Journal article

Development of a rapid method to assess beer foamability based on relative protein content using robobeer and machine learning modeling

CG Viejo, CH Caboche, ED Kerr, CL Pegg, BL Schulz, K Howell, S Fuentes

Beverages | MDPI AG | Published : 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) da..

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