Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing.

RGB images adulteration classifiers saffron features spectral images

Journal

Foods (Basel, Switzerland)
ISSN: 2304-8158
Titre abrégé: Foods
Pays: Switzerland
ID NLM: 101670569

Informations de publication

Date de publication:
30 May 2023
Historique:
received: 14 03 2023
revised: 10 04 2023
accepted: 15 04 2023
medline: 10 6 2023
pubmed: 10 6 2023
entrez: 10 6 2023
Statut: epublish

Résumé

Saffron (

Identifiants

pubmed: 37297436
pii: foods12112192
doi: 10.3390/foods12112192
pmc: PMC10252310
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Pejman Alighaleh (P)

Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad P.O. Box 9177948974, Iran.

Reyhaneh Pakdel (R)

Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad P.O. Box 9177948974, Iran.

Narges Ghanei Ghooshkhaneh (N)

Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad P.O. Box 9177948974, Iran.

Soodabeh Einafshar (S)

Department of Agricultural Engineering Institute, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad P.O. Box 9177335488, Iran.

Abbas Rohani (A)

Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad P.O. Box 9177948974, Iran.

Mohammad Hossein Saeidirad (MH)

Department of Agricultural Engineering Institute, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad P.O. Box 9177335488, Iran.

Classifications MeSH