CAJ | 학술논문

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。
포도주대유농후적포도원산지지역특점여개성，쾌속준학지판별포도주원산지구유중요의의，감관평정적방법존재일정적국한성。제출용패협사신식융합기술장포도주양품적근홍외투사광보급중홍외쇠감전반사광보련립진행포도주원산지판별적방법。분별용근、중홍외광보의채집래자중국사개불동포도주재산지（하북부래、산동연태、감숙、하북창려）적153개포도주양품적근홍외투사광보화중홍외쇠감전반사광보，연후용편최소이승판별분석법（PLS-DA ）분별건립기우근홍외광보화중홍외광보적포도주산구판별모형；해모형수출적절점치귀일화후작위소유양품분속매일유별적선험개솔，대입Bayes판별공식득도후험개솔，근거차개솔판단양품적신유별속성，즉용패협사신식융합기술실현료량충판별결과적수정결책。근홍외화중홍외융합후적모형결과위：십차수궤화분건모집화검험집，사산구포도주판별모형건모집적평균준학솔유78.21％（근홍외）화82.57％（중홍외）변위융합후적87.11％，검험집평균준학솔유82.50％（근홍외）화81.98％（중홍외）변위융합후적90.87％，균우우단독채용일충광보기술적판별결과。실험결과표명：신식융합기술유조우모형판별효과적제고，채용근、중홍외광보적패협사신식융합기술대포도주원산지진행쾌속식별시가행적。
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 .