Journal article

Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence

Claudia Gonzalez Viejo, Sigfredo Fuentes, Carmen Hernandez-Brenes

FERMENTATION-BASEL | MDPI | Published : 2021


Early detection of beer faults is an important assessment in the brewing process to secure a high-quality product and consumer acceptability. This study proposed an integrated AI system for smart detection of beer faults based on the comparison of near-infrared spectroscopy (NIR) and a newly developed electronic nose (e-nose) using machine learning modelling. For these purposes, a commercial larger beer was used as a base prototype, which was spiked with 18 common beer faults plus the control aroma. The 19 aroma profiles were used as targets for classification ma-chine learning (ML) modelling. Six different ML models were developed; Model 1 (M1) and M2 were developed using the NIR absorbance..

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Funding Acknowledgements

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