近红外光谱和电子鼻技术用于葡萄酒发酵过程中酒精度的定量分析

采用近红外光谱和电子鼻对葡萄酒的酒精发酵过程进行了动态采样检测，通过主成分回归和偏最小二乘回归对酒精度变化进行了监控和预测研究。分别建立了近红外光谱、电子鼻以及二者融合数据对酒精度定量分析的主成分回归和偏最小二乘回归模型。结果表明，近红外光谱数据和电子鼻数据的主成分回归和偏最小二乘回归模型的相关系数(r)均大于0.99，但校正均方根误差(RMSEC)和预测均方根误差(RMSEP)较大。近红外光谱和电子鼻数据融合后，模型质量得到提高，建立的偏最小二乘模型r为0.999 2，RMSEC和RMSEP分别降低为0.206%和0.205%(v/v)，定量精度较高。近红外光谱和电子鼻均适用于红酒发酵过程中对酒精度的定量分析，且二者结合应用能提高定量精度。

Combination of Near Infrared Spectroscopy and Electronic Nose for Alcohol Quantification during the Red Wine Fermentation

The red wine fermentation needs fast and nondestructive techniques, which can help to control the fermentation process and assure the quality of wine. In the present study, near infrared spectroscopy (NIR) and electronic nose (EN) were used to predict the alcohol content during the red wine alcoholic fermentation. Calibration models were developed between instrumental data and chemical analysis using principal component regression (PCR) and partial least squares regression (PLSR) with cross validation. Good correlations (R>0.99) were acquired for both the models developed by the NIR and EN data. However, RMSEC and RMSEP were a little larger. Combining NIR and EN can optimize the model and improve the prediction accuracy. The PLSR model based on combined data shows the best correlation (R=0.999 2), with RMSEC and RMSEP being 0.206 and 0.205% (v/v), respectively. Both NIR spectroscopy and EN can predict the alcohol concentration during the alcoholic fermentation of red wine, and the combination of two instruments can improve the analysis precision. Although the measurements were carried out in off-line mode, this study demonstrates that NIR and EN can be used as on line, fast, nondestructive and in time techniques to provide in-time information about the fermentation process and to assure the quality of final products.