Back to Search
Start Over
Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches
- Source :
- Food Chemistry: X, Vol 22, Iss , Pp 101300- (2024)
- Publication Year :
- 2024
- Publisher :
- Elsevier, 2024.
-
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.
Details
- Language :
- English
- ISSN :
- 25901575
- Volume :
- 22
- Issue :
- 101300-
- Database :
- Directory of Open Access Journals
- Journal :
- Food Chemistry: X
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.b09385bf7cfa45e2b6284013e700b595
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.fochx.2024.101300