Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data

  1. Calle, J.L.P.
  2. Barea-Sepúlveda, M.
  3. Ruiz-Rodríguez, A.
  4. Álvarez, J.Á.
  5. Ferreiro-González, M.
  6. Palma, M.
Journal:
Sensors

ISSN: 1424-8220

Year of publication: 2022

Volume: 22

Issue: 10

Type: Article

DOI: 10.3390/S22103852 GOOGLE SCHOLAR lock_openOpen access editor HANDLE: https://hdl.handle.net/10498/27242
RODIN. Repositorio Institucional UCA.: lock_openOpen access Handle
Data source: Scopus