Predicting In vitro Culture Medium Macro-Nutrients Composition for Pear Rootstocks Using Regression Analysis and Neural Network Models

Two modeling techniques [artificial neural network-genetic algorithm (ANN-GA) and stepwise regression analysis] were used to predict the effect of medium macro-nutrients on in vitro performance of pear rootstocks (OHF and Pyrodwarf). The ANN-GA described associations between investigating eight macronutrients (NO [Formula: see text] , NH [Formula: see text] , Ca(2+), K(+), Mg(2+), PO [Formula: see text] , SO [Formula: see text] , and Cl(−)) and explant growth parameters [proliferation rate (PR), shoot length (SL), shoot tip necrosis (STN), chlorosis (Chl), and vitrification (Vitri)]. ANN-GA revealed a substantially higher accuracy of prediction than for regression models. According to the ANN-GA results, among the input variables concentrations (mM), NH [Formula: see text] (301.7), and NO [Formula: see text] , NH [Formula: see text] (64), SO [Formula: see text] (54.1), K(+) (40.4), and NO [Formula: see text] (35.1) in OHF and Ca(2+) (23.7), NH [Formula: see text] (10.7), NO [Formula: see text] (9.1), NH [Formula: see text] (317.6), and NH [Formula: see text] (79.6) in Pyrodwarf had the highest values of VSR in data set, respectively, for PR, SL, STN, Chl, and Vitri. The ANN-GA showed that media containing (mM) 62.5 NO [Formula: see text] , 5.7 NH [Formula: see text] , 2.7 Ca(2+), 31.5 K(+), 3.3 Mg(2+), 2.6 PO [Formula: see text] , 5.6 SO [Formula: see text] , and 3.5 Cl(−) could lead to optimal PR for OHF and optimal PR for Pyrodwarf may be obtained with media containing 25.6 NO [Formula: see text] , 13.1 NH [Formula: see text] , 5.5 Ca(2+), 35.7 K(+), 1.5 Mg(2+), 2.1 PO [Formula: see text] , 3.6 SO [Formula: see text] , and 3 Cl(−).