Simultaneous Spectrophotometric Determination of Three Tolualdehyde Isomers by Artificial Neural Networks and Its Comparison with Partial Least Squares

The simultaneous determination of tolualdehyde isomer mixtures by using a spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interference. By multivariate calibration methods, such as partial least squares (PLS) and artificial neural network (ANN), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. The ANN calibration model of simultaneous determination of tolualdehyde isomers was based on absorption spectra in the 230–304 nm range for 48 different mixtures of tolualdehyde isomers, and the concentration ranges for o-tolualdehyde, m-tolualdehyde and p-tolualdehyde were 6.0–15.0, 7.0–16.0 and 8.0–19.0 µg·mL−1, respectively. Other 7 samples were applied to testing the predictive ability of the calibration model. The effect of the three isomers with different concentration ratios in their mixtures on the accuracy of determination results was discussed and the appropriate range of concentration ratio was found. The proposed method was applied to simultaneous determination of the three isomers in synthetic samples and the recoveries were between 84.00% and 109.60%. The results of determination were compared with the PLS model without significant difference at 0.05 level for o-tolualdehyde and m-tolualdehyde by paired t-test. But a better performance for p-tolualdehyde in their mixtures could be obtained by the ANN calibration model.