Towards generalizable food source identification : An explainable deep learning approach to rice authentication employing stable isotope and elemental marker analysis
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 113967 |
Journal / Publication | Food Research International |
Volume | 179 |
Online published | 3 Jan 2024 |
Publication status | Published - Mar 2024 |
Link(s)
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. © 2024 Elsevier Ltd
Research Area(s)
- Fraud detection, Geographical origin, Rice origin verification, Stable isotopes, Trace elements
Citation Format(s)
Towards generalizable food source identification: An explainable deep learning approach to rice authentication employing stable isotope and elemental marker analysis. / Chu, Yinghao; Wu, Jiajie; Yan, Zhi et al.
In: Food Research International, Vol. 179, 113967, 03.2024.
In: Food Research International, Vol. 179, 113967, 03.2024.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review