Next Article in Journal
Roasting Conditions and Coffee Flavor: A Multi-Study Empirical Investigation
Next Article in Special Issue
Analysis of Lambic Beer Volatiles during Aging Using Gas Chromatography–Mass Spectrometry (GCMS) and Gas Chromatography–Olfactometry (GCO)
Previous Article in Journal
The State of Automated Facial Expression Analysis (AFEA) in Evaluating Consumer Packaged Beverages
Open AccessArticle

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

1
School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
2
School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, QLD 4072, Australia
*
Author to whom correspondence should be addressed.
Beverages 2020, 6(2), 28; https://doi.org/10.3390/beverages6020028
Received: 18 March 2020 / Revised: 1 April 2020 / Accepted: 21 April 2020 / Published: 3 May 2020
(This article belongs to the Special Issue Beer Quality and Flavour)
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. View Full-Text
Keywords: proteomics; artificial neural networks; robotics; artificial intelligence proteomics; artificial neural networks; robotics; artificial intelligence
Show Figures

Figure 1

MDPI and ACS Style

Gonzalez Viejo, C.; Caboche, C.H.; Kerr, E.D.; Pegg, C.L.; Schulz, B.L.; Howell, K.; Fuentes, S. Development of a Rapid Method to Assess Beer Foamability Based on Relative Protein Content Using RoboBEER and Machine Learning Modeling. Beverages 2020, 6, 28. https://doi.org/10.3390/beverages6020028

AMA Style

Gonzalez Viejo C, Caboche CH, Kerr ED, Pegg CL, Schulz BL, Howell K, Fuentes S. Development of a Rapid Method to Assess Beer Foamability Based on Relative Protein Content Using RoboBEER and Machine Learning Modeling. Beverages. 2020; 6(2):28. https://doi.org/10.3390/beverages6020028

Chicago/Turabian Style

Gonzalez Viejo, Claudia; Caboche, Christopher H.; Kerr, Edward D.; Pegg, Cassandra L.; Schulz, Benjamin L.; Howell, Kate; Fuentes, Sigfredo. 2020. "Development of a Rapid Method to Assess Beer Foamability Based on Relative Protein Content Using RoboBEER and Machine Learning Modeling" Beverages 6, no. 2: 28. https://doi.org/10.3390/beverages6020028

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop