Model optimization for geographical discrimination of Lentinula edodes based stable isotopes and multi-elements in China

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Abstract

Geographical origin discrimination of agricultural products is essential to guarantee food safety and fair trade. Lentinula edodes (Shiitake mushroom) samples cultivated in three major production regions in China were characterized using stable isotopes (δ¹³C, δ²H, δ¹⁸O, δ¹⁵N, and δ³⁴S) and elemental contents (%C, %N, %S, %protein and C/N ratios). Results showed that 9 of 10 analyzed Shiitake variables had significant differences among production regions. Due to the arid/dry climate and a large day-night alternating temperature difference, the protein content of L. edodes in northeast China is higher than in northern and southeast China. Sulfur isotopes (δ³⁴S) are useful indicators of origin for L. edodes in China and to evaluate its protein quality. Hydrogen and oxygen isotopes (δ²H and δ¹⁸O) are most effective for regional discrimination. Two PLS-DA(partial least-squares discriminate analysis) models were developed; one using only five stable isotopes (Model 1) and the other using five stable isotopes and three elemental contents (Model 2), respectively. Model 2 had a discrimination accuracy of 95.2%, which was higher 10% than Model 1. Chemometric classification models using chemical elements and multi-isotopes provide a useful method to authenticate Chinese Shiitake origins.