Quantitative structure-property relationship modeling for prediction of chromatography retention indices of volatile organic compounds in peppers based on Monte Carlo approach

Abstract

Abstract Worldwide, various types of pepper are used in food as an additive due to their unique pungency, aroma, taste, and color. This spice is valued by its pungency contributed by the alkaloid piperine and aroma attributed to volatile essential oils. The essential oils are composed of volatile organic compounds (VOCs) with different concentrations and ratios. The aim of the present work is to develop a reliable QSPR model for retention indices (RI) of 273 identified VOCs of different types of peppers. The inbuilt Monte Carlo algorithm of CORAL software is used to generate QSPR models by using the hybrid optimal descriptor extracted from the combination of SMILES and HFG (hydrogen-filled graph). The whole dataset of 273 VOCs is used to make ten splits, each of which is further divided into four sets: active training, passive training, calibration, and validation. The balance of correlation method with four target functions i.e. TF0 (WIIC = WCII = 0), TF1 (WIIC = 0.5 & WCII = 0), TF2 (WIIC = 0 & WCII = 0.3) and TF3 (WIIC = 0.5 &WCII = 0.3) is used. The result of the statistical parameter of each target function is compared with each other. The simultaneous application of the index of ideality of correlation (IIC) and correlation intensity index (CII) improves the predictive potential of the model. The best model is judged on the basis of the numerical value of R2 of the validation set. The statistical result of the best model for the validation set of split 6 computed by TF3 (WIIC = 0.5 &WCII = 0.3) is R2 = 0.9308, CCC = 0.9588, IIC = 0.7704, CII = 0.9549, Q2 = 0.9281 and RMSE = 0.544. The promoters of increase/decrease for RI are also extracted using the best model (split 6).