Elementomics combined with dd-SIMCA and K-NN to identify the geographical origin of rice samples from China, India, and Vietnam

Brian Quinn*, Philip McCarron, Yunhe Hong, Nick Birse, Di Wu, Christopher T. Elliott, Ratnasekhar Ch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The COVID-19 pandemic has impacted the food industry and consumers, with production gaps, shipping delays, and changes in supply and demand leading to an increased risk of food fraud. Rice has a high probability for adulteration by food fraudsters, being a staple commodity for more than half the global population, making the assessment of geographical origins of rice for authenticity important in terms of protecting businesses and consumers. In this study, we describe ICP-MS elemental profiling coupled with elementomic modelling to identify the geographical indications of Indian, Chinese, and Vietnamese rice. A PLS-DA model exhibited good discrimination (R2 = 0.8393, Q2 = 0.7673, accuracy = 1.0). Data-driven soft independent modelling of class analogy (dd-SIMCA) and K-nearest neighbours (K-NN) models have good sensitivity (98%) and specificity (100%).
Original languageEnglish
Article number132738
JournalFood Chemistry
Volume386
Early online date26 Mar 2022
DOIs
Publication statusPublished - 30 Aug 2022

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