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首页> 外文期刊>Journal of the Institute of Brewing >Relationships of Overall Estery Aroma Character in Lagers with Volatile Headspace Congener Concentrations
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Relationships of Overall Estery Aroma Character in Lagers with Volatile Headspace Congener Concentrations

机译:啤酒中总体总体香气特征与挥发性顶空同源物浓度的关系

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In lager beers the intensity of "estery" aroma character is regarded as an important component of sensory quality, but its origins are somewhat uncertain. Overall "estery" aroma intensity was predicted from capillary gas chromatographic (GC) data following solid phase micro extraction (SPME) of headspaces. Estery character was scored in 23 commercial lagers using rank-rating, allowing assessors (13) constant access to a range of appropriate standards. From univariate data analysis, all assessors behaved similarly and lagers fell into three significantly different groups: low (1), high (1) and intermediate (21). The quantification of 36 flavour volatiles by SPME of headspaces was reproducible and principal component analysis explained 91% total variance. Multiple linear regression could utilise only a restricted (26) set of flavour volatiles, whereas partial least square regression, that considered all flavour components, showed significant differences and improved prediction. However, an artificial neural network that could compensate for non-linearities and interactions in ester perception gave the most robust prediction at R~2 = 0.88.
机译:在大啤酒中,“香气”香气特征的强度被认为是感官品质的重要组成部分,但其来源尚不确定。在顶空进行固相微萃取(SPME)后,根据毛细管气相色谱(GC)数据预测总体“酯”香气强度。在23个商业lager中使用等级评分对Estery字符进行评分,使评估人员(13)可以不断获得一系列适当的标准。从单变量数据分析来看,所有评估者的行为都相似,且滞后者分为三个明显不同的组:低(1),高(1)和中级(21)。通过SPME顶空对36种香精挥发物的定量可重现,主成分分析解释了91%的总方差。多元线性回归只能利用有限的(26)种风味挥发物,而考虑所有风味成分的偏最小二乘回归显示出显着差异并改善了预测。然而,可以补偿酯感测中的非线性和相互作用的人工神经网络在R〜2 = 0.88时给出了最可靠的预测。

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