Discriminating the geographical origins of chinese white lotus seeds by near-infrared spectroscopy and chemometrics

Abstract

The traceability of a Chinese white lotus seed (WLS) with Protected Designation of Origin (PDO) was investigated using near-infrared (NIR) spectroscopy and chemometrics. Three chemometrics methods, discrimination analysis (DA), class modeling, and a newly proposed strategy, the fusion of DA and class modeling, were investigated to compare their capacity to trace the geographical origins of WLS. Least squares support vector machine (LS-SVM) was developed to distinguish the PDO WLS from non-PDO WLS of four main producing areas. A class modeling technique, one-class partial least squares (OCPLS), was developed only using the data of PDO WLS. By the fusion of LS-SVM and OCPLS, the best prediction sensitivity and specificity were 0.900 and 0.973, respectively. The results indicate that fusion of DA and class modeling can enhance the specificity for detection of non-PDO products. The conclusion is that DA and class modeling should be combined for tracing food geographical origins.