Quantitative Analysis of Volatile Sulfur Compounds in Beer Using a Membrane Extraction Interface

Dercksen, A. W.; Laurens, Johannes B.; Torline, P.; Axcell, B. C.; Rohwer, Egmont Richard

doi:10.1094/asbcj-54-0228

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .…”

Section: Resultsmentioning

confidence: 99%

Predicting and improving complex beer flavor through machine learning

Schreurs,

Piampongsant,

Roncoroni

et al. 2024

Nat Commun

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The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Section: Resultsmentioning

confidence: 99%

Predicting and improving complex beer flavor through machine learning

Schreurs,

Piampongsant,

Roncoroni

et al. 2024

Nat Commun

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show abstract

“…The SCD was employed by Burmeister et al [73] to replace the FPD, the traditional detector for measurement of volatile sulfur compounds in beers, and was found to be sensitive and free from hydrocarbon quenching. Dercksen et al [74,75] reported a method using in-bottle purge-and-trap sampling and GC-SCD that generated a sulfur compound profile correlated to the sulfury notes detected by human panelists. Goldstein et al [76] analyzed beers using a GC method with solvent extraction and FID, FPD, SCD, and MS detection.…”

Section: Characterization Of Beers and Other Alcoholic Beveragesmentioning

confidence: 99%

Unique selective detectors for gas chromatography: Nitrogen and sulfur chemiluminescence detectors

Yan¹

2006

J of Separation Science

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ReviewUnique selective detectors for gas chromatography: Nitrogen and sulfur chemiluminescence detectors Many of present day's complex analyses and challenging analytical tasks demand highly selective detection techniques for GC. Universal nitrogen/sulfur chemiluminescence detection is one of the most powerful analytical tools for gas chromatographic analysis. Its strength stems from the unique and beneficial characteristics of high selectivity, sensitivity, and equimolarity. The nitrogen/sulfur chemiluminescence detection technology has provided analytical solutions unsurpassed by any other techniques to a broad range of applications in petroleum characterization, food/beverage flavor analysis, and environmental monitoring. This paper presents a general review of this unique technology, including a brief description of operating principles, an overview of detector performance, practical discussions on instrument operation, and a sizable list of applications.

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“…Recently, the use of a membrane extraction device with a sulfur chemiluminescence detector (SCD) for the analysis of volatile H 2 S in beer has been reported by Derksen et al (1996). The principle of the SCD detection is:…”

Section: Applications Of CL Techniques To Beer Analysismentioning

confidence: 99%

Chemiluminescent Methods in Alcoholic Beverage Analysis

1998

J. Agric. Food Chem.

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Quantitative Analysis of Volatile Sulfur Compounds in Beer Using a Membrane Extraction Interface

Cited by 6 publications

References 6 publications

Predicting and improving complex beer flavor through machine learning

Predicting and improving complex beer flavor through machine learning

Unique selective detectors for gas chromatography: Nitrogen and sulfur chemiluminescence detectors

Chemiluminescent Methods in Alcoholic Beverage Analysis

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