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优质奶啤乳源酵母菌的筛选及发酵性能的研究 被引量:9

Study on screening of high quality dairy yeast for milk beer producing and fermentation characteristics
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摘要 以新疆乌鲁木齐地区采集的开菲尔粒作为分离源,进行了乳源酵母的分离、优质奶啤乳源酵母的筛选和发酵性能的研究工作。对分离的乳源酵母菌进行了发酵产物理化指标分析、遗传稳定性的检测和发酵产品感官、风味的分析,最后通过主成分分析法对不同乳源酵母菌发酵奶啤的特征进行综合评价。结果表明,在分离得到的17株乳源酵母菌中,酵母菌MJ1发酵24 h后,满足奶啤工业理化和卫生标准的要求,酒精生成量为3.88 g/L,发酵产品理化、感官和风味的主成分分析的评价结果最佳,遗传稳定,适宜奶啤生产发酵,经鉴定此酵母菌为马克斯克鲁维酵母(Kluyveromyces marxianus)。 Yeast species play an important role on the qualities of processed milk beer. The study seeks to screen and assess the fermentation performance of dairy yeast from Kefir gains products obtained from Urumchi, Xinjiang Province, China. The screening assessment criteria were based on colony morphology and evaluation of the product based on metabolism, genetic stability and sensory characteristics and vola- tile profile of the milk beer. In order to highlight the prominent features of each milk beer obtained from different yeasts, PCA was carried out using the quality index, sensorial attributes and the volatile profile. In all, 17 strains were screen out of which Kluyveromyces marxianus (MJ1)was identified to be the most suitable based on the assessment criteria. The results show that MJ1 fermentation products met the physical and hygiene quality standards of industrial milk beer product, alcohol production was 3.88 g/L. MJ1 was stable genetically. Based on its main attributes employed in the PCA (Principal component analysis), MJ1 was found to be the best yeast to be used in the production of milk beer.
作者 胡曼 王亮 巩小芬 王江月 刘朋龙 詹涛 李阳 HU Man WANG Liang GONG Xiaofen WANG Jiangyue LIU Penglong ZHAN Tao LI Yang(Jiangsu University, School of Food and Biological Engineering, Zhenjiang 212013, China Xinjiang Tianrun biotechnology co. LTD, Urumqi 830088, China)
出处 《中国乳品工业》 CSCD 北大核心 2017年第7期27-32,共6页 China Dairy Industry
基金 新疆生产建设兵团工业及高新技术科技攻关与成果转换计划项目(2015AB032) 江苏大学高级专业人才科研启动基金(12JDG069)
关键词 奶啤 乳源酵母菌 筛选 固相微萃取-气相色谱-质谱法 主成分分析 鉴定 milk beer yeast screening solid-phase micro-extraction-gas chromatography-mass spectrometry (SPME-GC-MS) principal component analysis (PCA) identification
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