Acta Optica Sinica, Volume. 42, Issue 6, 0617001(2022)
Analytical Method of Viable Algae Cells in Water Based on Variable Fluorescence Statistical Distribution
There is a good correlation between the amount of variable fluorescence and the number of viable algae cells. However, under the conditions of different species, size, and growth cycle of phytoplankton, there is a significant difference in the amount of variable fluorescence of single algae cell, so there will be a big error in calculating the number of viable algae cells by using variable fluorescence intensity directly. Therefore, an analytical method of viable algae cells in water based on variable fluorescence statistical distribution is proposed. According to the characteristic that the distribution shape of the number of cells in the subsample is consistent with the variable fluorescence amount, the distribution shape of the variable fluorescence amount is used to directly calculate the number of viable algal cells in the sample. The results show that under the condition that the variable fluorescence amount of single cell of phytoplankton changes 44 times, the cell density of viable algae cells calculated by the variable fluorescence statistical analysis method is basically consistent with the microscopic results. The correlation coefficient of linear fitting between them is above 0.93, and the average absolute value of relative error is between 5.98% and 16.94%. The results show that the proposed method basically solves the effect of variable fluorescence of algae single cell on the counting results of viable algae cells.
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Lu Wang, Gaofang Yin, Nanjing Zhao, Tingting Gan, Peilong Qi, Zhichao Ding, Hui Hua, Min Chen, Mingjun Ma, Ruifang Yang, Li Fang. Analytical Method of Viable Algae Cells in Water Based on Variable Fluorescence Statistical Distribution[J]. Acta Optica Sinica, 2022, 42(6): 0617001
Category: Medical optics and biotechnology
Received: Aug. 31, 2021
Accepted: Oct. 8, 2021
Published Online: Mar. 8, 2022
The Author Email: Yin Gaofang (gfyin@aiofm.ac.cn), Zhao Nanjing (njzhao@aiofm.ac.cn)