Rapid Identification and Characterization of Recovered Edible Oil, Based on Raman and Near-Infrared Spectroscopy
- 10.2991/msam-18.2018.67How to use a DOI?
- vegetable oil; spectral fusion; Raman spectroscopy; near-infrared spectroscopy
A variety of recovered-edible-oil identification models were established by using Raman combined with near-infrared spectroscopy (NIR). Eight types of 156 edible vegetable oil samples were collected to acquire their Raman and NIR spectra. The spectral data were processed for modeling. The preprocessing methods for the Raman spectra included the moving average method (11 points), adaptive iterative reweighted-penalty least squares method, and the normalization method based on the intensity of the characteristic peak at 1454 cm-1 (MA11-airPLS-Nor). The preprocessing method for the NIR spectra was the standard normal variable transformation algorithm combined with a detrending technique (SNV_DT). The Raman and NIR spectra were fused at the feature level by using independently the serial fusion and wavelet fusion approaches. The results showed that with the serial-fusion- and wavelet-fusion-based models, the identification of recovered oils can be achieved very rapidly. Furthermore, the comprehensive performances of the models based on fused Raman and NIR data were better than those of models based on separate Raman or NIR data.
- © 2018, 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/).
Cite this article
TY - CONF AU - Yang Chen AU - Qingsong Luo AU - Jie Wang AU - Xiao Zheng PY - 2018/07 DA - 2018/07 TI - Rapid Identification and Characterization of Recovered Edible Oil, Based on Raman and Near-Infrared Spectroscopy BT - Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018) PB - Atlantis Press SP - 321 EP - 324 SN - 1951-6851 UR - https://doi.org/10.2991/msam-18.2018.67 DO - 10.2991/msam-18.2018.67 ID - Chen2018/07 ER -