Application of energy dispersive X-ray fluorescence spectrometry and near-infrared reflectance spectroscopy combined with multivariate statistical analysis for discriminating the geographical origin of soybeans

Abstract

Energy dispersive X-ray fluorescence spectrometry (ED-XRF) and near-infrared reflectance spectroscopy (NIRS), two non-destructive and rapid techniques, were explored to classify the geographical origin of soybeans. Multivariate statistical methods, including principal component analysis (PCA), discriminant analysis (DA), and modified partial least squares (MPLS), were applied as a chemometric tool to classify soybeans into two groups. A statistically significant difference was observed in the ED-XRF elemental determination of Al, P, S, Cl, Ca, Fe, Ni, Cu, Zn, Br, and Rb between domestic and imported soybeans. A total of 112 samples were almost perfectly classified according to their origin in the application of canonical DA with ED-XRF. NIRS based on DA application showed excellent classification results with 99.1% accuracy after optimizing spectral preprocessing. NIRS based on the MPLS model with leave-one-out cross-validation showed 100% prediction results using first or second derivative pretreatment of the raw spectrum with Salvitzy-Golay or Norris derivative smoothing techniques. The best calibration and validation statistics in the MPLS model for the root mean square error of calibration (RMSEC) and root mean square error of cross-validation (RMSECV) were found to be 0.132% (R2c = 0.9606) and 0.146% (R2cv = 0.9687), respectively. These results suggest that ED-XRF and NIRS combined with multivariate statistical analysis can be suitable technologies for the efficient determination of the geographical origin of soybeans.