基于近红外透射光谱的汾阳王酒快速鉴别

Fast Discrimination of Adulterated Fenyangwang Wine Based on Near Infrared Spectroscopy

为了快速鉴别掺杂与伪劣清香型白酒,利用近红外(NIR)透射光谱分析技术结合化学计量学方法,以酒精度为53%的汾阳王酒为例,建立BP神经网络和最小二乘支持向量机(LS-SVM)鉴别分析模型。分别采集180份掺杂假冒和120份伪劣汾阳王酒样品的光谱数据,采用Savitzky-Golay(SG)卷积平滑法对光谱数据进行预处理,应用主成分分析(PCA)法分别提取了7个和11个主成分因子,然后采用BP神经网络和最小二乘支持向量机(LS-SVM)对未知样本进行了判别分析。结果表明,经SG-PCA-BP模型鉴别假冒伪劣的准确率均达到100%,SG-PCA-LS-SVM模型鉴别假冒伪劣的准确率分别为84.4%和83.3%。

In order to fast discriminate adulterated Fenyangwang wine,taking Fenyangwang wine with 53% vol of alcohol concentration as example,BP neural network and least squares support vector machine( LS-SVM) discriminant analysis models were established based on near infrared spectroscopy combined with chemometric methods.The spectra of 180 and 120 adulterated Fenyangwang wine samples were collected.Savitzky-Golay( SG) convolution smoothing was used to pre-treat the spectral data.Seven and eleven principal component factors were extracted respectively by using principal component analysis( PCA).Then,BP neural network and LS-SVM were used in discriminant analysis of unknown samples.Results showed that the accuracy of SG-PCA-BP neural network was up to 100%.The accuracy values of SG-PCA-LS-SVM models for two experimental groups were 84.4% and 83.3%,respectively.