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Volume 6
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Abstract

Rapid Method for Faults Detection in Beer Using a Low-Cost Electronic Nose and Machine Learning Modelling †

by
Claudia Gonzalez Viejo
1,*,
Sigfredo Fuentes
1 and
Carmen Hernandez-Brenes
2
1
Digital Agriculture, Food and Wine Group (DAFW), Faculty of Veterinary and Agricultural Sciences, School of Agriculture and Food, University of Melbourne, Parkville, VIC 3010, Australia
2
Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico
*
Author to whom correspondence should be addressed.
†
Presented at the 2nd International Electronic Conference on Foods—Future Foods and Food Technologies for a Sustainable World, 15–30 October 2021; Available online: https://foods2021.sciforum.net/.
Biol. Life Sci. Forum 2021, 6(1), 46; https://doi.org/10.3390/Foods2021-10956
Published: 13 October 2021
(This article belongs to the Proceedings of The 2nd International Electronic Conference on Foods—“Future Foods and Food Technologies for a Sustainable World”)
Versions Notes

Abstract

:
Beer is susceptible to developing different faults (off-flavours/off-aromas) due to its main ingredients and variability in the conditions within the production stages and storage; this is especially challenging for craft breweries. Therefore, it is important to develop novel, rapid, and non-destructive methods for detecting faults. A dry lager beer was used as the base to spike with 18 different faults commonly found in beer at two different concentrations. Those 18 samples and a control group were analyzed in triplicates using a low-cost and portable electronic nose (e-nose) to assess the volatile compounds. Three machine learning models based on artificial neural networks (ANN) were developed using the e-nose outputs as inputs to (i) classify the samples into control, low, and high concentration of faults (Model 1); (ii) predict faults in the low concentration samples (Model 2); and (iii) predict faults in the high-concentration samples (Model 3). The three models had very high accuracy (Model 1: R = 0.95; Model 2: R = 0.97; Model 3: R = 0.96). This method may also be applied within different stages of beer production for the early detection of faults, which may help to apply any corrective actions before obtaining the final product.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/Foods2021-10956/s1, Poster: Rapid Method for Faults Detection in Beer Using a Low-Cost Electronic Nose and Machine Learning Modelling.

Author Contributions

Conceptualization, C.G.V. and S.F.; data curation, C.G.V. and S.F.; formal analysis, C.G.V.; funding acquisition, C.H.-B.; investigation, C.G.V. and S.F.; methodology, C.G.V. and S.F.; project administration, C.G.V. and S.F.; resources, C.G.V. and S.F.; software, C.G.V. and S.F.; validation, C.G.V. and S.F.; visualization, C.G.V. and S.F.; writing—original draft, C.G.V. and S.F.; writing—review and editing, C.G.V., S.F. and C.H.-B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Mexican Beer and Health Council (Consejo de Investigación sobre Salud y Cerveza de México).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data and intellectual property belong to The University of Melbourne; any sharing needs to be evaluated and approved by the university.

Conflicts of Interest

The authors declare no conflict of interest.
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Share and Cite

MDPI and ACS Style

Gonzalez Viejo, C.; Fuentes, S.; Hernandez-Brenes, C. Rapid Method for Faults Detection in Beer Using a Low-Cost Electronic Nose and Machine Learning Modelling. Biol. Life Sci. Forum 2021, 6, 46. https://doi.org/10.3390/Foods2021-10956

AMA Style

Gonzalez Viejo C, Fuentes S, Hernandez-Brenes C. Rapid Method for Faults Detection in Beer Using a Low-Cost Electronic Nose and Machine Learning Modelling. Biology and Life Sciences Forum. 2021; 6(1):46. https://doi.org/10.3390/Foods2021-10956

Chicago/Turabian Style

Gonzalez Viejo, Claudia, Sigfredo Fuentes, and Carmen Hernandez-Brenes. 2021. "Rapid Method for Faults Detection in Beer Using a Low-Cost Electronic Nose and Machine Learning Modelling" Biology and Life Sciences Forum 6, no. 1: 46. https://doi.org/10.3390/Foods2021-10956

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