刘 巍, 战吉宬, 黄卫东, 李德美, 刘国杰. 基于近红外光谱技术的葡萄酒原产地辨识方法[J]. 农业工程学报, 2010, 26(13): 374-378.
    引用本文: 刘 巍, 战吉宬, 黄卫东, 李德美, 刘国杰. 基于近红外光谱技术的葡萄酒原产地辨识方法[J]. 农业工程学报, 2010, 26(13): 374-378.
    Liu Wei, Zhan Jicheng, Huang Weidong, Li Demei, Liu Guojie. Application of near infrared spectroscopy and clustering analysis to classify wines from different origins[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(13): 374-378.
    Citation: Liu Wei, Zhan Jicheng, Huang Weidong, Li Demei, Liu Guojie. Application of near infrared spectroscopy and clustering analysis to classify wines from different origins[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(13): 374-378.

    基于近红外光谱技术的葡萄酒原产地辨识方法

    Application of near infrared spectroscopy and clustering analysis to classify wines from different origins

    • 摘要: 该文以鉴别葡萄酒原产地为目的,利用快速无损的近红外光谱分析技术,对47份来自昌黎、沙城和法国波尔多(Bordeaux)的红葡萄酒样品进行逐步回归分析选取光谱区域,再进行主成分分析和聚类识别,建立了判别葡萄酒原产地的预测模型。试验结果表明:昌黎、沙城和波尔多产地的葡萄酒产地鉴别的光谱区域为1 400~1 550 nm 和2 000~ 2 300 nm;3个产地的葡萄酒在主成分特征空间中基本为独立分布,其中波尔多酒样和国内酒样间距离最远,国内昌黎和沙城酒样间有小部分交叉;利用9个独立预测集样本对由38个训练集样品所建立的预测模型进行验证,产地的正确识别率达到了88.9%。因此,应用近红外光谱分析技术可准确、快速地辨别葡萄酒的产地。

       

      Abstract: The quick, lossless and green near-infrared spectroscopy (NIRS) technology was explored to classify red wines according to their geographical origins namely Changli, Shacheng from China and Bordeaux from France. The NIRS preprocessed data collected from 47 red wine samples were analyzed by using stepwise regression analysis (SAR), principal components analysis (PCA) and cluster analysis (CA), and a prediction model of red wine geographical origin was established for discriminant analysis. The test result showed that the feature spectrum of the red wines of Changli, Shacheng and Bordeaux was 1 400–1 550 nm and 2 000–2 300 nm. The PCA space of red wines from three origins was basically independent distribution. It was the longest distance of spectra between the samples from Bordeaux and domestic origin. There was a little cross part between Changli and Shacheng samples. The clustering analysis model by the Ward’s method based on 38 samples was used to predict the 9 unknown samples. The origin recognition rate of 88. 9% was achieved. It is concluded that the NIRS technology is both accurate and stable for geographical origin wines discrimination.

       

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