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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