Quantitative analysis by resolving variation matrices of pH–Spectrophotometric titration data using Self-Modeling Curve Resolution

Abstract

A new method is proposed for resolving pH-spectrophotometric titration data to determine mixtures of monoprotic acids. The method uses variation matrices to circumvent the rank deficiency problem of such data by shifting to another target, namely the reaction space. Self-modeling curve resolution is used to resolve variation matrices of pH–spectrophotometric titration data for acid mixtures. The variation matrix is obtained by subtracting the zero-point spectrum (e.g., acidic spectrum) from each spectrum at each pH value. Mean-centering window evolving factor analysis is used to identify the local reaction map. Reaction spectra can be estimated using selective regions of the local reaction map, from which reaction extent vectors can be obtained by alternating least-squares optimization. It was shown that quantitative analysis can be performed by augmentation of the variation matrix of the unknown and standard samples and comparison of obtained reaction extent curves. The applicability of the proposed method was evaluated using model data for binary and ternary mixtures of monoprotic acids. pH–spectrophotometric titration data for real binary mixtures of tartrazine/sunset yellow were also investigated using the proposed method and their concentration were determined in a real sample.