e 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. e 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. is result was far better than those without any pretreatment. e 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 classi�cation of calibration and prediction was proposed by normalization principle. e optimizations were done on this basis of this classi�cation.

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A quantitative determination method for the diagnosis of hyperlipidemia was developed using Fourier transform infrared (FTIR) spectroscopy. Random forest (RF) was demonstrated as a potential multivariate algorithm for the FTIR analysis of low-density lipoprotein cholesterol (LDL-C) and tri-glycerides (TG) in human serum samples. The informative wavebands for LDL-C and TG were selected based on the Gini importance. The selected wavebands were mainly within the fingerprint region. The RF modeling results were better than those derived using PLS in validation process, because the chance for over-fitting was possibly eliminated in RF algorithm. ARF also demonstrated favorable results in the test process. The prospective model exhibited a higher than 90% true prediction in negative/positive properties for male and female samples. These clinical statistical results indicated the optimization of RF algorithm performed accurately in the FTIR determination of LDL-C and TG. RF is evaluated as a promising tool for diagnosing and controlling hyperlipidemia in populations. The parameter optimization methodology is useful in the improving model accuracy using FTIR spectroscopic technology.