葡萄酒发酵过程主要参数近红外光谱分析  

NIR Spectroscopy Analysis of Main Parameters in Red Wine Fermentation

作　　者：张树明[1] 杨阳[1] 梁学军 李景明[1] 倪元颖[1] 

机构地区：[1]中国农业大学食品科学与营养工程学院,北京100083 [2]怀来龙徽庄园葡萄酒有限公司,北京100143

出　　处：《农业机械学报》2013年第1期152-156,共5页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金资助项目(30971979);农业部'948'引进项目(2004-Z33)

摘　　要：用近红外光谱结合化学计量学方法对葡萄酒酒精发酵中葡萄糖、果糖、乙醇和甘油4个指标进行了定量分析,化学指标的测定采用高效液相色谱法。通过对近红外数据进行筛选、变量标准化等预处理,比较了主成分回归和偏最小二乘回归定量分析的模型质量,以决定系数、校正均方根误差、预测均方根误差为模型质量的评价指标。通过比较发现,对于葡萄糖和果糖,主成分回归与偏最小二乘回归的预测精度相当;对于乙醇,主成分回归预测结果较优;对于甘油,偏最小二乘回归的预测结果要优于主成分回归。主成分回归所采用的成分数要多于偏最小二乘回归,但二者都可以用于上述4种成分的定量分析,其预测精度也相近。The glucose, fructose, ethanol and glycerol were predicted by using NIR spectroscopy and multivariate calibration methods. And the reference values were analyzed by HPLC to evaluate four components above. The principle component analysis regression （PCR） and partial least squares regression （PLSR） were compared. The correlation coefficient of calibration （R^2） , the root mean square error of calibration （RMSEC） and the root mean square error of prediction （RMSEP） were used to evaluate the models. Results from the application of PCR and PLSR were presented, showing there were no significant differences between PCR and PLSR to predict glucose, fructose and ethanol. While for the glycerol, it was better to use the PLSR. PLSR almost always required fewer latent variables than PCR, but this did not appear to influence predictive ability.

关 键 词：葡萄酒 发酵参数 无损检测 近红外光谱 偏最小二乘回归 

分 类 号：O657.33[理学—分析化学] TS261.43[理学—化学]