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Feasibility study of discriminating edible vegetable oils by 2D-NIR

Authors: Bin Chen; Ping Tian; Dao-Li Lu; Zhi-Qin Zhou; Mei-Li Shao;

Feasibility study of discriminating edible vegetable oils by 2D-NIR

Abstract

In order to develop a quick and non-destructive discrimination method for differentiating edible vegetable oils, near infrared spectra of four kinds of vegetable oil, namely soybean oil, palm oil, sesame oil and peanut oil from three different manufacturers were obtained using a Fourier transform near-infrared spectrometer and were combined using two-dimensional correlation spectroscopy over a range temperatures. The results showed that the peak information of different kinds of vegetable oil in the autopower spectra was different, and the difference was especially significant in the synchronous 2D-NIR spectra in the key area of 4800–4500 cm−1. Therefore, 2D-NIR spectroscopy can be used as an effective method to discriminate different kinds of vegetable oil. A comparison of peanut oils from different manufacturers showed that the difference between oils from manufacturers attached to the same holding company was very small, and that larger differences were observed for oils from manufacturers attached to different holding companies. This may be due to differences in production technology between holding companies. These findings may be used to identify which holding company produced a given sample of vegetable oil.

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Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
15
Average
Top 10%
Average
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