Saber WesamEldin I A, Al-Askar Abdulaziz A, Ghoneem Khalid M
Microbial Activity Unit, Microbiology Department, Soils, Water and Environment Research Institute, Agricultural Research Center, Giza 12619, Egypt.
Botany and Microbiology Department, Faculty of Science, King Saud University, Riyadh 11451, Saudi Arabia.
Polymers (Basel). 2023 Mar 13;15(6):1419. doi: 10.3390/polym15061419.
Pullulan is a biodegradable, renewable, and environmentally friendly hydrogel biopolymer, with potential uses in food, medicine, and cosmetics. New endophytic (accession number; OP924554) was used for the biosynthesis of pullulan. Innovatively, the fermentation process was optimized using both Taguchi's approach and the decision tree learning algorithm for the determination of important variables for pullulan biosynthesis. The relative importance of the seven tested variables that were obtained by Taguchi and the decision tree model was accurate and followed each other's, confirming the accuracy of the experimental design. The decision tree model was more economical by reducing the quantity of medium sucrose content by 33% without a negative reduction in the biosynthesis of pullulan. The optimum nutritional conditions (g/L) were sucrose (60 or 40), KHPO (6.0), NaCl (1.5), MgSO (0.3), and yeast extract (1.0) at pH 5.5, and short incubation time (48 h), yielding 7.23% pullulan. The spectroscopic characterization (FT-IR and H-NMR spectroscopy) confirmed the structure of the obtained pullulan. This is the first report on using Taguchi and the decision tree for pullulan production by a new endophyte. Further research is encouraged for additional studies on using artificial intelligence to maximize fermentation conditions.
普鲁兰多糖是一种可生物降解、可再生且环保的水凝胶生物聚合物，在食品、医药和化妆品领域具有潜在用途。新的内生菌（登录号：OP924554）被用于普鲁兰多糖的生物合成。创新之处在于，使用田口方法和决策树学习算法对发酵过程进行了优化，以确定普鲁兰多糖生物合成的重要变量。通过田口方法和决策树模型获得的七个测试变量的相对重要性是准确的且相互一致，证实了实验设计的准确性。决策树模型更经济，通过将培养基蔗糖含量减少33%，而普鲁兰多糖的生物合成没有负面减少。最佳营养条件（g/L）为蔗糖（60或40）、KHPO（6.0）、NaCl（1.5）、MgSO（0.3）和酵母提取物（1.0），pH值为5.5，培养时间短（48小时），可产生7.23%的普鲁兰多糖。光谱表征（傅里叶变换红外光谱和氢核磁共振光谱）证实了所得普鲁兰多糖的结构。这是关于使用田口方法和决策树通过新的内生菌生产普鲁兰多糖的首次报道。鼓励进一步研究利用人工智能来优化发酵条件。
