Effects of Traceability Scope and Sample Quantity on Origin Tracing of Mineral Elements in Mung Beans

TL;DR

This study shows that using more mung bean samples from a wider area improves the accuracy of tracing their origin using mineral element analysis.

Contribution

The study demonstrates how traceability scope and sample quantity affect the accuracy of origin tracing models for mung beans.

Findings

The origin discrimination model accuracy increases with broader traceability scope, reaching 100% for Tailai–Sishui.

Larger sample quantities improve model accuracy, with 99.33% for 600 samples compared to 99.0% for 200 samples.

Abstract

Mineral element fingerprint analysis technology is one of the effective methods for grain origin identification. The accuracy of the identification model is closely related to sample origin, including traceability scope and sample quantity. In this study, mung bean samples from four sites (Tailai and Dorbod Mongol Autonomous County in Heilongjiang Province, Baicheng City in Jilin Province, and Sishui County in Shandong Province) were used as the research object. The contents of mineral elements in mung bean samples were determined by inductively coupled plasma mass spectrometry (ICP‐MS). Based on stoichiometric results, origin tracing models for different traceability scopes and different quantities of samples were established. The results show that the origin discrimination model established by the samples with different traceability scopes has the correct rate for the original discrimination of Tailai–Dorbod Mongol Autonomous (99.33%) < Tailai–Baicheng (99.67%) < Tailai–Sishui (100.0%). The origin discrimination model established by the different quantities has the correct rate for the original origin discrimination of 99.0% (n = 200) < 99.25% (n = 400) < 99.33% (n = 600). The results verified that the larger sample tracing scope and the larger sample quantity can improve the discrimination accuracy of the established origin tracing model.

Mineral element fingerprint analysis technology is one of the effective methods for grain origin identification. The accuracy of the identification model is closely related to sample origin, including traceability scope and sample quantity.

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