黄酒糖度预测的可见-近红外光谱方法研究

提出了用可见近红外光谱结合不同化学计量学方法预测黄酒糖度的新方法。用240个黄酒样本建模,60个样本进行预测。通过对光谱数据进行平滑、变量标准化及一阶导数等预处理,建立并比较了偏最小二乘法,小波变换与偏最小二乘法相结合,主成分分析与人工神经网络相结合以及主成分分析与最小二乘支持向量机相结合四种不同建模方法的预测精度,以相关系数r、预测标准差、偏差等为评判标准,得到黄酒糖度预测的最优模型为最小二乘支持向量机模型。该模型对黄酒糖度预测的相关系数为0.962、预测标准差为0.021、偏差为-0.001,获得了理想的预测精度。结果表明应用可见近红外光谱对黄酒糖度进行预测是可行的,且最小二乘支持向量机模型能得到最优的预测结果。

Methods for the Prediction of Sugar Content of Rice Wine Using Visible-Near Infrared Spectroscopy

A new approach of using visible-near infrared spectroscopy combined with different chemometric methods was investigated for the prediction of sugar content of rice wine.240 wine samples were used for the calibration set,while 60 for the validation set.After some pretreatments of smoothing,standard normal variate(SNV) and first derivative to the spectral data,four different models were developed and the precision were compared,including partial least squares(PLS),combination of wavelet transform and PLS(WT-PLS),combination of principal component analysis and artificial neural network(PCA-ANN),and combination of PCA and least squares-support vector machine(PCA-LS-SVM).According to the standards of correlation coefficient(r),the root mean square error of prediction(RMSEP) and bias,a best calibration model of PCA-LS-SVM was achieved for the prediction of sugar content of rice wine.The correlation coefficient r=0.962,RMSEP=0.021 and Bias=-0.001 by PCA-LS-SVM,and a satisfying prediction precision was achieved.The results indicated that visible-near infrared spectroscopy could be successfully applied for the prediction of sugar content of rice wine and PCA-LS-SVM model could achieve a best prediction results.