Multivariate calibration methods are used for the determination of mixtures of acids (or bases) with similar pK by potentiometric titration. The linear titration equations of mixtures of acids are derived and the obtained potentiometric data are processed by multivariate calibration approaches such as partial least squares.The most serious problem with multivariate calibration methods is the lack of possibility to handle interferents which are not present during calibration[1]. If acid-base titration model of the unmodeled component can be obtained and mathematically added in variable amounts to the original calibration spectra, then a new synthetic multivariate calibration model can be generated from the augmented data to accommodate the presence of the unmodeled source of spectral.In this study a new simple method is proposed for estimation of acid-base titration model of unknown interferent by estimation of its pK. In the proposed method, no chemical information about interference is necessary. The practical utility of this method was demonstrated for the analysis of simulated and experimental outlier prediction set and acceptable results were obtained using partial least square regression calibration model. The effects of differences in pKa on accuracy were studied in detail. Synthetic mixtures of benzoic acid (analyte) and acetic acid or salicylic acid or 2- chlorobenzoic acid (unknown interferent) were analyzed by the proposed method and acceptable results were obtained. The satisfactory results were btained for determination of benzoic acid inwhite vinegar samples. The accuracy and precision of the results are all satisfactory, and show the applicability of the method for real and complex samples.

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