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

Journal: ACS sensors
Published Date: Nov 22, 2022

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.

Authors

  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Yijie Zhang
    Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China; School of Psychology, South China Normal University, Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China. Electronic address: fanfandez@163.com.
  • Tairan Liu
    Department of Mechanical Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Alan Yu
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States.
  • Yichen Wu
    Department of Electrical Engineering, University of California Los Angeles (UCLA), USA. ozcan@ucla.edu.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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