Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality

Abstract

The assessment of aromas in beer is critical to assess its quality since it could be used as an indicator of contamination or faults, which will directly influence consumers’ acceptability. Traditional techniques to evaluate aromas are time-consuming, require special training, costly equipment, and trained personnel. Therefore, this study aimed to develop a portable, low-cost electronic nose (e-nose) coupled with machine learning modeling to predict aromas in beer. Nine different gas sensors were used i) ethanol, ii) methane, iii) carbon monoxide, iv) hydrogen, v) ammonia/alcohol/benzene, vi) hydrogen sulfide, vii) ammonia, viii) benzene/alcohol/ammonia and ix) carbon dioxide. Output data we..