Joint Optimization of Savitzky-Golay Smoothing Modes and PLS Factors Was Applied to Near Infrared Spectral Analysis of Serum Cholesterol

Abstract

The rapid determination method of blood clinical biochemical indicators based on near infrared spectral (NIRS) analysis is an important research branch in health monitoring systems. In this paper, the rapid determination method and the optimal analysis model of serum cholesterol were established by using the NIRS technology, partial least squares (PLS) and Savitzky-Golay (SG) smoothing method. Based on the prediction effect of the optimal single wavenumber model, calibration set and prediction set were divided. The calibration and prediction models were established by using PLS method adopting the combination bands of 10000-5300 cm-1 and 4920-4160 cm-1 with SG smoothing. By extending the number of smoothing points to 5, 7... 61 (odd) and polynomial degree to 2, 3, 4, 5, 6, fourteen smoothing coefficient tables including 400 SG smooth modes were calculated. Based on computer algorithms platform which was developed by authors, PLS models corresponding to all combinations of 400 SG smooth modes and 1-40 PLS factors were constructed. The optimal model was selected according to the prediction effect, and the derivation order is 1, the polynomial degree is 3 or 4, the number of smoothing points is 43, the optimal PLS factor is 13, the prediction correlation coefficient RP is 0.811, and the optimal RMSEP reaches 0.416 mmol/L. The dividing method for calibration set and prediction set, the extending of SG smoothing modes, large-scale joint optimization of SG smoothing modes and PLS factors can be effectively applied to the model optimization of NIRS analysis.