Identification of peanut oil based on feature layer spectral data fusion method

DOI

10.2991/amcce-17.2017.159How to use a DOI?

Keywords

Peanut oil; Raman spectroscopy; Near infrared spectroscopy; Data fusion; Partial least squares-linear discriminant analysis

Abstract

The purpose of this study is to conduct qualitative analysis on the adulteration in peanut oil by combining data fusion of Raman and near infrared ( NIR) spectral characteristics with chemometrics methods With laser Raman and NIR spectrometer the spectra of 134 adulterated oil samples and 24 pure peanut oil were collected The spectra data of Raman and NIR were preprocessed Competitive adaptive reweighted sampling CARS were used to extract the characteristic wavelengths of the spectra data Combining data fusion technique and partial least squares linear discriminant analysis (PLS-LDA) method, the Ram-PLS-LDA model, NIR-PLS-LDA model and Ram-NIR-PLS-LDA model were established by using the obtained feature layer data. The calibration set and prediction set accuracy of the SG9-airPLS-Nor-CARS-SNV_DT- CARS-PLS-LDA model are 100%. According to the analysis, the prediction accuracy of Ram-NIR-PLS-LDA model is better than that of single spectral model, data fusion technology can enhance the ability to identify the model, which is conducive to practical application. It shows that the two kinds of spectra are complementary, and the using of spectral analysis and data fusion technology has great application value in the identification of edible oil.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jie Wang
AU  - Yaru Yu
AU  - SHuang Wu
AU  - Xiao Zheng
AU  - Dongping He
PY  - 2017/03
DA  - 2017/03
TI  - Identification of peanut oil based on feature layer spectral data fusion method
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017)
PB  - Atlantis Press
SP  - 897
EP  - 902
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-17.2017.159
DO  - 10.2991/amcce-17.2017.159
ID  - Wang2017/03
ER  -