The application of parallel processing in the selection of spectral variables in beer quality control

•Quality Control beer dataset used through machine learning methods.•4 Portable devices connected to predict best model.•53,117,35 iterations processed with groups of 250 NIR regions each.•Up to 56% more faster than a normal procedure. Parallel data analysis was investigated to improve performance i...

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Bibliographic Details
Published in:Food chemistry 2022-01, Vol.367, p.130681-130681, Article 130681
Main Authors: Helfer, Gilson Augusto, Barbosa, Jorge Luis Victória, Hermes, Eduardo, Fagundes, Brunno José, Santos, Roberta Oliveira, Costa, Adilson Ben da
Format: Article
Language:English
Subjects:
Methods of Parallelism
Partial Least Squares
Variable selection
WebService
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Summary:•Quality Control beer dataset used through machine learning methods.•4 Portable devices connected to predict best model.•53,117,35 iterations processed with groups of 250 NIR regions each.•Up to 56% more faster than a normal procedure. Parallel data analysis was investigated to improve performance in variable selection and to develop predictive models for beer quality control. A set of spectral near infrared (NIR) data from 60 beer samples and its primitive extracts as the original concentration was used. The dataset was distributed to Raspberry Pi 3 Model B devices connected to a network that was running a Machine Learning service. With more than 4 devices acting in parallel, it was possible to reduce time in 57% to find the best linear regression coefficient (0.999) with the lower RMSECV (0.216) if compared to a singular desktop computer. Thus, parallel processing can significantly reduce the time to indicate the best model fitted during the variable’s selection.
ISSN:0308-8146
1873-7072
DOI:10.1016/j.foodchem.2021.130681