the models were able to classify unknown samples correctly (100% correct classification). If a harvest year was left out of the training sets and used for predictions, the correct classification rates achieved ranged from 76% to 100%. The results therefore highlight the potential of metabolomic fingerprinting for organic food authentication purposes.

Internal control genes with highly uniform expression throughout the experimental conditions are required for accurate gene expression analysis as no universal reference genes exists. In this study, the expression stability of 24 candidate genes from Triticum aestivum cv. Cubus flag leaves grown under organic and conventional farming systems was evaluated in two locations in order to select suitable genes that can be used for normalization of real-time quantitative reverse-transcription PCR (RT-qPCR) reactions. The genes were selected among the most common used reference genes as well as genes encoding proteins involved in several metabolic pathways.

There is a need for selective and sensitive methods to detect the presence of food allergens at trace levels in highly processed food products. In this work, a combination of non-targeted and targeted proteomics approaches are used to illustrate the difficulties encountered in the detection of the major peanut allergens Ara h 1, Ara h 2 and Ara h 3 from a representative processed food matrix. Shotgun proteomics was employed for selection of the proteotypic peptides for targeted approaches via selective reaction monitoring. Peanut presence through detection of the proteotypic Ara h 3/4 peptides AHVQVVDSNGNR (m/z 432.5, 3+) and SPDIYNPQAGSLK (m/z 695.4, 2+) was confirmed and the developed method was able to detect peanut presence at trace levels (≥10 μg peanut g⁻¹ matrix) in baked cookies.

Honey, a sweet and nutritious food produced by honeybees, is extensively consumed by humans due to its potential health benefits. Unfortunately, the adulteration of honey with inexpensive sugar syrups is a prevalent issue. Verifying the authenticity of honey is crucial for maintaining its quality and safety standards. The aim of this study was to identify the illicit addition of sugar syrups to honey imported into the European Union (EU). The European Commission’s Joint Research Centre (JRC) has employed different analytical approaches to detect several markers of adulteration in honey; however, this paper mainly focuses on the use of Liquid Chromatography–High-Resolution Mass Spectrometry (LC-HRMS). Two qualitative methods were developed to detect mannose (Man), difructose anhydride III (DFA III), 2-acetylfuran-3-glucopyranoside (AFGP), and oligo-/polysaccharides with degrees of polymerization (DPs) of 6 to 11. Out of the 320 honey samples provided by the authorities of the participating EU Member States, 147 (46%) were suspicious for non-compliance with the EU Honey Directive 2001/110/EC, mostly due to the presence of mannose and oligo-/polysaccharides. As a result, the development and standardization of sophisticated and universally recognized testing procedures will increase the capability of official control laboratories to detect honey adulteration and will serve as a powerful preventive measure against fraudulent practices in the global honey market.