Continuous Wavelet Transform Combined with Parametric-Free Calibration Enhancement Framework for Calibration of Time-shift Near-infrared Spectra

Near-infrared (NIR)spectroscopy is a highly efficient and non-destructive technology applied in many areas. The measured spectra, however, are susceptible to shift due to various external interferences, resulting in biased analytical results. Time-shifts in NIR spectra are typical phenomena of continuously shifting spectra with measuring time, which is common in practical applications. In this study, a new method for modeling the time-shift NIR spectra was proposed, in which the shifting signal was divided into shifting background and sample-dependent shifts, and then corrected by continuous wavelet transform (CWT)and semi-supervised parameter-free calibration enhancement (SS-PFCE),respectively. The efficiency of the method was evaluated by modeling 1941 time-shift NIR spectra of soil samples collected in Yunnan Province (China)in 2019 and 2020 to predict the soil organic matter (SOM)content. The spectra taken in 2019 were calibrated (Root mean squared error of prediction (RMSEP)= 6.7 g/kg,R-2 = 0. 76)and a large deviation was observed in predicting the spectra taken in 2020 (RMSEP = 31. 3 g/kg,R-2 = 0. 50). After CWT treatment, the prediction of spectra measured in 2020 was significantly promoted by predicting the spectra (RMSEP = 11.6 g/kg,R-2 = 0. 66),and the prediction was further improved by SS-PFCE (RMSEP = 8. 3 g/kg,R-2 = 0. 67). The results indicated that CWT and SS-PFCE were efficient for eliminating the shifting background and sample-related shifts in time-shift NIR spectra, respectively.