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Profiling of contemporary beer styles using liquid chromatography quadrupole time-of-flight mass spectrometry, multivariate analysis, and machine learning techniques.
Anderson, Hailee E; Liden, Tiffany; Berger, Blair K; Zanella, Delphine; Manh, Linh Ho; Wang, Shouyi; Schug, Kevin A.
Afiliación
  • Anderson HE; Department of Chemistry and Biochemistry, The University of Texas at Arlington, 700 Planetarium Place, Arlington, TX, 76019, USA.
  • Liden T; Department of Chemistry and Biochemistry, The University of Texas at Arlington, 700 Planetarium Place, Arlington, TX, 76019, USA.
  • Berger BK; Department of Chemistry and Biochemistry, The University of Texas at Arlington, 700 Planetarium Place, Arlington, TX, 76019, USA.
  • Zanella D; University of Liege, Molecular System, Organic & Biological Analytical Chemistry Group, 11 Allee Du Six Aout, 4000, Liege, Belgium.
  • Manh LH; Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, 500 West First St., Arlington, TX, 76019, USA.
  • Wang S; Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, 500 West First St., Arlington, TX, 76019, USA.
  • Schug KA; Department of Chemistry and Biochemistry, The University of Texas at Arlington, 700 Planetarium Place, Arlington, TX, 76019, USA; Affiliate of Collaborative Laboratories for Environmental Analysis and Remediation, The University of Texas at Arlington, Arlington, TX, 76019, USA. Electronic address: k
Anal Chim Acta ; 1172: 338668, 2021 Aug 08.
Article en En | MEDLINE | ID: mdl-34119014
Although all beer is brewed using the same four classes of ingredients, contemporary beer styles show wide variation in flavor and color, suggesting differences in their chemical profiles. A selection of 32 beers covering five styles (India pale ale, blonde, stout, wheat, and sour) were investigated to determine chemical features, which discriminate between popular beer styles. The beers were analyzed in an untargeted fashion using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). The separation and detection method were tuned to include compounds from important beer components, namely iso-α-acids and phenolic compounds. Due to the sheer number of unknown compounds in beer, multivariate analysis and machine learning techniques were used to pinpoint some of the compounds most influential in distinguishing beer styles. It was determined that while many phenols and iso-α-acids were present in the beers, they were not the compounds most responsible for the variations between styles. However, it was possible to discriminate each beer style using multivariate analysis. Principal component analysis (PCA) was able to separate and cluster the individual beer samples by style. A combination of statistical tools were used to predict formulas for some of the most influential metabolites from each style. Machine learning models accurately classified patterns in the five beer styles, indicating that they can be precisely distinguished by their nonvolatile chemical profile.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cerveza / Aprendizaje Automático Tipo de estudio: Prognostic_studies Idioma: En Revista: Anal Chim Acta Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cerveza / Aprendizaje Automático Tipo de estudio: Prognostic_studies Idioma: En Revista: Anal Chim Acta Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Países Bajos