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基于Pearson系數(shù)與多元核支持向量分類的葡萄酒分析
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國(guó)家自然科學(xué)基金資助項(xiàng)目(61144004)、全國(guó)統(tǒng)計(jì)科學(xué)重點(diǎn)資助項(xiàng)目(2013LZ52)、中國(guó)博士后科學(xué)基金資助項(xiàng)目(2012M520495)、廣東省自然科學(xué)基金資助項(xiàng)目(S2013010014601、S2013010013212)和惠州市科技計(jì)劃項(xiàng)目(2012P15)


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

    選擇Pearson相關(guān)系數(shù)篩選出與紅葡萄酒各理化指標(biāo)相關(guān)性較強(qiáng)的釀酒紅葡萄理化指標(biāo),用逐步回歸法建立回歸方程確定了它們之間的數(shù)量關(guān)系。同時(shí),采用多元核支持向量機(jī)對(duì)紅葡萄酒樣品進(jìn)行分類,所分類別與人工口感評(píng)分所分類別基本相符,正確率達(dá)到91.89%,結(jié)論表明釀酒紅葡萄和紅葡萄酒的理化指標(biāo)能很好地確定葡萄酒的口感評(píng)價(jià)。

    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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蔣輝,鄧偉民,陳曉青.基于Pearson系數(shù)與多元核支持向量分類的葡萄酒分析[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),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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  • 在線發(fā)布日期: 2014-01-03
  • 出版日期: 2014-01-03