Vis/NIR and FTIR spectroscopy supported by machine learning techniques to distinguish pure from impure Iranian rice varieties

• Autorzy: Zaresani H. , Sayyah A.H.A. , Zareiforoush H. , Khorramifar A. , Gancarz M. , Tabor S. , Karami H.
• Języki publikacji: EN

Abstrakty

EN

• Wydawca: -
• Czasopismo: International Agrophysics
• Rocznik: 2024
• Tom: 38
• Numer: 2
• Opis fizyczny: p.203-211,fig.,ref.

Twórcy

autor

Zaresani H.

• Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran

autor

Sayyah A.H.A.

• Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran

autor

Zareiforoush H.

• Department of Biosystems Engineering, Faculty of Agriculture, University of Guilan, P. O. Box: 41635-1314, Rasht, Guilan, Iran

autor

Khorramifar A.

• Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran

autor

Gancarz M.

• Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Kraków, Poland
• Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
• Center of Innovation and Research on Healthy and Safe Food, University of Agriculture in Kraków, Balicka 104, 30-149 Kraków, Poland

autor

Tabor S.

• Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Kraków, Poland

autor

Karami H.

• Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq

Identyfikatory

DOI

10.31545/intagr/185392