A Prototype to Detect the Alcohol Content of Beers Based on an Electronic Nose

Abstract

Due to the emergence of new microbreweries in the Brazilian market, there is a need to construct equipment to quickly and accurately identify the alcohol content in beverages, together with a reduced marketing cost. Towards this purpose, the electronic noses prove to be the most suitable equipment for this situation. In this work, a prototype was developed to detect the concentration of ethanol in a high spectrum of beers presents in the market. It was used cheap and easy-to-acquire 13 gas sensors made with a metal oxide semiconductor (MOS). Samples with 15 predetermined alcohol contents were used for the training and construction of the models. For validation, seven different commercial beverages were used. The correlation (R2) of 0.888 for the MLR (RMSE = 0.45) and the error of 5.47% for the ELM (RMSE = 0.33) demonstrate that the equipment can be an effective tool for detecting the levels of alcohol contained in beverages.

Rapid Non-Destructive Quantification of Eugenol in Curdlan Biofilms by Electronic Nose Combined with Gas Chromatography-Mass Spectrometry

“…In recent years, non-destructive testing technology has been receiving more attention as an emerging technology. The E-nose technology is designed to simulate the olfactory system of mammals by detecting the odor status of a specific location in real time through gas sensing array and response pattern [ 21 ]. It has attracted attention due to its advantages of simple sample processing, short response time, good recognition effect and inherent non-damage analysis [ 22 ].…”

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

“…Santos and Lozano [113] used an electronic nose (e-nose), which consists of a device with an array of gas sensors that may be a metal oxide or polymer semiconductors capable of mimicking the olfactory system [114] to analyze two main beer off-odors, acetaldehyde and ethyl acetate; the authors used the output values from the e-nose as inputs to develop a probabilistic neural network model with 94% accuracy in the validation stage to predict whether those compounds fell above the threshold, which are considered as defects in beer. Voss et al [115] developed an e-nose with 13 different gas sensors to analyze beers, and used these responses as inputs in an extreme learning machine model to predict alcohol content with RMSE = 0.63 in validation and RMSE = 0.33 in the testing stage; however, the authors did not mention the correlation or determination coefficient values for this method. Zhang et al [116] used SVM to construct a model using the fermentation parameters to predict the acetic acid (vinegar) content in the beer; however, the reported validation accuracy was low (R < 0.60).…”

Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification

“…An e-nose system usually consists of: a sensor array including multiple sensors that react in some repeatable way when exposed to volatile substances released by analytes, a data acquisition (DAQ) system for measuring and collecting the responses of sensor array with a computer program which analysis results. This powerful tool has assisted many fields in food analysis [ 7 , 8 ], such as beer quality inspection [ 9 , 10 ], quality level identification of tea [ 11 ], characterization of juices [ 12 ], and differentiation of aromatic flowers [ 13 ]. Additionally, e-nose can be applied to the monitoring of air quality [ 14 , 15 , 16 , 17 , 18 , 19 ] of gases emitted by the soil [ 20 , 21 ].…”