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Detection of butter adulteration with lard by employing (1)H-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 ((1)H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all...

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Main Authors: Ahmad Fadzillah, Nurrulhidayah, Che Man, Yaakob, Abd. Rohman, Abd. Rohman, Rosman, Arieff Salleh, Ismail, Amin, Mustafa, Shuhaimi, Khatib, Alfi
Format: Article
Published: Japan Oil Chemists’ Society 2015
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/id/eprint/58255/
http://dx.doi.org/10.5650/jos.ess14255
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spelling my.utm.582552021-08-22T02:15:25Z http://eprints.utm.my/id/eprint/58255/ Detection of butter adulteration with lard by employing (1)H-NMR spectroscopy and multivariate data analysis Ahmad Fadzillah, Nurrulhidayah Che Man, Yaakob Abd. Rohman, Abd. Rohman Rosman, Arieff Salleh Ismail, Amin Mustafa, Shuhaimi Khatib, Alfi Q Science (General) 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 ((1)H-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 (R(2)) 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 R(2)Y and Q(2)Y were 0.0853 and -0.309, respectively. Japan Oil Chemists’ Society 2015 Article PeerReviewed Ahmad Fadzillah, Nurrulhidayah and Che Man, Yaakob and Abd. Rohman, Abd. Rohman and Rosman, Arieff Salleh and Ismail, Amin and Mustafa, Shuhaimi and Khatib, Alfi (2015) Detection of butter adulteration with lard by employing (1)H-NMR spectroscopy and multivariate data analysis. Journal of Oleo Science, 64 (7). pp. 697-703. ISSN 1345-8957 http://dx.doi.org/10.5650/jos.ess14255
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science (General)
spellingShingle Q Science (General)
Ahmad Fadzillah, Nurrulhidayah
Che Man, Yaakob
Abd. Rohman, Abd. Rohman
Rosman, Arieff Salleh
Ismail, Amin
Mustafa, Shuhaimi
Khatib, Alfi
Detection of butter adulteration with lard by employing (1)H-NMR spectroscopy and multivariate data analysis
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 ((1)H-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 (R(2)) 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 R(2)Y and Q(2)Y were 0.0853 and -0.309, respectively.
format Article
author Ahmad Fadzillah, Nurrulhidayah
Che Man, Yaakob
Abd. Rohman, Abd. Rohman
Rosman, Arieff Salleh
Ismail, Amin
Mustafa, Shuhaimi
Khatib, Alfi
author_facet Ahmad Fadzillah, Nurrulhidayah
Che Man, Yaakob
Abd. Rohman, Abd. Rohman
Rosman, Arieff Salleh
Ismail, Amin
Mustafa, Shuhaimi
Khatib, Alfi
author_sort Ahmad Fadzillah, Nurrulhidayah
title Detection of butter adulteration with lard by employing (1)H-NMR spectroscopy and multivariate data analysis
title_short Detection of butter adulteration with lard by employing (1)H-NMR spectroscopy and multivariate data analysis
title_full Detection of butter adulteration with lard by employing (1)H-NMR spectroscopy and multivariate data analysis
title_fullStr Detection of butter adulteration with lard by employing (1)H-NMR spectroscopy and multivariate data analysis
title_full_unstemmed Detection of butter adulteration with lard by employing (1)H-NMR spectroscopy and multivariate data analysis
title_sort detection of butter adulteration with lard by employing (1)h-nmr spectroscopy and multivariate data analysis
publisher Japan Oil Chemists’ Society
publishDate 2015
url http://eprints.utm.my/id/eprint/58255/
http://dx.doi.org/10.5650/jos.ess14255
_version_ 1709667335158104064
score 13.211869

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