Calibration Transfer Based on Nonparametric Varying Coefficient Regression

Differences between near-infrared (NIR) spectroscopy instruments make it difficult to apply calibration models universally across multiple instruments; hence, calibration transfer (CT) is crucial. To ensure that a model developed on one instrument is also applicable to a new instrument, this study establishes a CT method based on nonparametric techniques, referred to as nonparametric varying-coefficient regression calibration transfer (NVT). This method uses a varying-coefficient model (VCM) to build a functional relationship between the master and slave spectra using a set of standard sample spectra and employs B-splines as basis functions for function fitting. This functional relationship helps transfer the slave spectra of other samples into the master spectra, reducing the spectral differences caused by instrument variations. The performance of NVT was tested on the determination of moisture, oil, protein, and starch in corn, and the content of total plant alkaloids, reducing sugars, total sugars, and total nitrogen in tobacco using NIR spectroscopy. NVT was compared with two common CT methods: spectral space transformation (SST) and piecewise direct standardization (PDS). The results show that NVT can effectively eliminate some spectral differences caused by different instruments and significantly improve analytical accuracy. Compared with that of PDS, the CT effect of NVT is significantly improved, whereas compared with that of SST, it is slightly improved. This method is insensitive to parameters, making it easy to select parameters and providing a new idea for CT method design.