Calibration Transfer for Near-Infrared (NIR) Spectroscopy Based on Neighborhood Preserving Embedding

Abstract

Calibration transfer is a subtle issue in the practical application of near-infrared (NIR) spectroscopy technique. In this paper, a novel method to calibration transfer based on neighborhood preserving embedding (CTNPE) for correcting spectral differences was proposed. As a manifold learning method, neighborhood preserving embedding (NPE) can not only capture the nonlinear manifold structure, but also retain the linearity and show good generalization ability. Since this approach can reveal low dimensional manifold structure in high dimensional spectroscopic data, it is beneficial to construct the transform relationship between source and target spectra. The performance of CTNPE was assessed and compared to that of piecewise direct standardization (PDS) and other four dimensionality reduction-based methods, including transfer based on target factor analysis (TTFA), spectral space transformation (SST), calibration transfer based on canonical correlation analysis (CTCCA) and based on independent component analysis (CTICA), in two real cases. The results indicated that CTNPE was able to successfully transfer spectra between instruments and samples in different physical states. Furthermore, CTNPE provided lower prediction errors than PDS, TTFA, CTCCA, SST, CTICA and direct prediction without a transfer function. Therefore, the comprehensive investigation carried out in the presented work demonstrates that CTNPE is a promising calibration transfer method for NIR, especially for correcting the variations for samples in different physical states.