Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning†
AbstractIt 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.
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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.
Galdo B, Rivero D, Fernandez-Blanco E. Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning. Proceedings. 2019; 21(1):48.Chicago/Turabian Style
Galdo, Brais; Rivero, Daniel; Fernandez-Blanco, Enrique. 2019. "Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning." Proceedings 21, no. 1: 48.
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