Quantitative analysis by resolving variation matrices of pH-Spectrophotometric titration data using Self-Modeling...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Chemical Analysis

Background:

• pH-spectrophotometric titration is crucial for analyzing acid mixtures.
• Rank deficiency in data presents a challenge for accurate analysis.
• Existing methods may struggle with complex mixtures.

Purpose of the Study:

• To develop a new method for resolving pH-spectrophotometric titration data.
• To accurately determine concentrations of monoprotic acid mixtures.
• To address the rank deficiency problem in spectrophotometric data.

Main Methods:

• Utilizing variation matrices to shift focus to the reaction space.
• Applying self-modeling curve resolution to analyze variation matrices.
• Employing mean-centering window evolving factor analysis to identify reaction maps.
• Estimating reaction spectra and obtaining reaction extent vectors via alternating least-squares optimization.

Main Results:

• The proposed method successfully resolves mixtures of monoprotic acids.
• Quantitative analysis is achieved by augmenting variation matrices of unknown and standard samples.
• Model data for binary and ternary mixtures were accurately analyzed.
• Real binary mixtures (tartrazine/sunset yellow) concentrations were determined.

Conclusions:

• The new method effectively resolves pH-spectrophotometric titration data for acid mixtures.
• It provides a robust approach for quantitative analysis of complex samples.
• The method demonstrates applicability to both model and real-world samples.