Analysis and prediction of the major fatty acids in vegetable oils using dielectric spectroscopy at 5–30 MHz

Masyitah Amat Sairin, Samsuzana Abd Aziz, Chan Yoke Mun, Alfadhl Yahya Khaled and Fakhrul Zaman Rokhani

PLOS ONE, 2022, vol. 17, issue 5, 1-14

Abstract: A dielectric spectroscopy method was applied to determine major fatty acids composition in vegetable oils. Dielectric constants of vegetable oils were measured in the frequency range of 5–30 MHz. After data pre-treatment, prediction models were constructed using partial least squares (PLS) regression between dielectric spectral values and the fatty acids compositions measured by gas chromatography. Generally, the root means square error of validation (RMSECV) was less than 11.23% in the prediction of individual fatty acids. The determination coefficient (R2) between predicted and measured oleic, linoleic, mono-unsaturated, and poly-unsaturated fatty acids were 0.84, 0.77, 0.84, and 0.84, respectively. These results indicated that dielectric spectroscopy coupled with PLS regression could be a promising method for predicting major fatty acid composition in vegetable oils and has the potential to be used for in-situ monitoring systems of daily consumption of dietary fatty acids.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0268827

DOI: 10.1371/journal.pone.0268827

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