The combined optimization of Savitzky-Golay (SG) smoothing and multiplicative scatter correction (MSC) were discussed based on the partial least squares (PLS) models in Fourier transform near-infrared (FT-NIR) spectroscopy analysis. A total of 5 cases of separately (or combined) using SG smoothing and MSC were designed and compared for optimization. For every case, the SG smoothing parameters were optimized with the number of PLS latent variables ( ), with an expanded number of smoothing points. Taking the FT-NIR analysis of soil organic matter (SOM) as an example, the joint optimization of SG smoothing and MSC was achieved based on PLS modeling. The results showed that the optimal pretreatment was successively using SG smoothing and MSC, in which the SG smoothing parameters were 4th degree of polynomial, 2nd-order derivative, and 67 smoothing points, the best corresponding , RMSEP, and were 7, 0.3982 (%), and 0.8862, respectively. This result was far better than those without any pretreatment. The combined optimization of SG smoothing and MSC could obviously improve the modeling result for NIR analysis of SOM. In addition, a new method for the classification of calibration and prediction was proposed by normalization principle. The optimizations were done on this basis of this classification. 1. Introduction With the development of modern science and technology, near-infrared (NIR) spectroscopy analysis is widely applied to many fields, such as agriculture, food, environment, biomedicine, and so forth because of its quickness, easiness, no reagents, pollution-free process, and multicomponent simultaneous determination [1, 2]. Fourier transform near-infrared (FT-NIR) spectroscopy analysis is much powerful in signal processing and spectroscopy analyzing, which forms a good approximation of the original spectrum by curve fitting with a fewer-term Fourier series [3–6]. FT-NIR spectroscopy analysis is a technology extracting the component information from the experimental data. The large quantity of data with the higher dimension requires chemometric methods for the quantitative analysis. Partial least squares (PLS) is an effective dimension reduction method in near-infrared spectroscopy analysis. It is a widely used method of spectral modeling integrating principal component analysis and multiple linear regression. This method not only digs out the information of dependent variable but simultaneously also reduces the dimension of the spectral matrix [7–13]. The latent variables show the spectrum information of sample components, and the number of latent
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