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

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. © 2015 lu xu et al.