[Nonlinear Regression Methods of Ethanol Raman Spectra Quantitative Analysis].

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Chemical Sensing

Background:

• Quantitative analysis of ethanol-water solutions traditionally uses Raman spectroscopy, but linear methods are limited to low concentrations.
• Existing methods struggle with mutational random noise and strong fluorescence background interference.
• A need exists for a robust analytical method applicable to a wide range of ethanol concentrations.

Purpose of the Study:

• To develop and validate nonlinear regression methods for accurate ethanol concentration measurement in ethanol-water solutions.
• To overcome the limitations of linear analysis in terms of concentration range and accuracy.
• To effectively mitigate noise and fluorescence interference in Raman spectra.

Main Methods:

• Experimental measurement of Raman characteristic peak heights (ethanol CH2 at 2924.0 cm-1) and water background (3350 cm-1) using a laser Raman ethanol content detection system.
• Application of adjacent region averaging for noise reduction and multi-point interpolation for baseline calibration.
• Nonlinear regression analysis using polynomial and exponential models to correlate peak intensity ratios with ethanol concentration.

Main Results:

• Nonlinear regression analysis achieved a correlation coefficient higher than 0.997, significantly improving upon the linear fitting's 0.991.
• The nonlinear method demonstrated high accuracy for ethanol concentrations ranging from 3% to 97%.
• Linear methods were effective only within a limited range of 15%-60% ethanol concentration.

Conclusions:

• Nonlinear regression analysis provides a theoretically sound and practically effective approach for wide-range ethanol concentration determination.
• The developed analytical methods enable rapid, real-time, and accurate quantification of ethanol in solutions, even with spectral interferences.
• This approach enhances the capabilities of laser Raman ethanol content detection systems for diverse applications.