Towards generalizable food source identification: An explainable deep learning approach to rice authentication employing stable isotope and elemental marker analysis

Abstract

In addressing the generalization issue faced by data-driven methods in food origin traceability, especially when encountering diverse input variable sets, such as elemental contents (C, N, S), stable isotopes (C, N, S, H and O) and 43 elements measured under varying laboratory conditions. We introduce an innovative, versatile deep learning-based framework incorporating explainable analysis, adept at determining feature importance through learned neuron weights. Our proposed framework, validated using three rice sample batches from four Asian countries, totaling 354 instances, exhibited exceptional identification accuracy of up to 97%, surpassing traditional reference methods like decision tree and support vector machine. The adaptable methodological system accommodates various combinations of traceability indicators, facilitating seamless replication and extensive applicability. This groundbreaking solution effectively tackles generalization challenges arising from disparate variable sets across distinct data batches, paving the way for enhanced food origin traceability in real-world applications.