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Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning

Faculty of Computer Science, CITIC, University of A Coruna, 15071 Galicia, Spain
Author to whom correspondence should be addressed.
Presented at the 2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019.
Proceedings 2019, 21(1), 48;
Published: 13 August 2019
(This article belongs to the Proceedings of XoveTIC Conference)
PDF [165 KB, uploaded 13 August 2019]


It is a fact that, non-destructive measurement technologies have gain a lot of attention over the years. Among those technologies, NIR technology is the one which allows the analysis of electromagnetic spectrum looking for carbon-link interactions. This technology analyzes the electromagnetic spectrum in the band between 700 nm and 2500 nm, a band very close to the visible spectrum. Traditionally, the devices used to measure are utterly expensive and enormously bulky. That is why this project was focused on a portable spectrophotometer to make measures. This device is smaller and cheaper than the common spectrophotometer, although at the cost of a lower resolution. In this work, that device in combination with the use of machine learning was used to detect if a beer contains alcohol or it can be labeled as non-alcoholic drink.
Keywords: NIR; Electromagnetic spectrum; Neural networks NIR; Electromagnetic spectrum; Neural networks
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Galdo, B.; Rivero, D.; Fernandez-Blanco, E. Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning. Proceedings 2019, 21, 48.

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