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.
Zeitschrift:
Sensors

ISSN: 1424-8220

Datum der Publikation: 2022

Ausgabe: 22

Nummer: 10

Art: Artikel

DOI: 10.3390/S22103852 GOOGLE SCHOLAR lock_openOpen Access editor
Datenquelle: Scopus