Computer-assisted methods useful for the modeling of phenolic dyes wavelengths (λmax) using MLR and ANN methods

N. Bouarra, N. Nadji, L. Nouri, A. Boudjemaa, K. Bachari, D. Messadi


Abstract: In this work, a quantitative structure-property relationship (QSPR) was built by using multiple linear regression (MLR) and artificial neural networks (ANN) to predict the wavelengths (λmax)of phenolic dyes. After many procedures to reduce the number of descriptors, a hybrid genetic algorithm and multiple linear regression (GA/MLR) method was used to select the descriptors that resulted in the best fitted models. The statistical parameters of the MLR model (R² = 89.01 %, Q²LOO = 85.39 %, s = 24.763) showed a good predictive capacity of λmax. The comparison between statistical parameters obtained by MLR and ANN models indicates the superiority of the ANN over that the MLR model, which illustrates that the ANN method is an excellent alternative for developing QSPR models for λmax than MLR method.

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