Related Experiment Video
Updated: Nov 20, 2025

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
Published on: January 7, 2019
Hybrid variable selection strategy coupled with random forest (RF) for quantitative analysis of methanol in
Maogang Li1, Yanyan Xu1, Jing Men2
1Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an, 710127, China.
Abstract:
With the trend of portable and miniaturization, Raman spectrometer requires more advanced analytical methods providing more rapid and accurate analysis performance for in-situ analysis. In this work, a hybrid variable selection method based on V-WSP and variable importance measurement (VIM) coupled with random forest (RF) was used to improve the quantitative analysis performance of portable laser Raman instruments for quantitative analysis of methanol content in methanol gasoline. First, five preprocessing methods were applied to reduce the infection information in the raw spectra, respectively. Based on the spectra data processed by multivariate scattering correction (MSC), V-WSP was employed to filter the infection or redundant information in Raman spectroscopy, and 579 variables were obtained when the correlation threshold is 0.9600. Then, the variables were further eliminated by VIM. Finally, 43 variables were obtained by the V-WSP-VIM method. In data processing, out of bag (OOB) error estimation and 10-flod cross validation (CV) were applied to optimize the parameters of preprocessing methods, V-WSP, VIM and RF model. The results fully demonstrated that compared with the RF model based on raw spectra, the RF model based on V-WSP-VIM method can achieve a better prediction performance for the quantitative analysis of methanol content in methanol-gasoline, with the coefficients of determination of cross-validation (R2CV) improving from 0.9100 to 0.9662, the root mean square error of cross-validation (RMSECV) reducing from 0.0572 to 0.0365%, the coefficients of determination of prediction set (R2P) improving from 0.9214 to 0.9407, the root mean square error of prediction set (RMSEP) reducing from 0.0420 to 0.0382%, the variables reducing from 1044 to 43 and the modeling time reducing from 72.94 to 6.41 s. The results indicates that V-WSP-VIM coupled with RF is an effective method to improve the performance of portable laser Raman spectrometer for quantitative analysis of methanol content in methanol gasoline.
More Related Videos
Related Concept Videos
Mass Spectrometry: Complex Analysis
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
Raman Spectroscopy Instrumentation: Overview
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
Raman Spectroscopy: Overview
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
Response Surface Methodology
The process of RSM involves several key steps:
Classification of Titrimetric Analysis Based on Reaction Types
Titrations between an acid and a base lead to neutralization reactions that form...
Gas Chromatography–Mass Spectrometry (GC–MS)
A gas chromatograph consists of a long, narrow capillary column with a polysiloxane coating on the inner wall....

