Relationships of Overall Estery Aroma Character in Lagers with Volatile Headspace Congener Concentrations

Abstract

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 nonlinearities and interactions in ester perception gave the most robust prediction at R 2 = 0.88.

Flavour Instability of Pale Lager Beers: Determination of Analytical Markers in Relation to Sensory Ageing

“…Multivariate partial least squares regression techniques (PLSR) were employed to develop a model between the analytical data, represented in the X matrix, and the overall-ageing-score (OAS), represented in the Y matrix 23,49 . All used parameters with their corresponding code are summarised in Table XI.…”

“…To determine the most relevant parameters in relation to flavour instability, multivariate data analysis was performed on the analytical data and on the sensory evaluation of the fresh, forced aged and spontaneously aged beer samples of the six pale lagers. Multivariate partial least squares regression techniques (PLSR) were employed to develop a model between the analytical data, represented in the X matrix, and the overall-ageing-score (OAS), represented in the Y matrix 23,49 . All used parameters with their corresponding code are summarised in Table XI.…”

“…Due to large variations in the raw materials used and the applied production processes, it can be expected that the ageing behaviour will differ greatly between different pale lager beers. Therefore, partial least squares regression analysis (PLSR) 23,38,49 was applied, in order to reveal possible relationships between chemical data and sensory evaluation of beer ageing and to gain improved understanding of the ageing process of different commercial pale lager beers.…”

Beer Volatile Compounds and Their Application to Low-Malt Beer Fermentation

“…As an important component of sensory quality, the intensity of the ester based aroma characters in lager beer was evalu-BEER VOLATILE COMPOUNDS VOL. 106, 2008 321 ated using SPME and GC data (34). In SDME, a microdroplet is held in a large flowing aqueous phase or above an aqueous sample solution, and the analytes in the sample are extracted into the droplet.…”

Predicting acetic acid content in the final beer using neural networks and support vector machine

“…Secondly, in terms of beer classification, artificial neural networks could be very efficient in dealing with complex data of the electronic tongue based on different kinds of sensor arrays , volatile profile compounds , mineral contents and near‐infrared transflectance spectra . Thirdly, apart from the above mentioned applying fields, neural networks could also be successfully used for brand recognition , sensory evaluation and rapid detection of deviation from brand flavour specifications .…”