Application of Forensic Science in Food Fraud Detection: Current Technologies, Challenges, and Future Perspectives

Food fraud is the intentional economic deception involving the adulteration, substitution, mislabeling or counterfeiting of food products and it has been calculated that the annual economic losses due to food fraud are in the range of USD 40-50 billion. Multi-level, complex food value chains make it easy for bogus practices, which traditional inspection approaches are not able to detect. To meet this challenge, forensic science has developed and evolved to be the discipline of choice that combines advanced analytical chemistry, molecular biology, stable isotope geochemistry, digital imaging and data science and makes use of systematic frameworks based on the evidence in a case-based manner. This review critically assesses the current state of the art of the most common platforms of forensic technologies used for food fraud detection (chromatographic, spectroscopic, DNA-based, isotope ratio and imaging). The role of the emerging technologies such as machine learning, traceability based on blockchain, sensors based on nanomaterials, and foodomics is discussed in the context of real-life application needs. The application of these methods is demonstrated in commodity specific applications that are pertinent to olive oil, honey, spices, seafood, meat, dairy, and beverages, in a regulatory and commercial context. Structural challenges remain persistently in place which are analysed and assessed, including analytical matrix variability, regulatory fragmentation, inadequate reference database and the adaptive intelligence of fraudulent actors. Future directions for integrated, field-deployed, real-time authentication systems are outlined with special focus on data fusion from multiple platforms and harmonisation of the global regulatory framework.