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基于嗅覺可視技術的醋醅理化指標分析
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江蘇省自然科學基金資助項目(BK2012286)和中國博士后科學基金特別資助項目(2013T60509)


Analysis of Physicochemical Index of Vinegar Substrate Based on Olfactory Visualization Technique
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    摘要:

    通過分析56個不同發(fā)酵池種子醋醅乙醇脫氫酶(ADH)的活性與56個對應接種池的不揮發(fā)酸和總酸的關系,得出將種子ADH活性控制在600~700U/mL時,被接種池的醋醅品質較高。利用嗅覺可視化技術結合誤差反向傳播人工神經網絡(BP-ANN)模型,快速檢測種子ADH活性和預測被接種池不揮發(fā)酸及總酸質量分數。結果表明,BP-ANN模型預測ADH、不揮發(fā)酸、總酸的相關系數分別為0.7816、0.8447和0.9463。因此,嗅覺可視化技術結合BP-ANN可有效預測醋酸發(fā)酵過程中的理化指標。

    Abstract:

    Solid state fermentation is a distinctive technology for vinegarmaking. In general, the vinegar quality was evaluated by the contents of nonvolatile acid and total acid. The relationships between the contents of alcohol dehydrogenase (ADH) activity from 56 different fermentation seed tanks and the contents of nonvolatile acid and total acid from 56 different vaccination fermentation tanks were analyzed. The result showed that, when ADH activity values were controlled in 600~700U/mL, the vinegar substrate would showa good performance in quality. Whats more, the olfactory visualization sensor combined with backpropagation artificial neural network (BP-ANN) was used to test the ADH activity and predict nonvolatile acid and total acid. The result showed that the value of correlation coefficient of ADH activity, nonvolatile acid and total acid were 0.7816, 0.8447 and 0.9463, respectively. Therefore, the olfactory visualization sensor combined with BP-ANN could be well used in the prediction of physicochemical index for vinegar substrate.

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管彬彬,趙杰文,金鴻娟,林 顥.基于嗅覺可視技術的醋醅理化指標分析[J].農業(yè)機械學報,2015,46(9):223-227. Guan Binbin, Zhao Jiewen, Jin Hongjuan, Lin Hao. Analysis of Physicochemical Index of Vinegar Substrate Based on Olfactory Visualization Technique[J]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(9):223-227.

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  • 收稿日期:2015-03-10
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  • 在線發(fā)布日期: 2015-09-10
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