Citation
Abstract
Adulteration of lard with other fats and oils in food production affects many areas including economics, religion, and health. Previous studies discriminated lard based on major components of fats, i.e. triglycerides and fatty acids. This study aimed to differentiate lard and other animal fats (beef, chicken and mutton fat) based on n-alkane profiles established by gas chromatography-mass spectrometry (GC–MS). Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were able to initiate clustering of lard and other animal fats. Good result was obtained using Random Forest (RF) and Partial Least Squares-Discriminant Analysis (PLS-DA). Statistical models propose tetracosane (C24) as a potential n-alkane marker and it was found that C24 was the major alkane with composition of 15.72% (GC–MS) of total alkanes identified. Based on this finding, more interesting study may potentially be explored for the interest of various fats and oils consumers in vast applications especially using chemometrics analysis.
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Biotechnology and Biomolecular Sciences Halal Products Research Institute |
DOI Number: | https://doi.org/10.1016/j.foodres.2022.112332 |
Publisher: | Elsevier |
Keywords: | Fat adulteration; GC–MS; Chemometric analysis; Machine learning; PLS-DA; Random Forest; N-alkane marker |
Depositing User: | Ms. Nur Faseha Mohd Kadim |
Date Deposited: | 12 Jul 2023 08:39 |
Last Modified: | 12 Jul 2023 08:39 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.foodres.2022.112332 |
URI: | http://psasir.upm.edu.my/id/eprint/100960 |
Statistic Details: | View Download Statistic |
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