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Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying

This study seeks to investigate the potential of using combined computer vision (CV) and laser-induced backscattering imaging (LLBI) in monitoring the quality attributes of sweet potato during drying. CV and backscattered images of 4 mm thickness sweet potato slices were captured after every one-hou...

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主要な著者: Hashim, Norhashila, Abdan, Khalina, Janius, Rimfiel, Iroemeha, Onwude Daniel, Chen, Guangnan
フォーマット: 論文
言語:English
出版事項: Elsevier 2018
オンライン･アクセス:http://psasir.upm.edu.my/id/eprint/73437/1/DRYING.pdf
http://psasir.upm.edu.my/id/eprint/73437/
https://www.sciencedirect.com/science/article/pii/S0168169917312486?via%3Dihub
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spelling my.upm.eprints.734372021-01-26T19:20:55Z http://psasir.upm.edu.my/id/eprint/73437/ Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying Hashim, Norhashila Abdan, Khalina Janius, Rimfiel Iroemeha, Onwude Daniel Chen, Guangnan This study seeks to investigate the potential of using combined computer vision (CV) and laser-induced backscattering imaging (LLBI) in monitoring the quality attributes of sweet potato during drying. CV and backscattered images of 4 mm thickness sweet potato slices were captured after every one-hour of drying, at drying temperatures of 50–70 °C. Reference quality properties, such as moisture content, L∗, a∗ and b∗ colour coordinates were measured hourly under the same drying conditions. Principal component analysis (PCA) and partial least square regression (PLS) were applied to the extracted combined CV (based on RGB) and backscattering imaging parameters to analyse the quality changes of sweet potato during drying. The results showed that there was significant effect of drying temperature and time on combined CV and backscattering imaging parameters. The combined optical method showed good correlation with moisture content and colour properties i.e L∗ and a∗ of sweet potato with R2 > 0.7. Specifically, the redness (a∗) gave the highest coefficient of determination (R2) of 0.80, while the moisture ratio (MR) showed the lowest root mean square error of validation (RMSEV) with the value of 0.18. Thus, this study has shown that combined CV and backscattering imaging parameters can serve as a non-destructive tool for detecting the changes in quality parameters of sweet potato during drying. Elsevier 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/73437/1/DRYING.pdf Hashim, Norhashila and Abdan, Khalina and Janius, Rimfiel and Iroemeha, Onwude Daniel and Chen, Guangnan (2018) Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying. Computers and Electronics in Agriculture, 150. 178 - 187. ISSN 0168-1699 https://www.sciencedirect.com/science/article/pii/S0168169917312486?via%3Dihub 10.1016/j.compag.2018.04.015
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description This study seeks to investigate the potential of using combined computer vision (CV) and laser-induced backscattering imaging (LLBI) in monitoring the quality attributes of sweet potato during drying. CV and backscattered images of 4 mm thickness sweet potato slices were captured after every one-hour of drying, at drying temperatures of 50–70 °C. Reference quality properties, such as moisture content, L∗, a∗ and b∗ colour coordinates were measured hourly under the same drying conditions. Principal component analysis (PCA) and partial least square regression (PLS) were applied to the extracted combined CV (based on RGB) and backscattering imaging parameters to analyse the quality changes of sweet potato during drying. The results showed that there was significant effect of drying temperature and time on combined CV and backscattering imaging parameters. The combined optical method showed good correlation with moisture content and colour properties i.e L∗ and a∗ of sweet potato with R2 > 0.7. Specifically, the redness (a∗) gave the highest coefficient of determination (R2) of 0.80, while the moisture ratio (MR) showed the lowest root mean square error of validation (RMSEV) with the value of 0.18. Thus, this study has shown that combined CV and backscattering imaging parameters can serve as a non-destructive tool for detecting the changes in quality parameters of sweet potato during drying.
format Article
author Hashim, Norhashila
Abdan, Khalina
Janius, Rimfiel
Iroemeha, Onwude Daniel
Chen, Guangnan
spellingShingle Hashim, Norhashila
Abdan, Khalina
Janius, Rimfiel
Iroemeha, Onwude Daniel
Chen, Guangnan
Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying
author_facet Hashim, Norhashila
Abdan, Khalina
Janius, Rimfiel
Iroemeha, Onwude Daniel
Chen, Guangnan
author_sort Hashim, Norhashila
title Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying
title_short Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying
title_full Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying
title_fullStr Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying
title_full_unstemmed Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying
title_sort combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (ipomoea batatas l.) during drying
publisher Elsevier
publishDate 2018
url http://psasir.upm.edu.my/id/eprint/73437/1/DRYING.pdf
http://psasir.upm.edu.my/id/eprint/73437/
https://www.sciencedirect.com/science/article/pii/S0168169917312486?via%3Dihub
_version_ 1690372566099689472
score 13.251813

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