基于Pearson系数与多元核支持向量分类的葡萄酒分析
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国家自然科学基金资助项目(61144004)、全国统计科学重点资助项目(2013LZ52)、中国博士后科学基金资助项目(2012M520495)、广东省自然科学基金资助项目(S2013010014601、S2013010013212)和惠州市科技计划项目(2012P15)


Analysis of Wine Based on Pearson Coefficient and Multiple Kernel Support Vector Classification
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    摘要:

    选择Pearson相关系数筛选出与红葡萄酒各理化指标相关性较强的酿酒红葡萄理化指标,用逐步回归法建立回归方程确定了它们之间的数量关系。同时,采用多元核支持向量机对红葡萄酒样品进行分类,所分类别与人工口感评分所分类别基本相符,正确率达到91.89%,结论表明酿酒红葡萄和红葡萄酒的理化指标能很好地确定葡萄酒的口感评价。

    Abstract:

    Pearson correlation coefficient was used to choose some physicochemical indexes of grape which have strong correlation with those of wine and multi factor regression equations was established to determine their quantitative relations by the stepwise regression. Each physicochemical index of wine has a specific linear relationship with several physicochemical indexes of corresponding grape or just only one. At the same time, the multi-kernel support vector machine was carried out to classify the wine samples. The results from the multi-kernel support vector machine are approximately consistent with those from the artificial with an accuracy of 91.89%. Results from this study show that the physicochemical indexes of grape and wine can determine the taste evaluation of wine well.

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蒋辉,邓伟民,陈晓青.基于Pearson系数与多元核支持向量分类的葡萄酒分析[J].农业机械学报,2014,45(1):203-208. Jiang Hui, Deng Weimin, Chen Xiaoqing. Analysis of Wine Based on Pearson Coefficient and Multiple Kernel Support Vector Classification[J]. Transactions of the Chinese Society for Agricultural Machinery,2014,45(1):203-208.

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  • 收稿日期:2013-01-30
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  • 在线发布日期: 2014-01-03
  • 出版日期: 2014-01-03