Automatic food and beverage authentication and adulteration detection by classification hybrid fusion

Abstract

A rising problem in the food industry is identifying the origin and/or purity of food products, and classification is often used for the identification. The process involves determining whether a food sample is similar to a collection of samples characterizing a known food product (is the food product a class member?). However, classification requires the analyst to face a myriad of complex decisions. With modern instrumentation, classification based on conventional data fusion processes increases the assortment of decisions. Typically, testing food products without data fusion involves (1) selection of an instrument or measuring device, (2) determining suitable measurement variables, for example, sensors or wavelengths for spectral data, (3) data preprocessing optimization, and (4) selection of a classification method followed by identifying the best tuning parameter setup for that classifier. If data fusion is desired, optimization of combined variations of (1)-( 4) is required. This paper overviews a recent approach that simplifies data fusion decisions for food authentication and adulteration classification purposes. This unique self-optimized fusion method avoids the confounding decisions by simultaneously evaluating any number of permutations of (1)-(3) in combination with a collection of non-optimized classifiers based on respective tuning parameter windows for (4). Self-optimization is obtained by using the sum of ranking differences (SRD) to form the final fused class membership decision for each new food test sample where the SRD was automatically optimized using a receiver operator characteristic (ROC) curve. The simple automatic SRD hybrid fusion/ROC curve process removes the myriad of subjective decisions imposed on the analyst and increases food classification reliability.Results are demonstrated with three datasets.

Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of Sample Similarity Dynamically Assessed with NIR Spectra

“…Many PRISM X measurements were used for food and beverage characterization. These include using mid-infrared, NIR, UV, and VIS full spectra and thermogravimetric data for beer product authentication, ICP-MS for authenticating fava beans from Santorini, mid-infrared to detect adulteration of strawberry puree with other fruits, contamination of clams with heavy metals, authentication of meat as either turkey or chicken, and microplastic identification . A collection of 13 chemical measurements were used to classify three wine cultivars grown in Italy .…”