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首页> 外文会议>International Symposium on Medical Measurements and Applications >Intelligent Pipetting System Towards Automatic Liquid Handling Applications
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Intelligent Pipetting System Towards Automatic Liquid Handling Applications

机译：面向自动液体处理应用的智能移液系统

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摘要

Automation of life sciences and pharmaceutical laboratories is high on demand in the present day, especially with the emergence of high throughput screening and drug discovery. Lab automation provides reliable and efficient solutions, limiting reagent amounts with increased throughput and reduced costs. In this work, we present an affordable, intelligent, sub-microlitre resolution automatic pipetting system. This system employs a differential pressure sensor to detect most frequently occurring pipetting errors in automatic liquid handling workstations, which include no liquid sample present in the source well, pipette tip not attached to the holder, clogging of the tip, aspiration of air bubbles or foam, insufficient liquid in the source well, improper dispensing of the samples and tip not intact with the holder. This also determines liquid volumes aspirated into the pipette tip, providing a self-validating mechanism for the system, thereby reducing human intervention and improving performance efficiency. A prototype system was developed in-house and gravimetric analysis was carried out to determine system performance. Repeatability coefficient of variation for the designed system was found to be below 5.5% for the volume range of 10 to 200 microlitres, while the error obtained was observed to be less than 1.6%. Experimental tests conducted could flawlessly detect mentioned pipetting abnormalities through the algorithm designed.
机译：如今，生命科学和制药实验室的自动化需求量很大，特别是随着高通量筛选和药物发现的出现。实验室自动化提供了可靠而有效的解决方案，从而限制了试剂用量，从而提高了通量并降低了成本。在这项工作中，我们提出了一种价格合理的，智能的，亚微升分辨率的自动移液系统。该系统采用差压传感器来检测自动液体处理工作站中最经常发生的移液错误，其中包括源井中不存在液体样品，移液器吸头未连接到固定器，吸头堵塞，气泡或泡沫抽吸，源井中的液体不足，样品分配不当以及针尖与支架未完好无损。这也确定了吸液到吸液管尖端的体积，为系统提供了一种自我验证机制，从而减少了人为干预并提高了性能效率。内部开发了原型系统，并进行了重量分析以确定系统性能。对于10到200微升的体积范围，发现所设计系统的重复性变异系数低于5.5％，而观察到的误差则低于1.6％。通过设计的算法，进行的实验测试可以完美地检测出提到的移液异常。

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