Virtual Impactor-Based Label-Free Pollen Detection using Holography and Deep Learning

Abstract

Exposure to bio-aerosols such as 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 types of pollen. To address this need, we present a mobile and cost-effective label-free sensor that takes holographic images of flowing particulate matter (PM) concentrated by a virtual impactor, which selectively slows down and guides particles larger than 6 μm to fly through an imaging window. The flowing particles are illuminated by a pulsed laser diode, casting their inline holograms on a complementary metal-oxide semiconductor 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 pollen type in a label-free manner based on the acquired holographic images. We demonstrated the success of this mobile pollen 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 PM.

Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy

“…That is especially noticeable on larger data sets with a higher number of pollen taxa. Deep learning methods have been also used successfully to classify pollen types from holographic images of flowing particle 28 in a mobile and cost-effective sensor, as well as to classify pollen types from scattering images 29 . Recent approaches in the field of automatic pollen classification utilize multi-modal identification, for example combining light-induced fluorescence and elastic light scattering data as in Rapid-E (Plair SA, Geneva, Switzerland) 8 , or adding also holography on these as in Poleno (Swisens AG, Horw, Switzerland) 30 .…”

Photonic system for real-time detection, discrimination, and quantification of microbes in air

“…Addressing the need for a portable and cost-effective device for long-term monitoring and quantification of various types of pollen which affect human health, Lou et al [38] presented a label-free sensor that takes holographic images of flowing particulate matter (PM) concentrated by a virtual impactor, which selectively guides particles of a specific size (larger than 6 μm) to fly through an imaging window. The inline holograms cast on a CMOS image sensor due to the flowing particles illuminated by a pulsed laser diode enable particle detection based on deep learning techniques.…”

“…However, this method has an underlying assumption that the variable chosen FIGURE 1 AUM photonic system [comprising of a continuous wave laser light source at the bottom on the front side, to illuminate the target volume of air, and, a position sensing photodetector at the top on the front side, to detect the back scattered laser light from the target volume. Optical filters in front of both laser source and the photodetector ensures that only the particular wavelength of light from the source passes through and falls back on the photodetector after scattering from microbes in target volume of air [Picture adopted from Tatavarti, 2021 [38]].…”