Rapid Detection of Rice Adulteration Using a Low-Cost Electronic Nose and Machine Learning Modelling
Food fraud is one of the primary issues that may threaten consumers’ trust and confidence in the food industry. Detecting food fraud, such as rice adulteration, is challenging since the adulterant looks identical to authentic rice. Moreover, the detection procedure is commonly time-consuming and req...
Main Authors: | Aimi Aznan, Claudia Gonzalez Viejo, Alexis Pang, Sigfredo Fuentes |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-11-01
|
Series: | Engineering Proceedings |
Subjects: | |
Online Access: | https://www.mdpi.com/2673-4591/27/1/1 |
Similar Items
-
Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning
by: Aimi Aznan, et al.
Published: (2022-11-01) -
Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
by: Aimi Aznan, et al.
Published: (2022-04-01) -
Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies
by: Aimi Aznan, et al.
Published: (2021-09-01) -
Non-Invasive Digital Technologies to Assess Wine Quality Traits and Provenance through the Bottle
by: Natalie Harris, et al.
Published: (2022-12-01) -
Digital Detection of Olive Oil Rancidity Levels and Aroma Profiles Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Machine Learning Modelling
by: Claudia Gonzalez Viejo, et al.
Published: (2022-04-01)