Rapid determination of moisture content in compound fertilizer using visible and near infrared spectroscopy combined with chemometrics

•Continuous wavelet transform as pre-processing transform for spectral signals.•Successive projections algorithm was used in continuous wavelet domain.•The CWT-SPA-LS-SVM model was established to determine moisture content in compound fertilizer. Moisture is a very important index for quality control of compound fertilizer. In this study, a rapid method to determine moisture content in compound fertilizer using visible and near infrared (Vis-NIR) spectroscopy combined with chemometrics is proposed. Continuous wavelet transform (CWT) with Daubechies family was used to preprocess the spectra of compound fertilizer. CWT was optimized by comparison the performance of partial least squares regression (PLSR) models. For comparison, raw spectra and three common pre-processing transforms, namely multiplicative scatter correction (MSC), first derivative (FD) and second derivative (SD) with Savitzky-Golay (SG) smoothing, were also carried out. Successive projections algorithm (SPA) was employed to select effective wavelengths with moisture under optimized continuous wavelet transforms. CWT-SPA-PLSR models were built with the effective wavelengths selected by SPA. Furthermore, CWT-SPA-LS-SVM model was developed with the best effective wavelengths. The results indicated that the optimized continuous wavelet transforms (db3 at scale 7, db4 at scale7 and db4 at scale 10) were superior; the best effective wavelengths were selected by SPA under db4 at scale 10; the performance of the CWT-SPA-LS-SVM model (R2 = 0.8054, RMSEP = 0.1795, RPD = 2.2672) is better than that of CWT-SPA-PLSR model (R2 = 0.7863, RMSEP = 0.1958, RPD = 2.0781). This study demonstrated visible and near infrared spectroscopy using continuous wavelet transform combined with successive projections algorithm and linear regression (PLSR) or nonlinear regression (LS-SVM) to determine moisture content in compound fertilizer was feasible.