Elementomics combined with dd-SIMCA and K-NN to identify the geographical origin of rice samples from China, India, and Vietnam. (30th August 2022)

• Record Type: Journal Article
• Authors: Quinn, Brian; McCarron, Philip; Hong, Yunhe; Birse, Nicholas; Wu, Di; Elliott, Christopher T; Ch, Ratnasekhar
• Is Part Of: Food chemistry. Volume 386(2022)
• Publication Date: 2022-08-30
• Subjects: Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.132738
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: First elementomic determination of the country of origin of rice amongst China, India, and Vietnam based on ICP-MS elemental profiles. Using K-nearest neighbours to improve the accuracy of the ICP-MS elementomic models. Method developed to combat fraud in rice where flavour compounds are added to enhance the smell. 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 (R 2 = 0.8393, Q 2 = 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%).