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Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches.

ABSTRACT: The composition of volatile compounds in beer is crucial to the quality of beer. Herein, we identified 23 volatile compounds, namely, 12 esters, 4 alcohols, 5 acids, and 2 phenols, in nine different beer types using GC-MS. By performing PCA of the data of the flavor compounds, the different beer types were well discriminated. Ethyl caproate, ethyl caprylate, and phenylethyl alcohol were identified as the crucial volatile compounds to discriminate different beers. PLS regression analysis was performed to model and predict the contents of six crucial volatile compounds in the beer samples based on the characteristic wavelength of the FTIR spectrum. The R² value of each sample in the prediction model was 0.9398-0.9994, and RMSEP was 0.0122-0.7011. The method proposed in this paper has been applied to determine flavor compounds in beer samples with good consistency compared with GC-MS.

SUBMITTER: Gao YF

PROVIDER: S-EPMC10987895 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature

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Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches.

Gao Yi-Fang YF Li Xiao-Yan XY Wang Qin-Ling QL Li Zhong-Han ZH Chi Shi-Xin SX Dong Yan Y Guo Ling L Zhang Ying-Hua YH

Food chemistry: X 20240319

The composition of volatile compounds in beer is crucial to the quality of beer. Herein, we identified 23 volatile compounds, namely, 12 esters, 4 alcohols, 5 acids, and 2 phenols, in nine different beer types using GC-MS. By performing PCA of the data of the flavor compounds, the different beer types were well discriminated. Ethyl caproate, ethyl caprylate, and phenylethyl alcohol were identified as the crucial volatile compounds to discriminate different beers. PLS regression analysis was perf ...[more]

PMID: 38571574

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Dataset Information

Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches.

Publications

Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches.

Similar Datasets

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