封装Trichoderma spp.的活力和预测. 使用机器学习的孤立:基于多层感知子的多层方法.
Thalesram Izidoro Pinotti1, Fábio Sandro Dos Santos1, Yanka Manoelly Dos Santos Gaspar1
1Campus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus, 64900-000, PI, Brazil.
微生物生物产品的稳定性通过微封装和人工神经网络来提高. 这种方法有助于预测存储期间的真菌生存能力,优化生物产品的发展.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 生物技术是生物技术.
背景情况:
- 微生物生物产品的开发需要了解储存期间的细胞稳定性和活力.
- 三皮菌 spp. 三皮菌 是有价值的微生物,在各种生物制品中具有应用.
- 皮阿维塞拉多原生植物拥有多样化的微生物群落.
研究的目的:
- 为了评估封装Trichoderma spp.的生存能力. 来自皮阿维塞拉多的菌株.
- 将微封装技术与稳定性评估的计算建模相结合.
- 探索人工神经网络在预测微生物生存能力的潜力.
主要方法:
- 在Trichoderma spp.的微封装中. 在甲基酸盐基质中使用离子凝.
- 通过涂层和60天的状计数来监测真菌活力.
- 训练一个多层感知器 (MLP) 人工神经网络,使用特定的规范化和退出技术.
主要成果:
- 菌株UFPI07,UFPI10,UFPI11和UFPI16表现出优越的生存能力 (>7 log CFU mL-1).
- 菌株UFPI06和UFPI18的生存能力下降更为显著.
- 该MLP模型提供了超越实验期的微生物趋势的探索性预测.
结论:
- 微封装和机器学习的结合为估计时间微生物行为提供了一个有希望的工具.
- 这种综合方法可以帮助优化生物制品配方并降低实验成本.
- 它代表了预测性微生物学和生物产品开发的探索性方法学的进步.
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