E-Eye-Based Approach for Traceability and Annuality Compliance of Lentils

Abstract

In recent years, thanks to their numerous nutritional benefits, legumes have been rediscovered and have attracted interest from many consumers. However, these products, the most valuable ones traditionally produced in smaller communities in particular, can be objects of fraud; this is the case of Italian lentils, which, being a dry product, have a fairly long shelf life, but, due to the minimal visual changes that can affect them, it is possible that expired lentils may be sold alongside edible ones. The present work aims at creating a non-destructive method for classifying Italian lentils according to their harvest year and origin, and for discriminating between expired and edible ones. In order to achieve this goal, Red-Green-Blue (RGB) imaging, which could be considered as a sort of e-eye and represents a cutting-edge, rapid, and effective analytical method, was used in combination with a discriminant classifier (Sequential Preprocessing through ORThogonalization-Linear Discriminant Analysis, SPORT-LDA) to create novel testing models. The SPORT-LDA models built to discriminate the different geographical origins provided an average correct classification rate on the test set of about 88%, whereas an overall 90% accuracy was obtained (on the test samples) by the SPORT-LDA model built to recognize whether a sample was still within its expiry date or not.