Exploring supervised neighborhood preserving embedding (SNPE) as a nonlinear feature extraction method for vibrational spectroscopic discrimination of agricultural samples according to geographical origins.

Supervised neighborhood preserving embedding (SNPE), a nonlinear dimensionality reduction method, was employed to represent near-infrared (NIR) and Raman spectral features of agricultural samples (Angelica gigas, sesame, and red pepper), and the newly constructed variables were used to discriminate their geographical origins. This study was done to evaluate the potential of SNPE for recognizing minute spectral differences between classes by preserving local relationships, in comparison with widely adopted linear feature representation methods such as principal component analysis (PCA) and partial least squares (PLS). For this purpose, diffuse reflectance NIR spectral datasets of Angelica gigas, sesame, and red pepper, and a Raman spectral dataset of the same red pepper were prepared. The spectra were represented into new variables in reduced dimensions by PCA, PLS, neighborhood preserving embedding (NPE), and SNPE, and the represented variables were used to determine the geographical origins of samples by using the k-nearest neighbor (k-NN) and support vector machine (SVM). The combination of SNPE and SVM differentiated the geographical origins with improved accuracy. Overall results demonstrate that SNPE is a valuable alternative feature representation method, especially when complex and highly overlapping vibrational spectra are used for analysis.