摘要
葡萄酒带有浓厚的葡萄原产地地域特点与个性,快速准确地判别葡萄酒原产地具有重要意义,感官评定的方法存在一定的局限性。提出用贝叶斯信息融合技术将葡萄酒样品的近红外透射光谱及中红外衰减全反射光谱联立进行葡萄酒原产地判别的方法。分别用近、中红外光谱仪采集来自中国四个不同葡萄主栽产地(河北怀来、山东烟台、甘肃、河北昌黎)的153个葡萄酒样品的近红外透射光谱和中红外衰减全反射光谱,然后用偏最小二乘判别分析法(PLS-DA)分别建立基于近红外光谱和中红外光谱的葡萄酒产区判别模型;该模型输出的节点值归一化后作为所有样品分属每一类别的先验概率,代入Bayes判别公式得到后验概率,根据此概率判断样品的新类别属性,即用贝叶斯信息融合技术实现了两种判别结果的修正决策。近红外和中红外融合后的模型结果为:十次随机划分建模集和检验集,四产区葡萄酒判别模型建模集的平均准确率由78.21%(近红外)和82.57%(中红外)变为融合后的87.11%,检验集平均准确率由82.50%(近红外)和81.98%(中红外)变为融合后的90.87%,均优于单独采用一种光谱技术的判别结果。实验结果表明:信息融合技术有助于模型判别效果的提高,采用近、中红外光谱的贝叶斯信息融合技术对葡萄酒原产地进行快速识别是可行的。
Geographical origins of wine grapes are significant factors affecting wine quality and wine prices .Tasters’ evaluation is a good method but has some limitations .It is important to discriminate different wine original regions quickly and accurately . The present paper proposed a method to determine wine original regions based on Bayesian information fusion that fused near-in-frared (NIR) transmission spectra information and mid-infrared (MIR) ATR spectra information of wines .This method im-proved the determination results by expanding the sources of analysis information .NIR spectra and MIR spectra of 153 wine samples from four different regions of grape growing were collected by near-infrared and mid-infrared Fourier transform spec-trometer separately .These four different regions are Huailai ,Yantai ,Gansu and Changli ,which are all typical geographical originals for Chinese wines .NIR and MIR discriminant models for wine regions were established using partial least squares dis-criminant analysis (PLS-DA) based on NIR spectra and MIR spectra separately .In PLS-DA ,the regions of wine samples are presented in group of binary code .There are four wine regions in this paper ,thereby using four nodes standing for categorical variables .The output nodes values for each sample in NIR and MIR models were normalized first .These values stand for the probabilities of each sample belonging to each category .They seemed as the input to the Bayesian discriminant formula as a priori probability value .The probabilities were substituteed into the Bayesian formula to get posterior probabilities ,by which we can judge the new class characteristics of these samples .Considering the stability of PLS-DA models ,all the wine samples were di-vided into calibration sets and validation sets randomly for ten times .The results of NIR and MIR discriminant models of four wine regions were as follows :the average accuracy rates of calibration sets were 78.21% (NIR) and 82.57% (MIR) ,and the average accuracy rates of validation sets were 82.50% (NIR) and 81.98% (MIR) .After using the method proposed in this pa-per ,the accuracy rates of calibration and validation changed to 87.11% and 90.87% separately ,which all achieved better results of determination than individual spectroscopy .These results suggest that Bayesian information fusion of NIR and MIR spectra is feasible for fast identification of wine original regions .
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2014年第10期2662-2666,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(31101289)
北京市共建项目专项项目资助
关键词
葡萄酒
产地
PLS-DA
红外光谱
信息融合
Wine
Original regions
PLS-DA
Infrared spectroscopy
Information fusion
