随着时间的推移,菌生物复合物生长的非破坏性量化
Lindsay E Pierce1, Anna Folley1, Liza R White1,2
1Department of Chemical and Biomedical Engineering, University of Maine, Orono, Maine, USA.
Biotechnology and bioengineering
|November 14, 2025
概括
这项研究引入了自动化的深度学习,用于量化生物复合材料中的菌株生长. 这种非破坏性方法可以进行精确的监测,改善可持续材料工程和采用.
科学领域:
- 材料科学 材料科学 材料科学
- 生物技术是生物技术.
- 可持续制造 可持续制造 可持续制造
背景情况:
- 菌根生物复合材料为传统材料提供环保的替代品.
- 准确的,非破坏性的生长监测对于高效的生物复合材料制造至关重要.
- 现有的量化方法往往具有破坏性或缺乏精度.
研究的目的:
- 开发和评估用于量化木粉生物复合材料中菌生长的非破坏性方法.
- 为了比较手动,算法和深度学习方法进行增长评估.
- 为了证明自动定量用于分析基质补充效果的实用性.
主要方法:
- 图像分析被用来定义和跟踪菌密度水平 (低,中,高).
- 手动掩盖,算法掩盖和深度学习模型被用于量化.
- 应用了深度学习模型来评估基质补充剂对生长模式的影响.
主要成果:
- 手动掩盖提供了快速但粗略的估计,用户变化很低.
- 算法掩盖改进了细节,但增加了可变性.
- 深度学习模型提供了最快,最一致的量化,消除了用户的变化,并揭示了由基质补充物影响的独特生长模式.
结论:
- 自动化深度学习量化对于菌生物复合材料是有效的.
- 这种非破坏性的方法使得可重复的,高分辨率的生长监测成为可能.
- 这些发现支持精确的工程和更广泛地采用菌根生物复合材料.
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