Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning.

Aznan, Aimi; Gonzalez Viejo, Claudia; Pang, Alexis; Fuentes, Sigfredo

Aznan, Aimi; Gonzalez Viejo, Claudia; Pang, Alexis; Fuentes, Sigfredo.

Afiliación

Aznan A; Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
Gonzalez Viejo C; Department of Agrotechnology, Faculty of Mechanical Engineering and Technology, University Malaysia Perlis, Arau 02600, Perlis, Malaysia.
Pang A; Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.
Fuentes S; Digital Agriculture, Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia.

Sensors (Basel) ; 22(22)2022 Nov 09.

Article en En | MEDLINE | ID: mdl-36433249

Asunto(s)

Oryza; Aprendizaje Automático; Nariz Electrónica; Redes Neurales de la Computación; Algoritmos

Palabras clave

adulteration; artificial intelligence; electronic nose; food fraud; near-infrared; sensor

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_financiamento_saude Asunto principal: Oryza Tipo de estudio: Diagnostic_studies / Health_economic_evaluation / Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Australia

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