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

Soo Kweon Lee, Ju Hun Lee, Hyeong Ryeol Kim, Youngsang Chun, Ja Hyun Lee, Chulhwan Park, Hah Young Yoo, Seung Wook Kim

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number24157
JournalScientific reports
Volume11
Issue number1
DOIs
Publication statusPublished - 2021 Dec

ASJC Scopus subject areas

  • General

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