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Relationships of Overall Estery Aroma Character in Lagers with Volatile Headspace Congener Concentrations
Author(s) -
Techakriengkrai Ittipon,
Paterson Alistair,
Taidi Behnam,
Piggott John R.
Publication year - 2006
Publication title -
journal of the institute of brewing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.523
H-Index - 51
eISSN - 2050-0416
pISSN - 0046-9750
DOI - 10.1002/j.2050-0416.2006.tb00706.x
Subject(s) - aroma , flavour , principal component analysis , chemistry , chromatography , linear regression , congener , solid phase microextraction , univariate , character (mathematics) , regression analysis , statistics , food science , mathematics , gas chromatography–mass spectrometry , multivariate statistics , mass spectrometry , geometry
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 rankrating, 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.