Abstract
BackgroundPredicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media. Previous studies have focused on predicting the impact of media components on explant growth via conventional multi-layer perceptron neural networks (MLPNN) and Multiple Linear Regression (MLR) methods. So, there is an opportunity to find more efficient algorithms such as Radial Basis Function Neural Network (RBFNN) and Gene Expression Programming (GEP). Here, a novel algorithm, i.e. GEP which has not been previously applied in plant tissue culture researches was compared to RBFNN and MLR for the first time. Pear rootstocks (Pyrodwarf and OHF) were used as case studies on predicting the effect of minerals and some hormones in the culture medium on proliferation indices.ResultsGenerally, RBFNN and GEP showed extremely higher performance accuracy than the MLR. Moreover, GEP models as the most accurate models were optimized using genetic algorithm (GA). The improvement was mainly due to the RBFNN and GEP strong estimation capability and their superior tolerance to experimental noises or improbability.ConclusionsGEP as the most robust and accurate prospecting procedure to achieve the highest proliferation quality and quantity has also the benefit of being easy to use.
Highlights
Predicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media
That, NO3− was found to be critical for OHF explant while NH4+ was found to be critical nutrient for Pyrodwarf explant growth and we suggested that the use of Artificial neural networks (ANN)-based model analyses would lead us to detect the optimized macronutrient concentrations essential to maximize the proliferation rate (PR) and shoot length (SL) and minimize the occurrence of shoot tip necrosis (STN) and Vitri [10]
The main objective of this paper was to compare the performance of Multiple Linear Regression (MLR), Radial Basis Function Neural Network (RBFNN) and Gene Expression Programming (GEP) models for predicting the concentrations of in vitro medium components to achieve the optimal growth parameters
Summary
Predicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media. Wada et al [20] found that some pear cultivars require high NO3− (52 to 60 mM), low to moderate N H4+ (data not shown) and high mesos concentrations (1.5 × MS) for the best growth results. Another approach for optimization of MS medium was performed by [21] who suggested that adding meta-topolin (6–9 μM), an aromatic cytokinin, to MS medium increases significantly the multiplication rate and shoot quality in OHF-333 (another clonal selection of Old Home × Farmingdale)
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