Chemometric tools for food fraud detection: The role of target class in non-targeted analysis. (1st July 2020)

• Record Type: Journal Article
• Authors: Rodionova, O.Ye.; Pomerantsev, A.L.
• Is Part Of: Food chemistry. Volume 317(2020)
• Publication Date: 2020-07-01
• Subjects: Food authentication -- Non-targeted analysis -- Oregano herbs -- NIR -- DD-SIMCA -- Multiclass PLS-DA; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2020.126448
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Difference between discriminant analysis and one-class classifiers is considered. Efficient non-targeted analysis should be based on one-class classifier. Sensitivity, specificity and efficiency exhaustively represent classification quality. NIR spectrometry combined with SIMCA is a rapid method for Oregano authentication. Abstract: The chemometric issues related to the application of non-targeted analysis for the detection of food frauds were analyzed employing discriminant analysis and a one-class classifier. The similarities and differences between the two methods were investigated. The results of classification are characterized by a set of indices called figures of merit. They comprehensively characterized the quality and reliability of classification. The principle is illustrated using an actual example of Oregano herbs adulteration. The informative region 9000–4000 cm −1 of near-Infrared spectroscopy is used as analytical means. The results of the application of each method for Oregano data collection are presented. It is shown that the discriminant method is only partially appropriate for solving the authentication problem. One class classifier is a powerful and devoted for non-targeted analysis. The step by step analysis introduced in the paper can also be successfully utilized in apply for revealing of forgeries of various food products.