Department of Chemical and Biological Engineering, Korea University, 145, Anam-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea.
Department of Interdisciplinary Bio-Micro System Technology, College of Engineering, Korea University, 145 Anam-Ro, Seongbuk-Gu, Seoul, 02841, Republic of Korea.
Sci Rep. 2021 Dec 17;11(1):24157. doi: 10.1038/s41598-021-03512-4.
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 (R = 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.
微生物食品发酵行业需要实时监测和准确量化细胞。然而，丝状真菌由于具有复杂的细胞类型，如颗粒、孢子和分散的菌丝，因此难以定量。在这项研究中，使用了大量的微观图像强度 (MII) 数据来开发一种简单而准确的虫草菌菌丝体定量方法。测量了发酵过程中收集的样品的干重 (DCW)。此外，通过将微观图像转换后，使用 ImageJ 程序获取强度值。通过分析 MII 和 DCW 之间的相关性获得的预测模型通过简单线性回归方法进行评估，结果具有统计学意义 (R = 0.941，p < 0.001)。此外，通过随机选择的样本进行验证显示出显著的准确性，因此，该模型有望成为预测和量化各种行业中真菌生长的有价值的工具。
