Long-term isotopic model study for ecofriendly rice (Oryza sativa L.) authentication: Updating a case study in South Korea

Food Chem. 2021 Nov 15:362:130215. doi: 10.1016/j.foodchem.2021.130215. Epub 2021 May 26.

Abstract

To overcome the lack of consumer trust in ecofriendly products due to low reliability of ecofriendly certification and decreasing areas certified for growing ecofriendly agricultural products, alternative approaches for reliable certification are required. Isotopic-chemometric analysis has potential for determining organic authenticity, but previous studies have struggled to differentiate the authenticities of different rice types. The present study examined 5-year variations in δ13C and δ15N in ecofriendly and conventional rice sold at retail markets in South Korea, while assessing the feasibility of discriminant models for authentication of organic rice. Supporting vector machine analysis showed 4.4-14.6% better overall predictability of rice types than discriminant analysis and was effective in discriminating organic or conventional rice from pesticide-free rice, potentially enabling high-throughput screening to authenticate organic rice at marketplaces. Our findings provide reliable information for authenticating ecofriendly rice, with a potential to improve consumer safety and thus the confidence in organic products.

Keywords: Bulk stable isotope ratio; Discriminant model; Ecofriendly rice; Rice authenticity; Support vector machine.

MeSH terms

  • Carbon Isotopes / analysis*
  • Discriminant Analysis
  • Food Analysis / methods*
  • Food Analysis / statistics & numerical data
  • Food, Organic / analysis*
  • Nitrogen Isotopes / analysis*
  • Organic Agriculture
  • Oryza / chemistry*
  • Pesticides
  • Reproducibility of Results
  • Republic of Korea
  • Support Vector Machine

Substances

  • Carbon Isotopes
  • Nitrogen Isotopes
  • Nitrogen-15
  • Pesticides
  • Carbon-13