摘要
稳定性好，实用性强的NIRS模型，需要收集代表性强的样品并进行大量的化学值检测工作，为了减少建模的工作量，本文尝试用酒精水溶液的NIRS模型预测葡萄酒发酵液中酒精度的含量。通过遗传算法选择相关性高而且受其他干扰因素影响少的波段（2 245～2 320 nm）建立模型，并根据斜率/截距校正法原理，在预测集中选择能够代表样品酒精度变化范围的样品，对其进行校正，得到新模型的Slope=0.9808和Bias=0.5233。最后，对葡萄酒发酵液剩余样品的酒精度进行预测，预测的相关系数r达到0.99以上，预测相对分析误差（RPD）为11.71，相对标准差（RSD）为3.11%。由此表明，用酒精水溶液的NIRS模型，通过波段选择以及模型校正，预测葡萄酒发酵液的酒精度具有良好的可行性。此方法大大减小了NIRS技术建模的工作量。
The robust NIRS model must be developed by the representative samples and precise chemical values, taking much of work. To reduce the calibration work, the present paper explored the NIRS model developed using ethanol liquor to predict ethanol of the wine samples. The authors used the gene arithmetic （GA） method to select the calibration region（2 245-2 320 nm） which has relatively high correlation with the consistency of ethanol in ethanol liquor and has little interfere by other components in wine. To remove the systematic error between the calibration set of ethanol liquor and the prediction set of turbid vinous ferment liquid, according to the method of slope/bias, the authors selected 21 samples in prediction set which can represent the range of consistency of vinous ferment liquid to revise the ethanol model in order to predict the remaining wine samples well. After the calculation, the authors obtained the bias and the slope to be 0. 523 3 and 0. 980 8, respectively. Then we predicted the other turbid samples of wine using the ethanol liquor model after being revised by the slope/bias method. And the prediction model for the ethanol of turbid samples was developed, with r, RPD and RSD for the prediction model for ethanol of samples being 0. 99%, 11.71% and 3. 11%, respectively, indicating that the ethanol liquor model is robust and can serve as the model of vinous ferment liquid to detect the ethanol of the wine. So this method can largely reduce the calibration work during the NIR calibration process, and has the practical feasibility and application value.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2009年第7期1805-1808,共4页
Spectroscopy and Spectral Analysis
基金
国家"十一五"科技支撑计划项目(2006BAD11A12-08)资助
