Advanced evaluation techniques: Gas sensor networks, machine learning, and chemometrics for fraud detection in plant and animal products

Food fraud occurs worldwide in several aspects, affecting almost all food commodities. Aside from being a significant economic issue, it is a concern in terms of health issue, quality and consumer rights. As food fraud methods have become more sophisticated, due to complexity of fraudulent practices, highly efficient and reliable adulteration detection techniques are needed. In many food fraud detection methods, sample preparation and analysis involve detailed steps that require advanced technologies, making the process tedious and time-consuming. Therefore, the development of non-destructive and rapid methods for detecting food fraud has attracted considerable attention. Recently, electronic nose (e-nose) systems have evolved to detect adulteration of food products reliably and quickly. E-nose detects VOC in a samples and create unique fingerprint, which can be analyzed using chemometrics to identify fraud in foods. This review aims to provide a brief overview of e-nose systems, chemometrics methods, and their application to the detection of adulteration in plant and animal products based on review articles. As fraud in the food industry increases, more research is needed to protect consumers from health risks and producers from economic loss. Therefore, this paper discusses the challagens associated with using e-nose technology for detecting food fraud.