【摘要】 采用偏最小二乘法(PLS)和光谱Savitzky-Golay(SG)平滑方法,建立甘蔗清糖浆锤度近红外光谱分析的优化模型。基于最优单波长模型预测效果划分定标集和预测集。全谱(400—2500nm)经过SG平滑处理后用PLS方法建模。建立计算机算法平台,把483种SG平滑模式和1—40的PLS因子数任意组合分别建立PLS模型,根据预测效果选出最优模型,最优模型的SG平滑模式为二阶导数平滑、4、5次多项式类型、43平滑点数,PLS因子数为13,预测均方根偏差(RMSEP)、相对预测均方根偏差(RRMSEP)和预测相关系数(rP)分别为0.433%、0.69%和0.978。预测精度很高,并且大幅度优于未做SG平滑处理直接PLS建模的预测效果。从而表明,SG平滑模式和PLS因子数的联合大范围筛选能够有效地应用于近红外光谱分析的模型优选。更多还原

【Abstract】 Using partial least squares(PLS) and Savitzky-Golay(SG) smoothing method,the optimal near infrared spectral analysis model for sugarcane simple syrup brix was established.The calibration set and the prediction set were divided based on the prediction effect of the optimal single wavelength model.The PLS model was established based on the whole region(400—2500nm) after SG smoothing.By building the computer algorithm platform,for any combination of 483 SG smoothing modes and 1—40 PLS factors,the PLS models were constructed respectively.According to the prediction effect to select the optimal model,and the SG smoothing mode of the optimal model is the second derivative smoothing,4 or 5 degree polynomial,43 smoothing points,the PLS factor is 13,RMSEP,RRMSEP and rP are 0.433%,0.69% and 0.978 respectively.The prediction accuracy is very high,and it is substantially superior to the prediction effect of the PLS model without SG smoothing,thus demonstrates that large-scale optimization combining SG smoothing modes and PLS factors can be effectively applied to the model optimization of near-infrared spectral analysis.更多还原