Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning
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Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine LearningFecha
2019-08-13Cita bibliográfica
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.
Resumen
[Abstract] 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.
Palabras clave
NIR
Electromagnetic spectrum
Neural networks
Electromagnetic spectrum
Neural networks
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Atribución 3.0 España
ISSN
2504-3900