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. 2021 Dec 17;11(1):24157.
doi: 10.1038/s41598-021-03512-4.

Rapid and concise quantification of mycelial growth by microscopic image intensity model and application to mass cultivation of fungi

Soo Kweon Lee  1 , Ju Hun Lee  1 , Hyeong Ryeol Kim  1 , Youngsang Chun  2 , Ja Hyun Lee  3 , Chulhwan Park  4 , Hah Young Yoo  5 , Seung Wook Kim  6
Affiliations

Rapid and concise quantification of mycelial growth by microscopic image intensity model and application to mass cultivation of fungi

Soo Kweon Lee et al. Sci Rep. .

Abstract

The microbial food fermentation industry requires real-time monitoring and accurate quantification of cells. However, filamentous fungi are difficult to quantify as they have complex cell types such as pellet, spores, and dispersed hyphae. In this study, numerous data of microscopic image intensity (MII) were used to develop a simple and accurate quantification method of Cordyceps mycelium. The dry cell weight (DCW) of the sample collected during the fermentation was measured. In addition, the intensity values were obtained through the ImageJ program after converting the microscopic images. The prediction model obtained by analyzing the correlation between MII and DCW was evaluated through a simple linear regression method and found to be statistically significant (R2 = 0.941, p < 0.001). In addition, validation with randomly selected samples showed significant accuracy, thus, this model is expected to be used as a valuable tool for predicting and quantifying fungal growth in various industries.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Schematic diagram showing direct and indirect cell quantification methods.
Figure 2
Figure 2
Original microscopic image of mycelial growth according to culture time of C. militaris KYL05 and transformed image for measuring intensity with Image J.
Figure 3
Figure 3
Correlation between microscopic image intensity (MII) of transformed images and dry cell weight (DCW) from quantification of mycelial growth.
Figure 4
Figure 4
Microscopic images of C. militaris KYL05 fermentation were selected from random samples of (A, B, C). The expected DCW was measured from the image intensity value. It was then compared with the real DCW. The degree of accuracy was 89.0% for (A), 93.7% for (B), and 91.7% for (C).
Figure 5
Figure 5
Effect of dilution factors (2, 5, 10, 102, 103) applied to the sample on intensity measurements. The R2 was 0.9493 for (A), 0.8973 for (B), 0.8606 for (C), and 0.8023 for (D).
See this image and copyright information in PMC

References

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