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Comparison and chemical structure-related basis of species discrimination of animal fats by Raman spectroscopy using near-infrared and visible excitation lasers.

Authors :
Gao, Fei
Ben-Amotz, Dor
Zhou, Simiao
Yang, Zengling
Han, Lujia
Liu, Xian
Source :
LWT - Food Science & Technology. Dec2020, Vol. 134, pN.PAG-N.PAG. 1p.
Publication Year :
2020

Abstract

The objective of present study is mainly to compare the discrimination capabilities of near-infrared Fourier transform Raman (FT-Raman, λ ex ~1064 nm) and visible-Raman (vis-Raman, λ ex ~532 nm) spectroscopy for animal fats. All animal fats were analysed by gas chromatography and then the spectra were collected using FT-Raman and vis-Raman spectroscopy. Although vis-Raman spectra partly presented fluorescence interference, the resulting Raman spectra still had higher signal-to-noise ratio than the corresponding FT-Raman spectra. Animal fats from different species produced obvious differences in peak intensity ratios at 1653/1745 and 1653/1441 cm−1, as well as shoulders at 1298 and 1264 cm−1. Chemometric analysis demonstrated that vis-Raman spectroscopy possessed better discrimination capability for animal fat compared to FT-Raman spectroscopy. According to the chemical structure-related basis, the differing degrees of unsaturated fatty acid and relative cis/trans fatty acid contents could be sufficiently reflected in Raman spectra and were the important factors for the successful species discrimination of animal fat. • Animal lipids were analysed by GC and showed different fatty acid constituents. • FT-Raman and vis-Raman spectra of animal fats were compared in detail. • Characteristic Raman peaks and constituents of animal fat were highly correlated. • FT-Raman and vis-Raman spectroscopy were quick and reliable discrimination methods. • Degree of unsaturation and relative cis/trans fatty acids were important factors. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00236438
Volume :
134
Database :
Academic Search Index
Journal :
LWT - Food Science & Technology
Publication Type :
Academic Journal
Accession number :
146635670
Full Text :
https://doi.org/10.1016/j.lwt.2020.110105