Lard classification from other animal fats using Dielectric Spectroscopy technique

Tan C. P., . and Abd Aziz S., . and Mustafa S., . and Abd Gani S. S., . and Amat Sairin M., . and Rokhani F. Z., . Lard classification from other animal fats using Dielectric Spectroscopy technique. pp. 773-782. ISSN 2231-7546

Lard adulteration in processed foods is a major public concern as it involves religion and health. Most lard discriminating works require huge lab-based equipment and complex sample preparation. The objective of the present work was to assess the feasibility of dielectric spectroscopy as a method for classification of fats from different animal sources in particular lard. The dielectric spectra of each animal fat were measured in the radio frequency of 100 Hz “ 100 kHz at 45C to 55C. The fatty acid composition of each fat was studied by using data from gas chromatography mass spectrometry (GCMS) to explain the dielectric behaviour of each fat. The principal component analysis (PCA) and artificial neural network (ANN) were used to classify different animal fats based on their dielectric spectra. It was found that lard showed the highest dielectric constant spectra among other animal fats and was mainly affected by the composition of C16 and C18 fatty acids. PCA classification plot showed clear performance in classifying different animal fats. Finally ANN classification showed different animal fats were classified into their respective groups effectively at high accuracy of 85. Dielectric spectroscopy in combination with quantitative analysis was concluded to provide rapid method to discriminate lard from other animal fats.

Full text available from: Official URL: http://www.ifrj.upm.edu.my/26%20(03)%202019/4%20-%...

Abstract

Lard adulteration in processed foods is a major public concern as it involves religion and health. Most lard discriminating works require huge lab-based equipment and complex sample preparation. The objective of the present work was to assess the feasibility of dielectric spectroscopy as a method for classification of fats from different animal sources in particular lard. The dielectric spectra of each animal fat were measured in the radio frequency of 100 Hz “ 100 kHz at 45C to 55C. The fatty acid composition of each fat was studied by using data from gas chromatography mass spectrometry (GCMS) to explain the dielectric behaviour of each fat. The principal component analysis (PCA) and artificial neural network (ANN) were used to classify different animal fats based on their dielectric spectra. It was found that lard showed the highest dielectric constant spectra among other animal fats and was mainly affected by the composition of C16 and C18 fatty acids. PCA classification plot showed clear performance in classifying different animal fats. Finally ANN classification showed different animal fats were classified into their respective groups effectively at high accuracy of 85. Dielectric spectroscopy in combination with quantitative analysis was concluded to provide rapid method to discriminate lard from other animal fats.

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Item Type:	Article
AGROVOC Term:	Lard
AGROVOC Term:	Classification
AGROVOC Term:	Animal fats
AGROVOC Term:	Spectroscopy
AGROVOC Term:	Mass spectroscopy
AGROVOC Term:	Gas chromatography
AGROVOC Term:	Mass spectrometry
AGROVOC Term:	Dielectric properties
AGROVOC Term:	Fatty acids
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:54
URI:	http://webagris.upm.edu.my/id/eprint/8212

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