A multi-component concentration spectral modeling method with parallel drift resistance based on disorderly...

Area of Science:

• Analytical Chemistry
• Chemometrics
• Spectroscopy

Background:

• Spectrophotometry is crucial for multi-component concentration detection.
• Current machine learning methods lack interpretability and struggle with spectral drift.
• Traditional preprocessing methods are noise-sensitive and cannot reverse spectral changes.

Purpose of the Study:

• To develop interpretable spectral models for multi-component analysis.
• To address spectral parallel drift and noise amplification issues in spectrophotometry.
• To improve the accuracy of concentration detection in complex mixtures.

Main Methods:

• Established a basic absorbance spectra model for multi-component solutions.
• Developed an anti-drift modeling method using adjacent differences.
• Proposed a disorderly difference method to balance noise reduction and drift elimination.

Main Results:

• The adjacent difference method effectively eliminates spectral drift and enables reverse estimation of the original spectral model.
• The disorderly difference method demonstrates superior performance in anti-drift modeling by balancing noise and drift.
• Validated effectiveness on a cobalt ion spectral dataset with interfering ions (iron, copper).

Conclusions:

• The proposed methods enhance the accuracy and interpretability of spectral models.
• Novel anti-drift techniques offer significant improvements over traditional preprocessing.
• The disorderly difference method provides a robust solution for complex spectral analysis challenges.