Elementomics combined with dd-SIMCA and K-NN to identify the geographical origin of rice samples from China, India, and Vietnam.
Food Chem
; 386: 132738, 2022 Aug 30.
Artigo
em Inglês
| MEDLINE | ID: covidwho-1748003
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%).
Texto completo:
Disponível
Coleções:
Bases de dados internacionais
Base de dados:
MEDLINE
Assunto principal:
Oryza
/
COVID-19
Tipo de estudo:
Estudo prognóstico
Limite:
Humanos
País/Região como assunto:
Ásia
Idioma:
Inglês
Revista:
Food Chem
Ano de publicação:
2022
Tipo de documento:
Artigo
País de afiliação:
J.foodchem.2022.132738
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