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Development of a Rapid Method to Assess Beer Foamability Based on Relative Protein Content Using RoboBEER and Machine Learning Modeling

Authors :
Claudia Gonzalez Viejo
Christopher H. Caboche
Edward D. Kerr
Cassandra L. Pegg
Benjamin L. Schulz
Kate Howell
Sigfredo Fuentes
Source :
Beverages, Vol 6, Iss 2, p 28 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

Foam-related parameters are associated with beer quality and dependent, among others, on the protein content. This study aimed to develop a machine learning (ML) model to predict the pattern and presence of 54 proteins. Triplicates of 24 beer samples were analyzed through proteomics. Furthermore, samples were analyzed using the RoboBEER to evaluate 15 physical parameters (color, foam, and bubbles), and a portable near-infrared (NIR) device. Proteins were grouped according to their molecular weight (MW), and a matrix was developed to assess only the significant correlations (p < 0.05) with the physical parameters. Two ML models were developed using the NIR (Model 1), and RoboBEER (Model 2) data as inputs to predict the relative quantification of 54 proteins. Proteins in the 0–20 kDa group were negatively correlated with the maximum volume of foam (MaxVol; r = −0.57) and total lifetime of foam (TLTF; r = −0.58), while those within 20–40 kDa had a positive correlation with MaxVol (r = 0.47) and TLTF (r = 0.47). Model 1 was not as accurate (testing r = 0.68; overall r = 0.89) as Model 2 (testing r = 0.90; overall r = 0.93), which may serve as a reliable and affordable method to incorporate the relative quantification of important proteins to explain beer quality.

Details

Language :
English
ISSN :
23065710
Volume :
6
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Beverages
Publication Type :
Academic Journal
Accession number :
edsdoj.3c15d4b7fc5240369166accb9517ecb8
Document Type :
article
Full Text :
https://doi.org/10.3390/beverages6020028