Calibration transfer for near-infrared (NIR) spectroscopy based on local preserving projection

Abstract

Calibration transfer is a mathematical or statistical technique to solve the universality of near-infrared (NIR) spectroscopy, which is particularly important in practical applications. Therefore, a calibration transfer method based on local preserving projection (CTLPP) to correct spectral differences was proposed in this paper. Local preserving projection (LPP) is a linear feature extraction algorithm, which is a linear approximation of Laplacian Eigenmap manifold learning. It can not only overcome the shortcomings of linear feature extraction algorithms which are difficult to maintain the nonlinear manifold of the original data, but also solve the problem that nonlinear manifold learning methods are difficult to directly map new samples, which is conducive to building the conversion relationship between the source spectra and the target spectra. The transfer performance of the proposed method was evaluated in three datasets, and compared with five approaches, namely, piecewise direct standardization (PDS), calibration transfer based on independent component analysis (CTICA), spectral space transformation (SST), principal components canonical correlation analysis (PC-CCA) and calibration transfer based on neighborhood preserving embedding (CTNPE). Experimental results indicated that this proposed method can successfully correct the detection spectra among different apparatus. In addition, it can obtain relatively low prediction root mean square error (RMSEP). Therefore, the comprehensive research conducted in this work demonstrated that the proposed method is a promising calibration transfer method in NIR applications, which can provide a reference when sharing the NIR spectral models with more and more instruments.