使用回归分析和神经网络模型预测梨砧木的体外培养基大量营养素组成
Predicting In vitro Culture Medium Macro-Nutrients Composition for Pear Rootstocks Using Regression Analysis and Neural Network Models.
作者信息
Jamshidi S, Yadollahi A, Ahmadi H, Arab M M, Eftekhari M
机构信息
Department of Horticulture, Faculty of Agriculture, Tarbiat Modares University Tehran, Iran.
Department of Poultry Science, Faculty of Agriculture, Tarbiat Modares University Tehran, Iran.
出版信息
Front Plant Sci. 2016 Mar 29;7:274. doi: 10.3389/fpls.2016.00274. eCollection 2016.
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(-).
两种建模技术[人工神经网络-遗传算法（ANN-GA）和逐步回归分析]被用于预测培养基中大量营养素对梨砧木（OHF和Pyrodwarf）离体培养性能的影响。ANN-GA描述了所研究的八种大量营养素（硝酸根离子、铵根离子、钙离子、钾离子、镁离子、磷酸根离子、硫酸根离子和氯离子）与外植体生长参数[增殖率（PR）、茎长（SL）、茎尖坏死（STN）、黄化（Chl）和玻璃化（Vitri）]之间的关联。ANN-GA显示出比回归模型更高的预测准确性。根据ANN-GA的结果，在输入变量浓度（mM）中，OHF中的铵根离子（301.7）以及硝酸根离子、铵根离子（64）、硫酸根离子（54.1）、钾离子（40.4）和硝酸根离子（35.1），以及Pyrodwarf中的钙离子（23.7）、铵根离子（10.7）、硝酸根离子（9.1）、铵根离子（317.6）和铵根离子（79.6）在数据集中分别对PR、SL、STN、Chl和Vitri具有最高的VSR值。ANN-GA表明，含有（mM）62.5硝酸根离子、5.7铵根离子、2.7钙离子、31.5钾离子、3.3镁离子、2.6磷酸根离子、5.6硫酸根离子和3.5氯离子的培养基可使OHF获得最佳PR，而含有25.6硝酸根离子、13.1铵根离子、5.5钙离子、35.7钾离子、1.5镁离子、2.1磷酸根离子、3.6硫酸根离子和3氯离子的培养基可能使Pyrodwarf获得最佳PR。
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