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Ultrafast time-encoded flow imaging for Giardia cysts and Cryptosporidium oocysts detection
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Paper Abstract

The harmful effects of Cryptosporidium oocysts and Giardia cysts in drinking water have been widely concerned by the international community. Currently, the EPA1623 method is one of the most mature and authoritative methods for detecting Cryptosporidium oocysts and Giardia cysts internationally. However, this method has the limitations of high cost of time and human labor. Based on ultrashort pulse time-space-frequency mapping principle, ultrafast time-encoded flow imaging can reach high speed and high resolution. Therefore, it is proposed for replacing the last three steps of EPA1623, which are immunomagnetic separation, fluorescent staining and enumeration. Specifically, mixed with immunomagnetic beads, the liquid quality sample of Giardia cysts and Cryptosporidium oocysts flow through the microfluidic channel with high throughput of 100 particles/s. With ultrafast time-encoded flow imaging system, images are acquired including oocysts and cysts which are magnetized by attachment of magnetic beads or not, and only magnetic beads. Extracted appearance and shape features, images are classified by K-means cluster algorithm. It is shown in results that, ultrafast time-encoded flow imaging method costs less than 10 minutes and maintains recovery at more than 80%, compared to the last three steps in EPA1623 which need almost 2 hours at less recovery. The proposed method makes full use of the biological properties of immunomagnetic beads, Cryptosporidium oocysts and Giardia cysts, and maintains high percent recovery with much shorter detection time.

Paper Details

Date Published: 20 November 2019
PDF: 7 pages
Proc. SPIE 11192, Real-time Photonic Measurements, Data Management, and Processing IV, 111920S (20 November 2019); doi: 10.1117/12.2536789
Show Author Affiliations
Yingxue Guo, Tsinghua Univ. (China)
Wanyue Zhao, Tsinghua Univ. (China)
Xiaohong Zhou, Tsinghua Univ. (China)
Yun Lu, Tsinghua Univ. (China)
Minghua Chen, Tsinghua Univ. (China)
Sigang Yang, Tsinghua Univ. (China)
Hongwei Chen, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 11192:
Real-time Photonic Measurements, Data Management, and Processing IV
Ming Li; Bahram Jalali; Mohammad Hossein Asghari, Editor(s)

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