Galdo, BraisRivero, DanielFernández-Blanco, Enrique2019-09-192019-09-192019-08-13Galdo, B.; Rivero, D.; Fernandez-Blanco, E. Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning. Proceedings 2019, 21, 48.2504-3900http://hdl.handle.net/2183/23954[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.engAtribución 3.0 Españahttp://creativecommons.org/licenses/by/3.0/es/NIRElectromagnetic spectrumNeural networksconference outputopen access10.3390/proceedings2019021048