论文标题

使用全息图和深度学习的基于虚拟影响器的无标签生物大赛检测

Virtual impactor-based label-free bio-aerosol detection using holography and deep learning

论文作者

Luo, Yi, Zhang, Yijie, Liu, Tairan, Yu, Alan, Wu, Yichen, Ozcan, Aydogan

论文摘要

暴露于霉菌孢子和花粉等生物 - 大紫胶会导致不利的健康影响。需要使用便携式且具有成本效益的设备来长期监测和量化各种生物铝溶胶。为了满足这一需求,我们提出了一种移动且具有成本效益的无标签生物透射剂传感器,该传感器拍摄了由虚拟撞击器集中的流动颗粒物的全息图像,该图像有选择性地减慢并引导大于〜6微米的颗粒以飞行成像窗口。流动的颗粒被脉冲激光二极管照亮,在无镜头移动成像设备中的CMOS图像传感器上施放了其内联全息图。该照明包含三个短脉冲,在一个脉冲中流动粒子可以忽略不计,同一粒子的一式三份全息图记录在单个框架上,然后才能退出成像视野视野,从而揭示了每个粒子的不同视角。虚拟撞击器中的颗粒通过差异检测方案进行定位,深层神经网络基于获得的全息图像以无标签方式对气溶胶类型进行了分类。我们证明了使用不同类型的花粉(即,百慕大,榆树,橡木,松树,遮阳篷和小麦)使用虚拟撞击器的这项移动生物 - 大气探测器的成功,并实现了92.91%的盲目分类精度。这种移动和成本效益的设备重约700 g,可用于长时间的各种生物 - 毛溶胶的无标记感应和量化,因为它基于没有捕获或固定颗粒物的无弹药虚拟撞击器。

Exposure to bio-aerosols such as mold spores and pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and quantification of various bio-aerosols. To address this need, we present a mobile and cost-effective label-free bio-aerosol sensor that takes holographic images of flowing particulate matter concentrated by a virtual impactor, which selectively slows down and guides particles larger than ~6 microns to fly through an imaging window. The flowing particles are illuminated by a pulsed laser diode, casting their inline holograms on a CMOS image sensor in a lens-free mobile imaging device. The illumination contains three short pulses with a negligible shift of the flowing particle within one pulse, and triplicate holograms of the same particle are recorded at a single frame before it exits the imaging field-of-view, revealing different perspectives of each particle. The particles within the virtual impactor are localized through a differential detection scheme, and a deep neural network classifies the aerosol type in a label-free manner, based on the acquired holographic images. We demonstrated the success of this mobile bio-aerosol detector with a virtual impactor using different types of pollen (i.e., bermuda, elm, oak, pine, sycamore, and wheat) and achieved a blind classification accuracy of 92.91%. This mobile and cost-effective device weighs ~700 g and can be used for label-free sensing and quantification of various bio-aerosols over extended periods since it is based on a cartridge-free virtual impactor that does not capture or immobilize particulate matter.

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