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 |
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Article number | 132738 |
Journal | Food Chemistry |
Volume | 386 |
Early online date | 26 Mar 2022 |
DOIs | |
Publication status | Published - 30 Aug 2022 |
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Dive into the research topics of 'Elementomics combined with dd-SIMCA and K-NN to identify the geographical origin of rice samples from China, India, and Vietnam'. Together they form a unique fingerprint.Student theses
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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 2023Student thesis: Doctoral Thesis › Doctor of Philosophy
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