自动化
计算机科学
软件
鉴定(生物学)
吞吐量
一致性(知识库)
范围(计算机科学)
微流控
实时计算
工艺工程
遥感
系统工程
人工智能
纳米技术
工程类
生态学
材料科学
生物
电信
程序设计语言
地质学
无线
机械工程
作者
Benjamin Gincley,Farhan Ahmed Khan,Elaine Hartnett,Autumn Fisher,Ameet Pinto
标识
DOI:10.1021/acs.est.4c01928
摘要
Manual microscopy is the gold standard for phytoplankton monitoring in diverse engineered and natural environments. However, it is both labor-intensive and requires specialized training for accuracy and consistency, and therefore difficult to implement on a routine basis without significant time investment. Automation can reduce this burden by simplifying the measurement to a single indicator (e.g., chlorophyll fluorescence) measurable by a probe, or by processing samples on an automated cytometer for more granular information. The cost of commercially available flow imaging cytometers, however, poses a steep financial barrier to adoption. To overcome these labor and cost barriers, we developed ARTiMiS: the Autonomous Real-Time Microbial 'Scope. The ARTiMiS is a low-cost flow imaging microscopy-based platform with onboard software capable of providing taxonomically resolved quantitation of phytoplankton communities in real-time. ARTiMiS leverages novel multimodal imaging and onboard machine learning-based data processing that is currently optimized for a curated and expandable database of industrially relevant microalgae. We demonstrate its operational limits, performance in identification of laboratory-cultivated microalgae, and potential for continuous monitoring of complex microalgal communities in full-scale industrial cultivation systems.
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