Relationship of Sensory Staleness in Two Lagers to Headspace Concentrations oftrans-2-Nonenal and Three Staling Aldehydes

Abstract

Key compounds in lager staling include furfural, hexanal, 5-hydroxymethyl furfural (5-HMF), and trans-2-nonenal. Quantitative data of headspace concentration in two lagers -one premium at 5% (abv), the other a standard product at 4% (abv) -were obtained by solid phase microextraction (SPME) followed by gas chromatography using a mass selective detector (GCMS). The concentrations of the aldehydes were used to predict overall stale scoring from sensory assessor data, of lagers stored at 4, 12, 30, and 37°C for 7, 14, 21 and 28 days. Concentrations of all four aldehydes increased with time of storage and with higher temperatures. Correlation coefficients for prediction of staleness in the premium lager were similar at 0.81 and 0.84 for partial least square regression (PLS1) and artificial neural network (ANN) modelling respectively, and the latter showed a lower root mean square error (RMS error). For the standard product, the correlation coefficients were 0.72 and 0.86, with ANN showing lower RMS error respectively. In both PLS models, E-2-nonenal had high regression coefficients and 5-HMF lower coefficients. Furfural and hexanal differed in contributions to the lagers.

A Discussion of Polyphenols in Beer Physical and Flavour Stability

“…Other carbonyls have also been used as chemical indicators of beer oxidative flavour development even though they typically exist at concentrations below the human detection threshold in beer. Compounds such as acetaldehyde, 2-furaldehyde (furfural), 5-hydroxymethyl-2-furaldehyde (5-hydroxymethyl furfural or 5 -HMF) and β-damascenone are considered useful chemical stalingindicators because their concentrations increase alongside increases in oxidative flavours during beer aging 70,103,112 . To date more than 700 compounds have been reported in various beer types 66 with specific volatiles resulting from a multitude of aging reactions: the Maillard reaction, the formation of linear aldehydes and esters, ester degrada-tion, acetal formation, etherification, degradation of hop bittering acids and presence of phenolic compounds 54,64,81,117 .…”

Sensory Beer Aging

“…Table 1 lists the major carbonyl compounds formed during beer aging that cause the development of old beer taste, including their thresholds of perception and their content in fresh beer. These substances may affect the organoleptic character of the beer directly, when their concentration in beer exceeds their sensory threshold of perception (this is very rare), or indirectly when their concentration does not exceed a threshold of perception but act synergistically with other compounds (Techakriengkrai et al, 2006). These substances are described in the literature as "aging indicators" (Čejka et al, 2013).…”

“…1 jsou uvedeny významné karbonylové sloučeniny, které vznikají během stárnutí piva a které způsobují vývin staré chuti piva, včetně jejich prahové hodnoty vnímání a obsahu v čerstvém pivu. Tyto látky mohou ovlivnit organoleptický charakter piva přímo, a to když jejich koncentrace v pivu přesáhne jejich senzorický práh vní-mání (to je velmi ojedinělé), anebo nepřímo, když jejich koncentrace nepřesáhne práh vnímání, ale synergicky působí s ostatními látkami (Techakriengkrai et al, 2006). Tyto látky bývají v literatuře označo-vány jako "indikátory stárnutí" (Čejka et al, 2013), v čerstvém pivu se vyskytují v nízkých koncentracích, ale ve starých pivech jejich obsah vzrůstá.…”

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 .…”