人工神经网络:优化和表征来自Bacillus velezensis物种α-氨酶的生产
Sasidhar Bhimana1, Saravanan Ravindran2
1Department of Pharmacy, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India.
Biotechnology and applied biochemistry
|February 10, 2025
概括
这项研究通过使用最终查设计 (DSD) 和人工神经网络 (ANN) 方法优化了Bacillus velezensis的α-amylase生产. 与传统技术相比,综合方法实现了酶产量的2.6倍增加.
科学领域:
- 生物技术是生物技术.
- 酶工程是什么? 酶工程是什么?
- 微生物发酵 微生物发酵
背景情况:
- 细菌 velezensis 是一个有前途的微生物来源,用于工业酶生产.
- 优化发酵条件对于最大限度地提高α-氨酶产量至关重要.
- 对于复杂的优化问题,传统的方法,如一次性一个因素 (OFAT) 通常是低效的.
研究的目的:
- 整合最终查设计 (DSD) 和人工神经网络 (ANN) 以优化α-氨酶的生产.
- 为了确定影响B. velezensis. 的α-氨酶生物合成的关键操作变量.
- 使用具有成本效益的农业固体基板,实现显著增加α-氨酶产量.
主要方法:
- 应用二级最终选设计 (DSD) 来评估九个操作变量.
- 利用人工神经网络 (ANN) 建模来进一步优化酶产量.
- 采用响应表面方法 (RSM) 进行过程变量分析.
- 通过实验执行优化条件的验证.
主要成果:
- 综合的DSD和ANN方法确定了最佳条件,产生1092.49U/mL的α-amylase.
- 这与OFAT方法 (418.25 U/mL) 相比增加了2.6倍.
- 纯化α-氨酶的特定活性增加了71.77倍,在pH值5.5和55°C时活性最佳.
结论:
- 结合的DSD-ANN方法提供了一个高效和有效的策略,以优化酶的生产.
- 这种方法显著提高了使用可持续基质的Bacillus velezensis的α-amylase产量.
- 特征的α-氨酶显示出有利于工业应用的特性.
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