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

doi:10.1016/j.foodchem.2022.132738

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 journal › Article › peer-review

18 Citations (Scopus)

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 language	English
Article number	132738
Journal	Food Chemistry
Volume	386
Early online date	26 Mar 2022
DOIs	https://doi.org/10.1016/j.foodchem.2022.132738
Publication status	Published - 30 Aug 2022

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Advanced mass spectrometry coupled with chemometric data analysis for innovative food authenticity measurement
Hong, Y. (Author), Elliott, C. (Supervisor) & van Ruth, S. (Supervisor), Dec 2023
Student thesis: Doctoral Thesis › Doctor of Philosophy

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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%).",

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T1 - Elementomics combined with dd-SIMCA and K-NN to identify the geographical origin of rice samples from China, India, and Vietnam

AU - Quinn, Brian

AU - McCarron, Philip

AU - Hong, Yunhe

AU - Birse, Nick

AU - Wu, Di

AU - Elliott, Christopher T.

AU - Ch, Ratnasekhar

PY - 2022/8/30

Y1 - 2022/8/30

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AB - 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%).

U2 - 10.1016/j.foodchem.2022.132738

DO - 10.1016/j.foodchem.2022.132738

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VL - 386

JO - Food Chemistry

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

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Advanced mass spectrometry coupled with chemometric data analysis for innovative food authenticity measurement

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