Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents

• Published in: Food chemistry Vol. 446; p. 138893
• Main Authors: Vinothkanna, Annadurai, Dar, Owias Iqbal, Liu, Zhu, Jia, Ai-Qun
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.07.2024
• Subjects: Analytical instruments; Artificial intelligence; Chemometrics; Food; Food adulteration; Food Contamination - analysis; Food Safety; Fraud; Machine learning; Patent databases; Food adulteration; Analytical instruments; Artificial intelligence; Patent databases; Chemometrics; Machine learning
• ISSN: 0308-8146; 1873-7072; 1873-7072
• DOI: 10.1016/j.foodchem.2024.138893

•Food fraud prevention is vital for consumer safety and overall wellness.•Qualitative and quantitative data analysis approaches impact process effectiveness.•FTIR, PCR, and GCMS combined chemometrics are the best tools to detect food fraud.•Patents of several countries revealed cutting-edge food fraud detecting systems.•Systematic review will help food scientists develop food fraud abatement systems. Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018–2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.