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一个自动化和智能化的微流体平台用于微藻检测和监测

Jiahao Zheng¹, Tim Cole¹, Yuxin Zhang¹

¹Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.

|December 7, 2023

概括

一个新的自动化和智能化微流体平台 (AIMP) 能够实时地在现场检测和监测微藻. 这种便携式设备准确地识别物种并监测阿斯塔克桑的生产,克服了传统的基于实验室的限制.

科学领域:

环境科学环境科学
生物技术是生物技术.
微流体学微流体学

背景情况:

传统的微藻检测是耗时和劳动密集的,需要实地样本进行实验室分析.
现有的微藻检测机器学习 (ML) 方法通常是基于实验室的,缺乏集成的实时现场功能.
在生态和商业应用中,需要便携式自动化解决方案来有效监测微藻.

研究的目的:

开发一个自动化和智能化微流体平台 (AIMP) 实时,现场微藻检测和监测.
创建一种经济且便携的解决方案,克服传统微藻分析方法的局限性.
展示平台在识别多种微藻物种和监测商业相关化合物的能力.

主要方法:

开发一个自动化和智能化微流体平台 (AIMP),具有自动控制,智能数据分析和用户交互.
使用基于YOLOv5架构的微藻物种检测网络 (MSDN) 进行基于图像的分类.
实施长期监测30天的阿斯塔桑生产在血球球菌Pluvialis.

主要成果:

该AIMP成功地检测和分类了四种微藻种 (*Cosmarium*, *Closterium*, *Micrasterias*和 *Haematococcus Pluvialis*) 的高精度.
在微藻物种检测方面,MSDN的平均精度 (mAP@0.5) 为92.8%.
该平台在检测Hematococcus Pluvialis时显示了97.5%的准确性,在分类阿斯塔克桑积累水平时显示了96.3%的准确性.

结论:

AIMP为微藻检测和监测提供了一种经济,便携和智能解决方案,大大改善了传统方法.
这项技术可以实时进行现场分析,加速生态研究和商业微藻利用的进展.
该研究提出了一种使用便携式智能设备进行微藻分析的新方法,为未来的进展铺平了道路.

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