Detection of butter adulteration with lard by employing 1H-NMR spectroscopy and multivariate data analysis
The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all pr...
Saved in:
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Japan Oil Chemists' Society
2015
|
Online Access: | http://psasir.upm.edu.my/id/eprint/63956/1/Detection%20of%20butter%20adulteration%20with%20lard%20by%20employing%201H-NMR%20spectroscopy%20and%20multivariate%20data%20analysis.pdf http://psasir.upm.edu.my/id/eprint/63956/ https://www.jstage.jst.go.jp/article/jos/64/7/64_ess14255/_article |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.upm.eprints.63956 |
---|---|
record_format |
eprints |
spelling |
my.upm.eprints.639562018-06-08T00:27:38Z http://psasir.upm.edu.my/id/eprint/63956/ Detection of butter adulteration with lard by employing 1H-NMR spectroscopy and multivariate data analysis Ahmad Fadzlillah, Nurrulhidayah Che Man, Yaakob Abdul Rohman Rosman, Arieff Salleh Ismail, Amin Mustafa, Shuhaimi Khatib, Alfi The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all proton bearing compounds, and consequently all relevant sample classes. Since the spectra obtained were too complex to be analyzed visually by the naked eyes, the classification of spectra was carried out. The multivariate calibration of partial least square (PLS) regression was used for modelling the relationship between actual value of lard and predicted value. The model yielded a highest regression coefficient (R2) of 0.998 and the lowest root mean square error calibration (RMSEC) of 0.0091% and root mean square error prediction (RMSEP) of 0.0090, respectively. Cross validation testing evaluates the predictive power of the model. PLS model was shown as good models as the intercept of R2Y and Q2Y were 0.0853 and –0.309, respectively. Japan Oil Chemists' Society 2015 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/63956/1/Detection%20of%20butter%20adulteration%20with%20lard%20by%20employing%201H-NMR%20spectroscopy%20and%20multivariate%20data%20analysis.pdf Ahmad Fadzlillah, Nurrulhidayah and Che Man, Yaakob and Abdul Rohman and Rosman, Arieff Salleh and Ismail, Amin and Mustafa, Shuhaimi and Khatib, Alfi (2015) Detection of butter adulteration with lard by employing 1H-NMR spectroscopy and multivariate data analysis. Journal of Oleo Science, 64 (7). pp. 697-703. ISSN 1345-8957; ESSN: 1347-3352 https://www.jstage.jst.go.jp/article/jos/64/7/64_ess14255/_article 10.5650/jos.ess14255 |
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 |
The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all proton bearing compounds, and consequently all relevant sample classes. Since the spectra obtained were too complex to be analyzed visually by the naked eyes, the classification of spectra was carried out. The multivariate calibration of partial least square (PLS) regression was used for modelling the relationship between actual value of lard and predicted value. The model yielded a highest regression coefficient (R2) of 0.998 and the lowest root mean square error calibration (RMSEC) of 0.0091% and root mean square error prediction (RMSEP) of 0.0090, respectively. Cross validation testing evaluates the predictive power of the model. PLS model was shown as good models as the intercept of R2Y and Q2Y were 0.0853 and –0.309, respectively. |
format |
Article |
author |
Ahmad Fadzlillah, Nurrulhidayah Che Man, Yaakob Abdul Rohman Rosman, Arieff Salleh Ismail, Amin Mustafa, Shuhaimi Khatib, Alfi |
spellingShingle |
Ahmad Fadzlillah, Nurrulhidayah Che Man, Yaakob Abdul Rohman Rosman, Arieff Salleh Ismail, Amin Mustafa, Shuhaimi Khatib, Alfi Detection of butter adulteration with lard by employing 1H-NMR spectroscopy and multivariate data analysis |
author_facet |
Ahmad Fadzlillah, Nurrulhidayah Che Man, Yaakob Abdul Rohman Rosman, Arieff Salleh Ismail, Amin Mustafa, Shuhaimi Khatib, Alfi |
author_sort |
Ahmad Fadzlillah, Nurrulhidayah |
title |
Detection of butter adulteration with lard by employing 1H-NMR spectroscopy and multivariate data analysis |
title_short |
Detection of butter adulteration with lard by employing 1H-NMR spectroscopy and multivariate data analysis |
title_full |
Detection of butter adulteration with lard by employing 1H-NMR spectroscopy and multivariate data analysis |
title_fullStr |
Detection of butter adulteration with lard by employing 1H-NMR spectroscopy and multivariate data analysis |
title_full_unstemmed |
Detection of butter adulteration with lard by employing 1H-NMR spectroscopy and multivariate data analysis |
title_sort |
detection of butter adulteration with lard by employing 1h-nmr spectroscopy and multivariate data analysis |
publisher |
Japan Oil Chemists' Society |
publishDate |
2015 |
url |
http://psasir.upm.edu.my/id/eprint/63956/1/Detection%20of%20butter%20adulteration%20with%20lard%20by%20employing%201H-NMR%20spectroscopy%20and%20multivariate%20data%20analysis.pdf http://psasir.upm.edu.my/id/eprint/63956/ https://www.jstage.jst.go.jp/article/jos/64/7/64_ess14255/_article |
_version_ |
1643837884301574144 |
score |
13.19449 |