Detection of butter adulteration with lard by employing H-1-NMR spectroscopy and multivariate data analysis

Detection of butter adulteration with lard by employing H-1-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...

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Main Authors: Ahmad Fadzillah, Nurulhidayah, Che Man, Yaakob, Rohman, Abdul, Rosman, Arieff Salleh, Ismail, Amin, Mustafa, Shuhaimi, Khatib, Alfi
Format: Article
Language:English
Published: Japan Oil Chemists' Society 2015
Subjects:
QD Chemistry
Online Access:http://irep.iium.edu.my/45222/
http://irep.iium.edu.my/45222/
http://irep.iium.edu.my/45222/
http://irep.iium.edu.my/45222/1/Nurulhidayah_et_al._2015.pdf
id iium-45222
recordtype eprints
spelling iium-452222020-03-03T07:07:05Z http://irep.iium.edu.my/45222/ Detection of butter adulteration with lard by employing H-1-NMR spectroscopy and multivariate data analysis Ahmad Fadzillah, Nurulhidayah Che Man, Yaakob Rohman, Abdul Rosman, Arieff Salleh Ismail, Amin Mustafa, Shuhaimi Khatib, Alfi QD Chemistry 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 2015-03-19 Article PeerReviewed application/pdf en http://irep.iium.edu.my/45222/1/Nurulhidayah_et_al._2015.pdf Ahmad Fadzillah, Nurulhidayah and Che Man, Yaakob and Rohman, Abdul and Rosman, Arieff Salleh and Ismail, Amin and Mustafa, Shuhaimi and Khatib, Alfi (2015) Detection of butter adulteration with lard by employing H-1-NMR spectroscopy and multivariate data analysis. Journal of Oleo Science, 64 (7). pp. 607-703. ISSN 1345-8957 E-ISSN 1347-3352 https://www.jstage.jst.go.jp/article/jos/64/7/64_ess14255/_article 10.5650/jos.ess14255
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic QD Chemistry
spellingShingle QD Chemistry
Ahmad Fadzillah, Nurulhidayah
Che Man, Yaakob
Rohman, Abdul
Rosman, Arieff Salleh
Ismail, Amin
Mustafa, Shuhaimi
Khatib, Alfi
Detection of butter adulteration with lard by employing H-1-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 (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 Fadzillah, Nurulhidayah
Che Man, Yaakob
Rohman, Abdul
Rosman, Arieff Salleh
Ismail, Amin
Mustafa, Shuhaimi
Khatib, Alfi
author_facet Ahmad Fadzillah, Nurulhidayah
Che Man, Yaakob
Rohman, Abdul
Rosman, Arieff Salleh
Ismail, Amin
Mustafa, Shuhaimi
Khatib, Alfi
author_sort Ahmad Fadzillah, Nurulhidayah
title Detection of butter adulteration with lard by employing H-1-NMR spectroscopy and multivariate data analysis
title_short Detection of butter adulteration with lard by employing H-1-NMR spectroscopy and multivariate data analysis
title_full Detection of butter adulteration with lard by employing H-1-NMR spectroscopy and multivariate data analysis
title_fullStr Detection of butter adulteration with lard by employing H-1-NMR spectroscopy and multivariate data analysis
title_full_unstemmed Detection of butter adulteration with lard by employing H-1-NMR spectroscopy and multivariate data analysis
title_sort detection of butter adulteration with lard by employing h-1-nmr spectroscopy and multivariate data analysis
publisher Japan Oil Chemists' Society
publishDate 2015
url http://irep.iium.edu.my/45222/
http://irep.iium.edu.my/45222/
http://irep.iium.edu.my/45222/
http://irep.iium.edu.my/45222/1/Nurulhidayah_et_al._2015.pdf
first_indexed 2023-09-18T21:04:21Z
last_indexed 2023-09-18T21:04:21Z
_version_ 1777410833640325120

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