Joint Optimization of Savitzky-Golay Smoothing Models and Partial Least Squares Factors for Near-infrared Spectroscopic Analysis of Serum Glucose
10.3724/SP.J.1096.2010.00342
- VernacularTitle:血糖近红外光谱分析的Savitzky-Golay平滑模式与偏最小二乘法因子数的联合优选
- Author:
Jun XIE
;
Tao PAN
;
Jiemei CHEN
;
Huazhou CHEN
;
Xiaohuan REN
- Publication Type:Journal Article
- Keywords:
Serum glucose;
Near-infrared spectroscopy;
Partial least squares;
Savitzky-Golay smoothing;
Dividing calibration set and prediction set
- From:
Chinese Journal of Analytical Chemistry
2010;38(3):342-346
- CountryChina
- Language:Chinese
-
Abstract:
The optimal model for the near-infrared spectroscopic analysis of serum glucose was established by partial least squares(PLS) and Savitzky-Golay(SG) smoothing method. Based on the prediction effect of the optimal single wave number model, a new dividing method for calibration set and prediction set was given. The calibration and prediction models were established by PLS method adopting the combination bands of 10000-5300 cm~(-1) and 4920-4160 cm~(-1) with Savitizky-Golay(SG) smoothing. By extending the number of smoothing points to 5, 7, ..., 87(odd) and polynomial degree to 2, 3, 4, 5, 6, fourteen smooth coefficient tables including 582 smooth modes were calculated. All PLS models corresponding to all smooth modes and all PLS factors(1-40) were constructed. The optimal model was selected by the prediction effect. And the derivation order was 1, the polynomial degree was 3 or 4, the number of smoothing points was 53, the optimal factor was 7 and the optimal RMSEP reach 0.376 mmol/L. The dividing method for calibration set and prediction set, the extending of SG smoothing modes, large-scale optimization combining SG smoothing modes and PLS factors can be effectively applied for the model optimization of near-infrared spectroscopic analysis.
