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Low false positive and accurate detection of yeast cell viability and concentration using an automatic staining and lensfree imaging platform.

Abstract：

:Yeast cell viability and concentration are the crucial factors affecting product quality in food industry and bio-fuel production, as well as the evaluation basis for environmental toxic compounds. To overcome the drawbacks of existing methods, including high error, false positive and low automation, we propose a highly accurate approach based on an automatic staining and high-throughput lensfree imaging platform. A precisely controlled staining process is implemented automatically, which largely avoids the error caused by inappropriate exposure times. Based on optical simulation analysis, energy distribution characteristics are proposed. They are better with steady theoretical evidence for live yeast cell recognition. The parameters are directly extracted from raw cell fingerprints without any reconstruction. Those progresses improve robustness and increase efficiency. Availability of this approach is validated by compared the detection results with gold-standard PI counting method in a H2O2 toxicity test. So it is expected to be widely used in industrial production and environmental toxicity assessment.

journal_name

Biochem Biophys Res Commun

journal_title

Biochemical and biophysical research communications

authors

Li G,Wei M,Sun J,Zhang Y,Zhang R

doi

10.1016/j.bbrc.2020.02.155

subject

Has Abstract

pub_date

2020-05-07 00:00:00

pages

793-799

issue

3

eissn

0006-291X

issn

1090-2104

pii

S0006-291X(20)30446-0

journal_volume

525

pub_type

杂志文章

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