Short near infrared spectroscopy coupled with partial least square for the detection of adulteration in soybean oil

Short near infrared spectroscopy coupled with partial least square for the detection of adulteration in soybean oil

This paper demonstrates the application of short near infrared spectroscopy to measure the presence of lard adulteration in soybean oil. Partial least square (PLS) regression was used as supervised learning algorithm to quantify the percentage of adulteration in soybean oil. Spectral data obtained w...

Full description

Bibliographic Details
Published in:AIP Conference Proceedings
Main Author: Basri K.N.; Khir M.F.A.; Rani R.A.; Sharif Z.; Rusop M.; Zoolfakar A.S.
Format: Conference paper
Language:English
Published: American Institute of Physics Inc. 2018
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047361166&doi=10.1063%2f1.5036854&partnerID=40&md5=1ec52a1cce173104352a5638c031f7b3
id 2-s2.0-85047361166
spelling 2-s2.0-85047361166
Basri K.N.; Khir M.F.A.; Rani R.A.; Sharif Z.; Rusop M.; Zoolfakar A.S.
Short near infrared spectroscopy coupled with partial least square for the detection of adulteration in soybean oil
2018
AIP Conference Proceedings
1963

10.1063/1.5036854
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047361166&doi=10.1063%2f1.5036854&partnerID=40&md5=1ec52a1cce173104352a5638c031f7b3
This paper demonstrates the application of short near infrared spectroscopy to measure the presence of lard adulteration in soybean oil. Partial least square (PLS) regression was used as supervised learning algorithm to quantify the percentage of adulteration in soybean oil. Spectral data obtained was divided into training and validation dataset using fixed ratio 7:3. R2 for calibration and prediction are 0.9999 while root mean square error of calibration and prediction are 0.3018 and 0.3246. The result obtained showed the robustness of the predictive model. © 2018 Author(s).
American Institute of Physics Inc.
0094243X
English
Conference paper
author Basri K.N.; Khir M.F.A.; Rani R.A.; Sharif Z.; Rusop M.; Zoolfakar A.S.
spellingShingle Basri K.N.; Khir M.F.A.; Rani R.A.; Sharif Z.; Rusop M.; Zoolfakar A.S.
Short near infrared spectroscopy coupled with partial least square for the detection of adulteration in soybean oil
author_facet Basri K.N.; Khir M.F.A.; Rani R.A.; Sharif Z.; Rusop M.; Zoolfakar A.S.
author_sort Basri K.N.; Khir M.F.A.; Rani R.A.; Sharif Z.; Rusop M.; Zoolfakar A.S.
title Short near infrared spectroscopy coupled with partial least square for the detection of adulteration in soybean oil
title_short Short near infrared spectroscopy coupled with partial least square for the detection of adulteration in soybean oil
title_full Short near infrared spectroscopy coupled with partial least square for the detection of adulteration in soybean oil
title_fullStr Short near infrared spectroscopy coupled with partial least square for the detection of adulteration in soybean oil
title_full_unstemmed Short near infrared spectroscopy coupled with partial least square for the detection of adulteration in soybean oil
title_sort Short near infrared spectroscopy coupled with partial least square for the detection of adulteration in soybean oil
publishDate 2018
container_title AIP Conference Proceedings
container_volume 1963
container_issue
doi_str_mv 10.1063/1.5036854
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047361166&doi=10.1063%2f1.5036854&partnerID=40&md5=1ec52a1cce173104352a5638c031f7b3
description This paper demonstrates the application of short near infrared spectroscopy to measure the presence of lard adulteration in soybean oil. Partial least square (PLS) regression was used as supervised learning algorithm to quantify the percentage of adulteration in soybean oil. Spectral data obtained was divided into training and validation dataset using fixed ratio 7:3. R2 for calibration and prediction are 0.9999 while root mean square error of calibration and prediction are 0.3018 and 0.3246. The result obtained showed the robustness of the predictive model. © 2018 Author(s).
publisher American Institute of Physics Inc.
issn 0094243X
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
_version_ 1809677603219439616

Similar Items