Cargando…

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

Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spec...

Descripción completa

Detalles Bibliográficos
Autores principales: Aznan, Aimi, Gonzalez Viejo, Claudia, Pang, Alexis, Fuentes, Sigfredo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9697730/
https://www.ncbi.nlm.nih.gov/pubmed/36433249
http://dx.doi.org/10.3390/s22228655
_version_ 1784838639670263808
author Aznan, Aimi
Gonzalez Viejo, Claudia
Pang, Alexis
Fuentes, Sigfredo
author_facet Aznan, Aimi
Gonzalez Viejo, Claudia
Pang, Alexis
Fuentes, Sigfredo
author_sort Aznan, Aimi
collection PubMed
description Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice’s weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94–0.98) and non-invasive measurement through the packaging (NIR; R = 0.95–0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain.
format Online
Article
Text
id pubmed-9697730
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-96977302022-11-26 Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning Aznan, Aimi Gonzalez Viejo, Claudia Pang, Alexis Fuentes, Sigfredo Sensors (Basel) Article Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice’s weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94–0.98) and non-invasive measurement through the packaging (NIR; R = 0.95–0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain. MDPI 2022-11-09 /pmc/articles/PMC9697730/ /pubmed/36433249 http://dx.doi.org/10.3390/s22228655 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Aznan, Aimi
Gonzalez Viejo, Claudia
Pang, Alexis
Fuentes, Sigfredo
Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning
title Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning
title_full Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning
title_fullStr Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning
title_full_unstemmed Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning
title_short Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning
title_sort rapid detection of fraudulent rice using low-cost digital sensing devices and machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9697730/
https://www.ncbi.nlm.nih.gov/pubmed/36433249
http://dx.doi.org/10.3390/s22228655
work_keys_str_mv AT aznanaimi rapiddetectionoffraudulentriceusinglowcostdigitalsensingdevicesandmachinelearning
AT gonzalezviejoclaudia rapiddetectionoffraudulentriceusinglowcostdigitalsensingdevicesandmachinelearning
AT pangalexis rapiddetectionoffraudulentriceusinglowcostdigitalsensingdevicesandmachinelearning
AT fuentessigfredo rapiddetectionoffraudulentriceusinglowcostdigitalsensingdevicesandmachinelearning