Determination of ethanol content in ethanol-gasoline based on derivative absorption spectrometry and information fusion

Abstract

The ethanol content in ethanol-gasoline is respectively detected by the first-order derivative UV/vis absorption spectrum, the first-order derivative near infrared (NIR) absorption spectrum and the information fusion method. The backward interval partial least squares (BiPLS) algorithm is used as the feature extraction method, which is established by the partial least squares (PLS) regression model. Based on the information fusion theory, the low level data fusion (LLDF) and mid-level data fusion (MLDF) models are established by the first-order derivative UV/vis and NIR spectra. The analytical results are compared with the related textual references. Thereby, the single-spectral model based on the first-order derivative NIR absorption spectrum has the optimal results, where R p 2 =0.999 1 and RMSEP=0.324 5, while the LLDF after vector normalization (LLDF-VN2) is the optimal multi-spectral fusion model, where R p 2 =0.998 3 and RMSEP=0.498 2. The proposed method can be used to detect the ethanol content in ethanol-gasoline rapidly and provides a better choice for the component detection in mixed oils.