从Leuconostoc mesenteroides ABNFT-1使用统计和基于人工智能的模型优化外聚糖的多种方法生产
Ankit Barot1, Kalyan Das1, Yogesh Patel2
1Department of Interdisciplinary Sciences, National Institute of Food Technology Entrepreneurship and Management (NIFTEM), Kundli-Sonipat, India.
Preparative biochemistry & biotechnology
|October 22, 2025
概括
这项研究使用混合方法优化了Leuconostoc mesenteroides ABNFT-1的外聚糖 (EPS) 生产. 机器学习模型,特别是Levenberg-Marquardt算法,显著提高了工业生物聚合物应用的EPS产量.
科学领域:
- 微生物学 微生物学
- 生物技术是生物技术.
- 聚合物科学 聚合物科学
背景情况:
- 外聚糖 (EPS) 是微生物聚合物,具有多种工业应用.
- 优化EPS生产对于具有成本效益的生物聚合物合成至关重要.
- 乐可诺斯托克中类固醇ABNFT-1被确定为一个高产量的EPS生产者.
研究的目的:
- 为了增强来自Leuconostoc mesenteroides ABNFT-1.的外聚糖 (EPS) 生产.
- 使用多步骤策略,系统地优化关键过程参数.
- 为了比较不同的人工神经网络 (ANN) 算法对EPS收益的预测准确度.
主要方法:
- 一个因素一次 (OFAT) 选用于初始参数评估.
- 响应表面方法 (RSM) 与中央复合设计 (CCD) 进行优化.
- 人工神经网络 (ANN) 建模 (LMA,SCGA,BRA) 用于产量预测.
主要成果:
- 在优化条件下 (10%的注射液,20%的糖,4%的KNO3,0.04%的FeSO4) 产生了21.74g/L的EPS.
- 使用LMA的ANN建模预测EPS的最大收益率为23.66g/L.
- 与SCGA和BRA相比,LMA显示出更高的预测准确度.
结论:
- 结合统计方法和机器学习的混合优化策略是最大限度地提高EPS生产的有效方法.
- 优化过程为EPS收益率带来了显著的改善.
- 这项研究为使用微生物EPS的工业规模生物聚合物合成提供了基础.
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