Methods of Authentication of Food Grown in Organic and Conventional Systems Using Chemometrics and Data Mining Algorithms: a Review

There is a general consensus that the consumption of organic food can contribute to a healthy diet; nevertheless, large-scale production of organic food is not an easy task since it requires intense care due to the number of pests, fungi, and diseases that can wipe out an entire crop. Researchers evaluating food quality are often concerned with the use of pesticides, antibiotics, and hormones in agriculture, along with genetic modification (GMOs) and additives in food processing. Thus, a major challenge that arises in this context is how to obtain products that are free of these toxic elements. In this review, we give an overview of the research conducted in relation to the chemometric tools for extraction of variables in several types of food and the use of data mining techniques and statistical analysis to classify samples grown in organic and conventional systems. The expansion of the organic sector, driven by growing demand and high prices, could lead to fraud. Then, creating mechanisms that can be used by regulators, supervisory bodies, or even installed in supermarkets so the client can do this verification may be a deterrent for this type of deception. Results presented by recent research have shown that chemometric methods associated with data mining algorithms or statistical methods can be used to successfully classify products grown in organic and conventional systems.