Simultaneous quantitative analysis of overlapping spectrophotometric signals using wavelet multiresolution analysis...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Signal Processing

Background:

• Spectrophotometric analysis often faces challenges with overlapping spectra, complicating the determination of multiple analytes.
• Noise and irrelevant information in spectral data can significantly hinder accurate quantitative analysis.

Purpose of the Study:

• To describe the mathematical foundations of discrete wavelet transform (DWT) and multiresolution analysis (MRA).
• To develop a novel method, Wavelet Partial Least Squares (WPLS), for simultaneous spectrophotometric determination of Fe(II) and Fe(III) in the presence of spectral overlap.
• To assess the efficacy of MRA as a pre-treatment tool for spectrophotometric data.

Main Methods:

• Discrete Wavelet Transform (DWT) and Multiresolution Analysis (MRA) using Daubechies orthogonal wavelet basis.
• Application of MRA for noise reduction and data pre-treatment of spectrophotometric spectra.
• Development of a Wavelet Partial Least Squares (WPLS) method integrating MRA with Partial Least Squares (PLS) regression.
• Utilizing Principal Component Analysis (PCA) algorithm for data reduction.

Main Results:

• Wavelet MRA effectively removed noise and irrelevant information from spectrophotometric spectra.
• The WPLS method demonstrated success in the simultaneous determination of Fe(II) and Fe(III) despite severe spectral overlap.
• Eight error functions were calculated post-MRA to deduce the optimal number of factors for analysis.

Conclusions:

• Wavelet MRA is a valuable tool for pre-processing spectrophotometric data, enhancing analytical accuracy.
• The WPLS method provides a robust solution for simultaneous multi-component determination with overlapping spectra.
• Developed software (SPWMRA and SPWPLS) facilitates MRA and WPLS applications in spectrophotometry.