Acta Optica Sinica, Volume. 43, Issue 18, 1812002(2023)

Microfluidics-Microfluorescence-Based Method for Density Detection of Planktonic Algal Cells

Peng Huang1,2, Gaofang Yin1,2,3、*, Nanjing Zhao1,2,3,4, Tingting Gan2,3, Xiang Hu1,2, Min Xu2,3, Tianhong Liang2,3, Renqing Jia2,3, and Xiaoling Zhang2,4
Author Affiliations
  • 1School of Biological Food and Environment, Hefei University, Hefei 230601, Anhui, China
  • 2Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, Anhui, China
  • 3University of Science and Technology of China, Hefei 230026, Anhui, China
  • 4Institutes of Material Science and Information Technology, Anhui University, Hefei 230601, Anhui, China
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    Objective

    As indicators of the ecological health of water bodies, planktonic algae are important primary producers in water ecosystems. The density monitoring of planktonic algae is of great significance to the diagnosis of water quality and the warning of algal blooms. Due to the presence of small individual and large numbers of planktonic algae, suspended impurities and other factors, traditional methods are difficult to achieve rapid and accurate measurements. Flow cytometric fluorescence method counts by detecting single cell fluorescence. This method features rapid, accurate, and highly efficient measurement, but it is not suitable for miniaturized field rapid measurement because of its complex injection structure and cumbersome focusing mode. Microfluidic chip technology realizes the functions of feeding, focusing, and sorting by constructing micro-channel pipelines on a square centimeter chip. This technology can simplify the complex feeding structure of the flow fluorescence method and has been widely employed in pharmaceutical and life science fields. Based on chlorophyll fluorescence in the characteristic band emitted by excited algal cells, this paper combines microfluidic chip technology and microfluorescence detection technology. It aims to realize rapid and accurate density detection of planktonic algal cells by detecting the number of single algal cell fluorescence peaks at a specific volume under a simple structure.

    Methods

    The experimental system consists of a sample feeding module, a fluorescence excitation module, and a fluorescence detection module. The excitation light from the monochromatic high-brightness LD is focused on the surface of the microfluidic channel by the drop-in microscopic optical structure. The algal cells in the microfluidic channel pass through the excitation window at a uniform speed under the propulsion of the syringe pump, and the cells are excited to emit fluorescence. Each cell flow across the microscopic field of view corresponds to a fluorescence peak, and then the density of algal cells in the sample can be calculated by recording the number of fluorescence peaks for a specific volume of the sample.

    Results and Discussions

    A method of detecting the planktonic algae density based on microfluidic and microfluorescence technology is studied to realize rapid and accurate density measurement of planktonic algal cells. By microfluidic chips, injection pumps, objective lenses, and photomultiplier tubes, an experimental system is established to measure the fluorescence signals of algal cells with different densities. Combined with optical simulations, this method can accurately measure the fluorescence signals of algal cells with low and medium densities. The relative errors of the counting results at low densities are less than 3.49% compared with those of microscopy and Coulter counting (Table 1), and the results of testing algal cells of different species and particle sizes show that the relative errors of the method in the density range of 1.3×106 L-1 are less than 3.96%. All of them were less than 3.96%, and the accuracy is not affected by the suspended matter, algal cell species, and size (Fig. 5). With the expanding range of testing algal density samples, the measurement error shows an increasing trend, which means that the measurement accuracy of the method and algal density is negatively correlated (Fig. 6). This is consistent with the simulation results, and the upper limit of the detection density is increased to 5×106 L-1 within the allowable error range of 10% (Fig. 7).

    Conclusions

    Due to the existence of small individual and large numbers of planktonic algae, suspended impurities and other factors, it is difficult to accurately detect algal density through the currently available algal density rapid detection technology. This paper proposes a microfluidics-microfluorescence-based method for planktonic algae cell density detection. This method is also based on microfluidics for rapid quantitative sample injection, confocal microfluorescence structure for high signal-to-noise acquisition of algal cell characteristic fluorescence signals, and the analysis of fluorescence peak information for planktonic algae cell counting. The results show that the relative measurement error in the density range of 1.3×106 L-1 is less than 3.96%, and the accuracy is not affected by the suspended matter, algal cell type, and size. The upper limit of algal density detection can be increased to 5×106 L-1 with the allowable error range of 10%, which can meet the demand of natural water bodies. The proposed microfluidic-microfluorescence technology that employs the unique chlorophyll fluorescence information of algae effectively overcomes the interference of suspended matters and features simple feeding module and optical structure. It is a new way for rapid and accurate detection of algal cell density.

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    Peng Huang, Gaofang Yin, Nanjing Zhao, Tingting Gan, Xiang Hu, Min Xu, Tianhong Liang, Renqing Jia, Xiaoling Zhang. Microfluidics-Microfluorescence-Based Method for Density Detection of Planktonic Algal Cells[J]. Acta Optica Sinica, 2023, 43(18): 1812002

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Nov. 16, 2022

    Accepted: Feb. 6, 2023

    Published Online: Sep. 11, 2023

    The Author Email: Yin Gaofang (gfyin@aiofm.ac.cn)

    DOI:10.3788/AOS221995

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