Novel Hybrid Calibration Transfer Method Based on Nonlinear Dimensionality Reduction for Robust Standardization in Near-Infrared Spectroscopy

Abstract

To correct the spectral changes measured on different instruments in near-infrared (NIR) spectroscopy, a novel hybrid calibration transfer method with nonlinear dimensionality reduction based on direct standardization (DS) and autoencoder (AE), named DS-AE, is reported. First, DS was employed to preliminarily eliminate the spectral difference from the master and slave instruments. Next, the spectral features were extracted by AE to construct the partial least squares (PLS) calibration model. Compared with the linear dimensionality reduction methods, AE learns more latent features of the input spectra that are beneficial to reflect the chemical information of samples. Two NIR experiment datasets, including wheat and corn samples measured on different spectrometers, were employed to evaluate the performance of the DS-AE method. DS, piecewise direct standardization (PDS), canonical correlation analysis (CCA), principal components canonical correlation analysis (PC-CCA), and transfer via an extreme learning machine auto-encoder (TEAM) were introduced for comparative analysis with the proposed method. The results showed that DS-AE provided the lowest root mean squared error of prediction (RMSEP) for the wheat dataset; For the corn dataset, DS-AE provided lower RMSEP than DS, PDS, and CCA, and comparable with PC-CCA and TEAM. The score of the PLS principal components (PCs) describes the spectral differences of different instruments. The results indicated that the hybrid DS-AE method effectively corrected for the spectral variations. In summary, the proposed hybrid DS-AE method provided an alternative for robust standardization of near-infrared spectra measured on different instruments.