三维荧光光谱结合平行因子及神经网络对清香型白酒的年份鉴别  

Year Discrimination of Mild Aroma Chinese Liquors Using Three-Dimensional Fluorescence Spectroscopy Combined with Parallel Factor and Neural Network

作　　者：朱焯炜[1,2] 阙立志[1] 陈国庆[1] 徐瑞煜[1] 朱拓[3] 

机构地区：[1]江南大学理学院,江苏无锡214122 [2]江南大学物联网工程学院,江苏无锡214122 [3]河海大学能源与电气学院,江苏南京210098

出　　处：《光谱学与光谱分析》2015年第9期2573-2577,共5页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61178032;61378037);江苏省科技支撑计划(社会发展)项目(BE2011828)资助

摘　　要：以某清香型白酒为研究对象,将三维荧光光谱技术与平行因子分析方法(parallel factor analysis,PARAFAC)、BP神经网络结合,建立清香型白酒年份鉴别模型。首先,利用FLS920全功能型荧光光谱仪测量获得不同年份白酒的三维荧光光谱数据,对激发发射三维矩阵进行三线性分解,得到四个主成分对应的浓度得分和激发-发射光谱轮廓图。将这4个浓度得分作为BP神经网络的输入,建立10,20和30年份白酒的鉴别模型。随机选取每个年份的10个样本,共30个样本组成测试集,剩余的90个白酒样本组成训练集建立训练模型。据此对未知样品进行预测,其预测正确率分别为90%,100%和100%。同时将该方法与多维偏最小二乘判别分析法(multi-way partial least squares discriminant analysis,NPLS-DA)进行了比较。研究结果表明:平行因子结合神经网络的判别模型具有更强的预测能力,该方法能够有效提取年份白酒的特征光谱信息,同时又降低了神经网络输入变量的维数,取得较好的鉴别效果。Three-dimensional fluorescence spectroscopy coupled with parallel factor analysis and neural network was applied to the year discrimination of mild aroma Chinese liquors.The excitation-emission fluorescence matrices （EEMs）of 120 samples with various years were measured by FLS920 fluorescence spectrometer.The trilinear decomposition of the data array was per-formed and the loading scores of and the excitation-emission profiles of four components were also obtained.The scores were employed as the inputs of the BP neural networks and the PARAFAC-BP identification model was constructed.10 samples were collected from 10,20 and 30 years of liquors respectively,and 30 samples were selected as the test sets.The remaining 90 sam-ples were used as the training sets to build the training model.The year prediction of unknown samples was also carried out,and the prediction accuracy was 90%,100% and 100%,respectively.Meanwhile,the discrimination analysis method and the multi-way partial least squares discriminant analysis were compared,namely PARAFAC-BP and NPLS-DA.The results indicated that parallel factor combined with the neural network （PARAFAC-BP）has higher prediction accuracy.The proposed method can ef-fectively extract the spectral characteristics,and also reduce the dimension of the input variables of neural network.A good year discrimination result was finally achieved.

关 键 词：年份鉴别 三维荧光光谱 平行因子 神经网络 

分 类 号：O657.34[理学—分析化学]