食品科学 ›› 2011, Vol. 32 ›› Issue (6 ): 131-134.doi: 10.7506/spkx1002-6630-201106029

• 工艺技术 • 上一篇    下一篇

正交试验法优化水提紫甘薯色素废渣发酵酒工艺及模糊综合评判模型评价感官质量

殷建忠,王 琦,吴少雄,张雪辉,徐 芳,张丽娟,杨解顺,解入文   

  1. 昆明医学院营养与食品研究所
  • 收稿日期:2010-08-06 修回日期:2011-02-11 出版日期:2011-03-25 发布日期:2011-03-03
  • 通讯作者: 殷建忠 E-mail:yinjianzhong2005@sina.com
  • 基金资助:
    云南省科技厅项目(2008ZC107M)

Orthogonal Array Optimization of Wine Fermentation from Purple Sweet Potato Residue after Pigment Extraction and Sensory Evaluation by Fuzzy Comprehensive Evaluation Model

YIN Jian-zhong,WANG Qi,WU Shao-xiong,ZHANG Xue-hui,XU Fang,ZHANG Li-juan,#br# YANG Jie-shun,XIE Ru-wen   

  1. Research Institute of Nutrition and Food Science, Kunming Medical University, Kunming 650500, China
  • Received:2010-08-06 Revised:2011-02-11 Online:2011-03-25 Published:2011-03-03
  • Contact: YIN Jian-zhong E-mail:yinjianzhong2005@sina.com

摘要: 以水提紫甘薯色素废渣为主要原料,经发酵工艺制得发酵酒,应用模糊综合评判模型评价发酵酒成品。结果表明,发酵温度、料液比、蔗糖添加量、发酵时间是影响发酵酒的关键因素,采用正交试验L9(34)确定最佳工艺为发酵温度25℃、料液比1:25、蔗糖添加量22%、发酵时间12d。模糊综合评判模型评价结果,9个酒样的感官质量优劣顺序为6#>3#>5#>4#>7#>9#>2#>1#>8#。模糊综合评判结果准确、科学。

关键词: 紫甘薯, 废渣, 发酵酒, 模糊综合评判模型

Abstract: The residue of purple sweet potato after pigment extraction was used as the main raw material to produce wine. A fuzzy comprehensive evaluation model was adopted to evaluate the sensory quality of the prepared wine product. The results indicated that fermentation temperature, material-to-liquid ratio, sugar addition amount, fermentation time were key factors during the fermentation process of wine. Orthogonal array design was used to determine optimal fermentation processing parameters. The optimal processing parameters were fermentation temperature of 25 ℃, material-to-liquid ratio of 1:25, sugar addition amount of 22%, and fermentation time of 12 days. Fuzzy comprehensive evaluation showed that the sensory quality for 9 kinds of wine was ordered as follows: 6#>3#>5#>4#>7#>9#>2#>1#> 8#. This evaluation method was accurate and scientific.

Key words: purple sweet potato, residue, fermentation wine, fuzzy comprehensive evaluation model

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