结合优化算法的数据驱动模型可能是预测和优化体外培养过程的有益方法。本研究旨在建立和优化一种新的菊花胚胎发生培养基。三个单独的数据驱动模型，包括多层感知器(MLP)，自适应神经模糊推理系统(ANFIS)，和支持向量回归(SVR)，被开发用于愈伤组织发育率(CR)，胚胎发生率（ER），和体细胞胚胎数（SEN）。因此,通过装袋方法将获得的最佳结果用于融合过程。对于培养基重新配制，八种离子大量营养素对CR的影响，ER,使用数据融合（DF）-非支配排序遗传算法-II（NSGA-II）和DF-遗传算法（GA）评估了四种维生素对SEN的影响，分别。结果表明，与所有其他单个模型相比，具有最高R2的DF模型具有出色的性能。根据DF-NSGAII，最高的ER和SEN可以从含有14.27mMNH4+的培养基中获得，38.92mMNO3-,22.79mMK+，5.08mMCl-,3.34mMCa2+，1.67mMMg2+，2.17mMSO42-，和1.44mMH2PO4-。基于DF-GA模型，最大SEN可以从含有0.61μM硫胺素的培养基中获得，5.93μM烟酸，0.25μM生物素，和0.26μM核黄素。通过实验将已建立的优化培养基的效率与Murashige和Skoog培养基（MS）进行了比较，以用于五个菊花品种的胚胎发生，结果表明，优化培养基优于MS培养基。关键点•MLP,SVR,和ANFIS通过装袋方法进行融合，以开发数据融合模型。•将NSGA-II和GA与数据融合模型相关联,用于建立和优化新的胚胎发生培养基。•新培养基(HNT)具有比MS培养基更好的效率。
Data-driven models in a combination of optimization algorithms could be beneficial methods for predicting and optimizing in vitro culture processes. This study was aimed at modeling and optimizing a new embryogenesis medium for chrysanthemum. Three individual data-driven models, including multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR), were developed for callogenesis rate (CR), embryogenesis rate (ER), and somatic embryo number (SEN). Consequently, the best obtained results were used in the fusion process by a bagging method. For medium reformulation, effects of eight ionic macronutrients on CR, ER, and SEN and effects of four vitamins on SEN were evaluated using data fusion (DF)-non-dominated sorting genetic algorithm-II (NSGA-II) and DF-genetic algorithm (GA), respectively. Results showed that DF models with the highest R2 had superb performance in comparison with all other individual models. According to DF-NSGAII, the highest ER and SEN can be obtained from the medium containing 14.27 mM NH4+, 38.92 mM NO3-, 22.79 mM K+, 5.08 mM Cl-, 3.34 mM Ca2+, 1.67 mM Mg2+, 2.17 mM SO42-, and 1.44 mM H2PO4-. Based on the DF-GA model, the maximum SEN can be obtained from a medium containing 0.61 μM thiamine, 5.93 μM nicotinic acid, 0.25 μM biotin, and 0.26 μM riboflavin. The efficiency of the established-optimized medium was experimentally compared to Murashige and Skoog medium (MS) for embryogenesis of five chrysanthemum cultivars, and results indicated the efficiency of optimized medium over MS medium.Key points• MLP, SVR, and ANFIS were fused by a bagging method to develop a data fusion model.• NSGA-II and GA were linked to the data fusion model for establishing and optimizing a new embryogenesis medium.• The new culture medium (HNT) had better efficiency than MS medium.
