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Abstract
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The solvent polarity ET(30) scale has found wide-spread applications in studying chemical processes in
solvents. This parameter is usually measured by vis spectrophotometric measurements of the longwavelength
intramolecular charge-transfer (CT) absorption band of Reichardt's pyridinium-N-phenolate
betaine dye, e.g. the ET(30) dye, dissolved in the solvent or solvent mixture of interest. Recent advances
in colorimetric measurements based on digital photo-capturing devices suggest these methods as a
simple, cheap and fast alternative to spectrophotometric measurements in some analytical applications.
In this work, we studied the feasibility of colorimetric measurements coupled with multivariate data
analysis to determine the empirical solvent polarity parameter ET(30). The picture of the ET(30) dye
dissolved in different solvents was captured by a digital camera and then color values in the RGB space
were analyzed by the principal component analysis (PCA) method. PCA scores of the unfolded image
were then used as input of multiple linear regression and an artificial neural network model to predict
the ET(30) parameter. The ANN models were optimized to gain a model of lower prediction ability
utilizing a cross-validation test. Then, this was used to predict ET(30) values for an external solvent test
set. The generated model could explain and predict 99% of the variances in the polarity data and can
predict ET(30) values with a root mean square error of 2.25 kcal mol1 (in the ET(30) scale). The results
suggest colorimetric measurements as a useful and practical alternative to the vis spectrophotometric
measurements for determination of solvent polarity parameters derived fromsolvatochromic betaine dyes
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