Determination of the pKa for caffeic acid in mixed solvent using the net analyte signal method and ab initio theory

Due to the biological effects of phenolic acid components, polyphenol-rich foods are a significant part of human and animal diets. In this study, the acidity constants of caffeic acid (3,4-dihydroxycinnamic acid) in binary mixtures of ethanol?water were determined spectrophotometrically using the introduced net analyte signal (NAS) algorithm and an ab initio quantum mechanical method. The NAS is an efficient chemometric algorithm for analysis of acid?base equilibrium systems by a spectrophotometric method. At different pH values, the distribution of acid species is obtained from an absorption data matrix and this procedure enabled the pKa of caffeic acid to be obtained alternatively. The results showed that pKa1 (4.02, 4.26, 4.39, 4.57 and 5.11) and pKa2 (8.43, 8.68, 8.79, 9.00 and 9.34) were increased by increasing the percent ethanol in water (0, 10, 20, 30 and 40 vol. %) and these results were in agreement with the results of the Gaussian method. The ab initio calculated Gibbs energy change showed that para-hydroxy group is more acidic than meta-hydroxy group. The red shifts of different species of caffeic acid obtained using the ab initio quantum mechanical method are in good agreement with the results of UV?Vis spectroscopy.

Selection of a calibration sample subset by a semi-supervised method

For spectroscopic measurements, representative samples are needed in the course of building a calibration model to guarantee accurate predictions. The most widely used selection method is the Kennard-Stone method, which can be used before a reference measurement is done. In this paper, a method termed semi-supervised selection is presented to determine whether a sample should be added to the calibration set. The selection procedure has two steps. First, part of the population of samples is selected using the Kennard-Stone method, and their concentrations are measured. Second, another part of the population of samples is selected based on the scalar value distribution of the net analyte signal. If the net analyte signal of a sample is distinctive compared to the existing net analyte signal values, then the sample is added to the calibration set. The analyte of interest in the sample is then measured so that the sample can be used as a calibration sample. By a validation test, it is shown that the presented method is more efficient than random selection and Kennard-Stone selection. As a result, both the time and the money spent on reference measurements are saved.

Determination of hydrogen sulfide and hydrogen peroxide in complex samples of milk and urine by spectroscopic standard addition data and chemometrics methods

Spectroscopic data of standard additions were collected and used to calculate the spectrum of the unknown interferent(s) based on the net analyte signal.