Is my food safe? – AI-based classification of lentil flour samples with trace levels of gluten or nuts. (30th August 2022)

• Record Type: Journal Article
• Authors: Pradana-López, Sandra; Pérez-Calabuig, Ana M.; Otero, Laura; Cancilla, John C.; Torrecilla, José S.
• Is Part Of: Food chemistry. Volume 386(2022)
• Publication Date: 2022-08-30
• Subjects: Lentils -- Adulteration -- Nuts -- Gluten -- Image analysis -- ResNet34; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.132832
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Detection of potentially hazardous adulterants in lentil flour. Rapid, accurate, and inexpensive method to quantify traces of wheat and nuts. Needless of laborious sample preparation. Deep learning tool can be extrapolated to other food products. Abstract: An artificial intelligence-based method to rapidly detect adulterated lentil flour in real time is presented. Mathematical models based on convolutional neural networks and transfer learning (viz., ResNet34) have been trained to identify lentil flour samples that contain trace levels of wheat (gluten) or pistachios (nuts), aiding two relevant populations (people with celiac disease and with nut allergies, respectively). The technique is based on the analysis of photographs taken by a simple reflex camera and further classification into groups assigned to adulterant type and amount (up to 50 ppm). Two different algorithms were trained, one per adulterant, using a total of 2200 images for each neural network. Using blind sets of data (10% of the collected images; initially and randomly separated) to evaluate the performance of the models led to strong performances, as 99.1% of lentil flour samples containing ground pistachio were correctly classified, while 96.4% accuracy was reached to classify the samples containing wheat flour.