Geographical origin traceability of rice using a FTIR-based metabolomics approach

Abstract

Infrared spectroscopy is a crucial tool to achieve the origin traceability of rice, but it is constrained by data mining. In this study, a novel infrared spectroscopy-based metabolomics analytical method was proposed to discriminate rice products from 14 Chinese cities by seeking ‘wave number markers’. Principal component analysis (PCA), cluster analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to separate all rice groups. The S-plot, permutation test and variable importance in projection (VIP) are used to screen eligible ‘markers’, which were further verified by a pairwise t-test. There are 55–265 ‘markers’ picked out from 14 rice groups, with their characteristic wave number bands to be 2935.658–3238.482, 3851.846–4000.364, 3329.136–3518.160, 1062.778–1213.225, 1161.147–1386.819, 3348.425–3560.594, 3115.038–3624.245, 2567.254–2872.007, 3334.923–3560.594, 3282.845–3543.235, 3338.780–3518.160, 3197.977–3560.594, 3163.258–3267.414 and 3292.489–3477.655 cm⁻¹, respectively. All but No. 5 rice groups show significantly low absorbance on their ‘marker’ bands. A mixed rice containing congenial No. 5 and No. 6 rice (80 : 20, m/m) was employed to test the validity of the method, and found that the ‘marker’ band of the mixed rice is the range of 1170.791–1338.598 cm⁻¹, implying the existence of considerable discrepancy between the mixed rice and other rice. The results indicate that infrared spectroscopy coupled with metabolomics analysis is competent for origin traceability of rice; thus, it provides a novel and workable approach for the accurate and rapid discrimination of rice from different geographical origins, and a distinctive perspective of metabolomics to explore infrared spectroscopy and beyond, especially not confined in the field of origin traceability.