Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence
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 develop...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-07-01
|
Series: | Fermentation |
Subjects: | |
Online Access: | https://www.mdpi.com/2311-5637/7/3/117 |
_version_ | 1797519392276217856 |
---|---|
author | Claudia Gonzalez Viejo Sigfredo Fuentes Carmen Hernandez-Brenes |
author_facet | Claudia Gonzalez Viejo Sigfredo Fuentes Carmen Hernandez-Brenes |
author_sort | Claudia Gonzalez Viejo |
collection | DOAJ |
description | 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 values (100 inputs from 1596–2396 nm) and e-nose (nine sensor readings) as inputs, respectively, to classify the samples into control, low and high concentration of faults. Model 3 (M3) and M4 were based on NIR and M5 and M6 based on the e-nose readings as inputs with 19 aroma profiles as targets for all models. A customized code tested 17 artificial neural network (ANN) algorithms automatically testing performance and neu-ron trimming. Results showed that the Bayesian regularization algorithm was the most adequate for classification rendering precisions of M1 = 95.6%, M2 = 95.3%, M3 = 98.9%, M4 = 98.3%, M5 = 96.8%, and M6 = 96.2% without statistical signs of under- or overfitting. The proposed system can be added to robotic pourers and the brewing process at low cost, which can benefit craft and larger brewing companies. |
first_indexed | 2024-03-10T07:42:08Z |
format | Article |
id | doaj.art-fc671d92d6f64f52ac625633407c5b0a |
institution | Directory Open Access Journal |
issn | 2311-5637 |
language | English |
last_indexed | 2024-03-10T07:42:08Z |
publishDate | 2021-07-01 |
publisher | MDPI AG |
record_format | Article |
series | Fermentation |
spelling | doaj.art-fc671d92d6f64f52ac625633407c5b0a2023-11-22T12:59:22ZengMDPI AGFermentation2311-56372021-07-017311710.3390/fermentation7030117Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial IntelligenceClaudia Gonzalez Viejo0Sigfredo Fuentes1Carmen Hernandez-Brenes2Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, AustraliaDigital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, AustraliaTecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, MexicoEarly 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 values (100 inputs from 1596–2396 nm) and e-nose (nine sensor readings) as inputs, respectively, to classify the samples into control, low and high concentration of faults. Model 3 (M3) and M4 were based on NIR and M5 and M6 based on the e-nose readings as inputs with 19 aroma profiles as targets for all models. A customized code tested 17 artificial neural network (ANN) algorithms automatically testing performance and neu-ron trimming. Results showed that the Bayesian regularization algorithm was the most adequate for classification rendering precisions of M1 = 95.6%, M2 = 95.3%, M3 = 98.9%, M4 = 98.3%, M5 = 96.8%, and M6 = 96.2% without statistical signs of under- or overfitting. The proposed system can be added to robotic pourers and the brewing process at low cost, which can benefit craft and larger brewing companies.https://www.mdpi.com/2311-5637/7/3/117machine learningoff aromasgas sensorsrobotic poureraroma thresholds |
spellingShingle | Claudia Gonzalez Viejo Sigfredo Fuentes Carmen Hernandez-Brenes Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence Fermentation machine learning off aromas gas sensors robotic pourer aroma thresholds |
title | Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence |
title_full | Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence |
title_fullStr | Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence |
title_full_unstemmed | Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence |
title_short | Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence |
title_sort | smart detection of faults in beers using near infrared spectroscopy a low cost electronic nose and artificial intelligence |
topic | machine learning off aromas gas sensors robotic pourer aroma thresholds |
url | https://www.mdpi.com/2311-5637/7/3/117 |
work_keys_str_mv | AT claudiagonzalezviejo smartdetectionoffaultsinbeersusingnearinfraredspectroscopyalowcostelectronicnoseandartificialintelligence AT sigfredofuentes smartdetectionoffaultsinbeersusingnearinfraredspectroscopyalowcostelectronicnoseandartificialintelligence AT carmenhernandezbrenes smartdetectionoffaultsinbeersusingnearinfraredspectroscopyalowcostelectronicnoseandartificialintelligence |