Rapid and Concise Quantification of Mycelial Growth by Microscopic Image Intensity Model and Application to Mass Cultivation of Fungi

Rapid and Concise Quantification of Mycelial Growth by Microscopic Image Intensity Model and Application to Mass Cultivation of Fungi | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 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Fungi Soo Kweon Lee, Ju Hun Lee, Hyeong Ryeol Kim, Youngsang Chun, Ja Hyun Lee, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-933868/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Dec, 2021 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version 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 ( R 2 = 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. Biotechnology and Bioengineering General Microbiology Biological Chemistry Microscopic Cordyceps dry cell weight (DCW) microscopic image intensity (MII) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Microorganisms have played an important role as producers in various bio-industries such as food, cosmetics, pharmaceuticals, biomaterials, and fuels 1,2,3,4 . In particular, in the bio-industry, microbial fermentation produces not only food, but also a variety of supplements such as antioxidants, flavors, colorants, preservatives, and sweeteners 5,6 . According to the BCC Market Research Report on Fermentation Industry, the global market for bioproducts (petroleum, natural gas, plastics/polymers, composites, pharmaceuticals, chemicals, and power) was estimated at $9.7 trillion in 2020. It will increase at a compounded annual growth rate (CAGR) of 4.8% to reach nearly $12.3 trillion by 2025. In particular, the global market for fermented products (excluding biofuels and biopolymers) is expected to grow at a CAGR of 17.7% over the next five years to reach $69 billion by 2025. The bio-industry growth is due to the rapid development of fundamental life sciences and advanced biotechnology, such as genetic engineering, process engineering, mass production, and purification 7 . Fermentation is a metabolic process that causes chemical changes in organic substrates through enzymatic actions of microorganisms 8 . During microbial fermentation in a bioreactor, environmental factors such as temperature, dissolved oxygen, pH, agitation rate, and monitoring of cell and nutrient concentrations are very important in mass production 9,10 . In particular, it is well known that the shape and concentration of cells during fermentation can affect the productivity of targeted metabolites 11, 12, 13, 14, 15 . Therefore, in fermentation, an understanding of the correlation between the growth of microorganisms and the production of metabolites is required, and various cell quantification techniques have been developed. In general, cell quantification is divided into direct and indirect measurements. Fig. 1 shows a schematic diagram of cell quantification, including representative examples of direct and indirect techniques. The most well-known direct methods are microscopic cell count, plate medium, and dry cell weight (DCW) measurements. Indirect methods include ATP bioluminescence measurements, turbidity measurements, and spectrophotometric measurements 16, 17 . Among direct methods, the DCW method is useful by measuring the weight of filamentous fungi that do not grow in a certain form 18 . However, before weighing the sample, it must be centrifuged and dried. Therefore, the analysis takes a long time. In addition, real-time monitoring is difficult. On the other hand, the indirect method has a relatively short analysis time, and real-time monitoring is relatively easy. Though, most of the applicable samples are limited to microbes with uniform shapes such as Escherichia coli , Bacillus , and yeast. It is difficult to apply an indirect method to filamentous fungi or mycelium that grow in the shape of a branch. In addition, if the sample contains non-cellular or colored substances, it may interfere with the measurement and decreases the accuracy of the result 16, 19 . Recently, technologies that overcome deficiencies of direct and indirect methods have been reported. However, most reports have applied fractals to analyze mycelial growth and develop models through correlation with metabolites produced 12, 13 . Those models could be used to understand the characteristics of cells growing in complex shapes. However, they are not suitable for quantitative analysis. Therefore, fast and accurate cell quantification techniques applied to the bio-industry for fermenting fungal mycelium are needed. In this study, a concise image analysis model was designed for quantifying fungal mycelium more quickly and accurately. A microscopic image intensity (MII) model was designed to analyze the correlation between the intensity value of hyphae morphological image and the weight of dry cells. It was based on the linear regression model targeting Cordyceps militaris , a filamentous fungus with a non-uniform cell shape. This strain is an improved strain for the production of cordycepin as a functional biomaterial in our previous study 20 . Its optimal production conditions have been determined. Finally, the developed MII model was evaluated by comparing predicted and experimental values of mycelial growth of C. militaris . Results Screening of mycelial growth. In our previous study, C. militaris was first employed to produce cordycepin, known as a bioactive substance. As the most effective producer, strain KYL05 was finally selected. Culture conditions and nutrient compositions were determined based on cordycepin production. A medium composition containing 2% glucose and 2% casein hydrolysate was found to be the most effective for its production 20 . In this process, numerous repeated experiments were performed to derive the optimum conditions. The concentration of the final product, cordycepin, was analyzed relatively faster using HPLC. However, growth measurement is a major delay factor in the analysis of fermentation profiling due to the long drying time for the preparation of dry cell weight. Therefore, a rapid quantification technique of cell density is needed for applications such as scale-up and process optimization. Reported methods are suitable for measuring the density of cells that appear round or oval in shapes, such as bacteria and yeast 21 , 22 , 23 . However, it is difficult to apply the DCW method to the mycelium of fungi that grow in complex shapes. To solve this problem, a new model was suggested and investigated using C. militaris KYL05. During the fermentation process, most cells existed in the form of spore. It was observed that the amount of mycelium rapidly increased at around 3 days. At this point, cells had grown in the form of spore and elongated hyphae. From the third day, more hyphae began to be observed than spores. On the 4 days, most of the mycelium grew into complex and elongated branches and spores. The shape of this mycelium was maintained up to 6 days. More mycelium in the form of a pellet rather than a spore began to be observed. Correlation between microscopic image intensity & DCW. Microscopic hyphal images collected over 6 days were transformed to determine their respective intensity values and used to investigate the relationship between mycelial mass and morphological changes. Fig. 2 shows the hyphae intensity of C. militaris KYL05 in a submerged culture for 6 days. During fermentation, DCW was increased, and the intensity of mycelium also increased. The mycelium of concentration was gradually increased between the 2 and 3 days. The intensity value also increased from 35.01 to 42.22. In addition, on the 4, 5, and 6 days, intensity values increased to 61.72, 63.76, and 67.99, respectively. So, the intensity value showed the same pattern as the DCW value, and it could be inferred that there is a correlation between them. DCW and microscopic image intensity values of samples were collected during fermentation. Based on their correlations, the MII model was established (Fig. 3 ). Table 1 MII model verification by SPSS program. Variable Unstandardized coefficients Standardized coefficients t (p) F (p) R 2 \({\beta }_{0}\) Standard error \({\beta }_{1}\) (Constant) 70.095 7.038 - 9.960 *** 1156.825 *** 0.941 MII 5.982 0.176 0.970 34.012 *** * p < 0.05, ** p < 0.01, *** p < 0.001 Verification of model. For accurate verification, collected data were analyzed in the SPSS program by simple regression analysis and summarized in Table 1 . Analyzed contents showed an equation of Y = 70.095 + 5.982X. According to ANOVA, F value of 1156.825 ( p < 0.001) was obtained, indicating that the MII model was a suitable model. Also, the coefficient of determination ( R 2 ) was 0.941, showing an explanatory power of 94.1%. This proved that 95% of the variance of actual DCW measurement (a dependent variable) could be explained by the intensity (an independent variable). According to the analysis, the significance probability ( p ) was less than 0.05, confirming that DCW could be measured by intensity. Degree of accuracy. The mycelium intensity and actual DCW values were randomly compared to confirm the model's applicability (Fig. 4 ). The image intensity value of sample (A) was measured to be 23.9 and the DCW was 270.1 mg/L. The actual DCW value was 240.5 mg/L, confirming an accuracy of 89%. The image intensity of sample (B) was 38.645 and the expected DCW was 383.9 mg/L. The actual DCW value was 409.9 mg/L, showing an accuracy of 93.7%. For sample (C), the intensity value was 50.845 and DCW was 438.4 mg/L, showing an accuracy of 91.7%. These results confirmed that the fungal mycelium could be quantified through the MII model. Effect of dilution factors. In addition, to investigate the effect of the dilution factor on the intensity value, various dilution factors (2, 5, 10, 10 2 , and 10 3 ) were applied to the C. militaris fermented samples (Fig. 5 ). As a result, it was confirmed that the R 2 values in (B), (C), and (D) decreased from 0.8973 to 0.8606 and 0.8023, respectively. Hence, that culture samples at dilutions of 10, 10 2 , and 10 3 were not suitable for analysis through the MII model. On the contrary, (A) showed that the R 2 value was measured as high as 0.9493 between dilution factors 2 to 5. Therefore, if a dilution factor of 2 to 5 is applied to the sample, it is expected that the accurate measurement of DCW through the MII model will be possible. Discussion The aim of this study was to design a new model for quantifying fungal mycelium. As a result, we have suggested a model that can measure mycelium immediately and accurately. Likewise, several similar models have been reported in various industries to determine mold shape and quantity 24 . Actually, productivity, which is considered the most important factor from an industrial perspective, is related to mycelium's shape and quantity 24 , 25 . Therefore, several types of methods have been reported for analyzing fungal mycelium. These image analysis methods were compared in detail with our model, and it was summarized in Table 2 . Table 2 Summary of various image analytical methods for fungal growth. No. Strain Culture type Image acquisition Image transformation & analical method Application Ref. 1 Penicillium decumbens JU-A10 Solid-state Digital camera Matlab, Fractal dimension On-line determination of fungal growth Duan et al. (2012) 2 Rhizopus oligosporus NRRL-2710 Solid-state Stereomicroscope Image J, Fractal dimension Characterization of fungal growth Díaz et al. (2010) 3 Aspergillus fumigatus PL-12/10 Submerged Microscopy Image J, Fractal dimension Measure the fungal growth Rajković et al. (2019) 4 Cephalosporium acremonium M25 Submerged Microscopy Image-Pro, Fractal dimension Predicted fungal growth & Cephalosporin C (CPC) productions Kim et al. (2005) 5 Aspergillus niger PM1 Submerged Microscopy Image J, Fractal dimension Quantify of fungal growth Papagianni (2006) 6 Aspergillus niger SKAn1015 Submerged Microscopy Image J, Fractal dimension Characterization of fungal Wucherpfennig et al. (2013) 7 Penicillium chrysogenum P-14 Submerged Flow-cytometry Fluorescence, Matlab Fast measurement of fungal growth Ehgartner et al. (2017) 8 Penicillium chrysogenum P-14 Submerged Flow-cytometry Fluorescence, Matlab Estimate of the relationship between fungal morphology, viability, and productivity Veiter et al. (2019) 9 Alcaligenes eutrophus NCIMB 11599 Submerged Fluorescence spectroscopy Matlab Predicted fungal growth Hagedorn et al. (2003) 10 Bacillus polymyxa POL4-2 Submerged Fluorescence spectroscopy Fluorophore On-line monitoring of fungal growth & antibiotic polymyxin B Lantz et al. (2006) 11 Claviceps purpurea 1029 NS Submerged Fluorescence spectroscopy Fluorophore Bioprocess monitoring of fungal growth Boehl et al. (2003) 12 Saccharomyces cerevisiae CEN.PK.113-7D Submerged Fluorescence spectroscopy Fluorophore Estimation of the fungal growth during cultivations Haack et al. (2004) 13 Cordyceps militaris KYL05 Submerged Microscopy Image J, IBM SPSS Immediately measurement of cell mass This study In general, fungal fermentation methods can be divided into two types: solid cultivation and submerged cultivation. It is well known that the quantification of fungal mycelium is difficult in a solid medium 26 , 27 . There are some methods of harvesting colonies and measuring the suspension by spectroscopy to solve this problem. However, their reproducibility and accuracy are low. Recently, several studies have been conducted to improve these problems. Duan et al. 28 have used Penicillium decumbens JU-A10 strain to construct a model to quantify the hyphae matrix's morphological changes in solid fermentation. The amount of fungal mycelium in the solid medium was predicted through the validation of the proposed model. The relative error was 0.54–5.22% for biomass and 0.45–3.89% for fractal dimension. Matlab and fractal dimensions were used for mycelium image transformation and analysis in that study 28 . Díaz et al. 29 have also reported the same type of culture condition for characterizing macro and micro structural development of Rhizopus oligosporus NRRL-2710 colonies growing on solid media in Petri dishes through image processing and fractal dimension. They found that growth of the colony front was useful for evaluating parameters of fungal development such as the number of tips and the average hypha length 29 . Other types of methods for microscopic observation of fungal mycelium in submerged cultivation have also been reported. Rajković et al. 30 have used fractal analysis of microscopic images (FAMI) to measure fractal dimensions (D). Obtained data of D were modeled for the prediction of the growth rate of Aspergillus fumigatus PL-12/10 30 . Kim et al. 12 and Lim et al. 13 have also investigated the relationship between the morphology and rheological properties of Cephalosporium acremonium M25 in a 2.5L bioreactor by fractal dimension based on Cephalosporin C (CPC), a secondary metabolite. Likewise, Aspergillus niger PM1 and SKAn1015 have been investigated using ImageJ and fractal dimensions to quantify and characterize mycelium growth 31 , 32 . These methods and models are useful for the prediction of mycelial form and mycelial development. However, they are not suitable for mycelial quantification. Filamentous fungi have a wide variety of morphological forms in submerged culture. These could appear as dispersed hyphae, interwoven mycelial aggregates, or denser hyphal aggregates, the so-called pellets 33 . In such cases, flow cytometry (FC) is a useful method for analyzing mycelial aggregates in the form of pellets. FC is a technique used to detect and measure physical and chemical properties of the population of cells or particles. Tens of thousands of cells can be tested quickly. Matlab could be used for data analysis. In fact, this method is suitable for analyzing mycelial aggregates in the form of pellets but not for other types of hyphae 33 , 34 . Similarly, fluorescence spectroscopy is a type of electromagnetic spectroscopy that can analyze the fluorescence of a sample. This is a method that employs the fluorescence of a sample by excitation of electrons of a specific compound molecule and emitting light 33 , 35 , 36 . According to Boehl et al. 37 , this method is useful for measuring the productivity of mycelium quantity and protein or alkaloid concentration 37 . However, this method could be disturbed by substances other than mycelium during sample analysis, resulting in low accuracy. In addition, it is difficult to measure mycelium that is not in a uniform shape. A method of measuring cell mass by calculating the fluorescence intensity value of Saccharomyces cerevisiae using multiple wavelengths has also been reported 38 . However, it was not suitable for mycelium quantification for the same reason. In this study, an MII model was developed based on the intensity of a microscopic image through simple linear regression analysis. Compared to previously reported methods, it could greatly save time for analyzing the amount of mycelium. The simple regression analysis applied to verify the model can be applied to various fields based on experience and intuition. It can grasp patterns and relationships and convert them into useful information without using experimental data 39 , 40 . Through this method, the amount of mycelium can be predicted with an accuracy of more than 94%. However, the model's accuracy has only been demonstrated for C. militaris KYL05 species. Further studies are needed using other species. Nevertheless, based on these results, we are confident that the MII model will enable hyphae monitoring when applying complex bioprocesses for fungal fermentation. It will provide basic information for controlling large-scale fermentation processes in the future. In conclusion, a rapid and concise quantification of the Cordyceps mycelium was required, and MII based analytical method was applied in this study. The prediction model was derived through the correlation between MII and DCW during fermentation of C. militaris KYL05. The MII model was validated by applying a simple linear regression analysis in the SPSS program, as a result, statistical significance ( R 2 = 0.941, p < 0.001) was confirmed. Therefore, by analyzing the image intensity of the mycelium collected through a microscope, it is possible to rapidly estimate the DCW. In addition, validation using randomly selected samples showed high accuracy, suggesting that the MII model enables rapid analysis of DCW during fungal fermentation in the bio-industry. Materials And Methods Microorganisms. In our previous work, C. militaris KCTC6064 was purchased from the Korea collection for type cultures (Jeongeup-si, Jeollabuk-do, Korea). The wild-type of C. militaris KCTC6064 was mutated by ultraviolet irradiation. The C. militaris KYL05 strain was then obtained 20 . This strain was used in the present study. Each month, organisms were transferred to potato dextrose agar slants to maintain storage culture. Culture conditions of C. militaris. The basal seed medium was potato dextrose broth (PDB; composition, 4 g/L potato starch, and 20 g/L glucose). The seed culture was performed in a 250 mL Erlenmeyer flask containing 50 ml of the basal seed medium. Culture was performed at 25°C with pH 6 for 3 days in a shaking incubator (200 rpm). The main medium was made with the following ingredients: 20 g/L casein hydrolysate, 20 g/L glucose, 0.1 g/L KH 2 PO4, 0.2 g/L K 2 HPO 4 ∙3H 2 O, and 0.2 g/L MgSO 4 ·7H 2 O 20, 41 . The inoculum (4%, v/v) of seed broth of C. militaris KYL05 was transferred into the main medium. The cultivation was performed in a 250 mL Erlenmeyer flask containing 50 ml of broth main medium at 25°C for 6 days in a shaking incubator (150 rpm) 11 , 20 , 42 . Measurement of dry cell weight. Cell growth was monitored every 24 h. After sampling, dry cell weight (DCW) was measured. After cultivation, the cultural broth was centrifuged at 8,000 × g for 30 min at 4°C. The sediment was then washed with distilled water. DCW was measured by samples weight through a pre-weighed filter paper (Whatman GF/C) and dried in a vacuum oven for 48 h at 60°C 11 , 20 . Image transformation from optical microscope image of C. militaris. Images of C. militaris KYL05 were captured at 24 h intervals during 6 days of cultivation using a microscope (Olympus BX51 model, Japan). The color (24 bits) images of the whole colonies obtained through the microscopy were converted to greyscale (8 bits) maps to black and white images using Image J program (version 1.46) ( https://imagej.nih.gov/ij/download.html ). It was automatically selected for the best range of given the image's intensity values based on the percentage of the total number of pixel values from the lowest to highest pixel value. At 8 bits, the gray level range was 0 to 255. The thresholding process was applied to each image by manually adjusting the level to 154 43 . In the 8-bit image of the border, the mycelia and media from the image of the growing front of the colony were virtually separated using the programs subtract background tool (digital filter). Contrast was then enhanced, followed by thresholding to 180 in the gray-scale and dilated using the dilate tool. The identification of pixels not belonging to the mycelium was done using media filters and tools to find the maximum. In order to remove noise from the original optical microscope image, a transformed image was obtained by removing pixels not belonging to the mycelium. Intensity from each transformed image was measured with the ImageJ program 19 , 43 . Simple linear regression model between the intensity of transformed image and DCW. The IBM Statistical Package for the Social Sciences (SPSS) Statistical 27.0.0 program ( https://www.ibm.com/kr-ko/analytics/spss-statistics-software ) was used to evaluate variables of image intensity. Among several statistical methods, a simple linear regression model that could analyze the relationship with the dependent variable by considering only one independent variable was used with the following equation ( 1 ): $$\text{y}={\beta }_{0}+{\beta }_{1}x+\text{ϵ}$$ 1 where y was the predicted value of the dependent variable (y) for any given value of the independent variable (x); \({\beta }_{0}\) was the intercept, the predicted value of y when the x was 0; \({\beta }_{1}\) was the regression coefficient (expect y to change as x increases); x was the independent variable (the variable expected to influence y); and \(\in\) value was the error of the estimate, or the variation in our estimate of the regression coefficient 44 , 45 . Through this, the mean, standard deviation, and residual variables were analyzed, along with the 95% confidence interval. In addition, the hypothesis was tested through the analysis of variance (ANOVA). The significance level was at p < 0.05. Declarations Data availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Authors’ contribution S.K.L. conducted experiments and analyzed the data with the help of J.H.L. (Ju Hun Lee), H.R.K. and Y.C., S.K.L. and H.Y.Y. wrote the draft of the manuscript. J.H.L. (Ja Hyun Lee), C.P., and H.Y.Y. funding acquisition and project administration. S.W.K. conceptualization, supervision, validation, and writing—review & editing. Funding This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (NRF-2019R1A2C1006793 and NRF-2020R1C1C1005060). Competing interests The authors declare no competing interests. References An, H. E., Lee, K. H., Jang, Y. W., Kim, C. B. & Yoo, H. Y. Improved glucose recovery from Sicyos angulatus by NaOH pretreatment and application to bioethanol production. Processes , 9 (2), 245 https://doi.org/10.3390/pr9020245 (2021). Lee, J. H. et al. Continuous production of bioethanol using microalgal sugars extracted from Nannochloropsis gaditana. Korean J. Chem. Eng , 36 (1), 71–76 https://doi.org/10.1007/s11814-018-0173-y (2019). Lee, K. H. et al. Improvement of enzymatic glucose conversion from chestnut shells through optimization of KOH pretreatment. Int. J. Environ. Res. Public Health , 18 (7), 3772 https://doi.org/10.3390/ijerph18073772 (2021). Ragauskas, A. J. et al. The path forward for biofuels and biomaterials., 311 , 484–489 https://doi.org/10.1126/science.1114736 (2006). Couto, S. R. & Sanromána, M. A. Application of solid-state fermentation to food industry—A review. J. Food Eng , 76 (3), 291–302 https://doi.org/10.1016/j.jfoodeng.2005.05.022 (2006). Mussagy, C. U., Winterburn, J. & Santos-Ebinuma, V. C. & Pereira JFB Production and extraction of carotenoids produced by microorganisms. Appl. Microbiol. Biotechnol , 103 , 1095–1114 https://doi.org/10.1007/s00253-018-9557-5 (2019). BCC Research. Biorefinery Products; Global Markets. BCC Research. EGY117C. https://doi.org/10.1080/17457300.2018.1426702 (2021). Machida, M. Progress of Aspergillus oryzae Genomics. Adv. Appl. Microbiol , 51 , 81–106 https://doi.org/10.1016/s0065-2164(02)51002-9 (2002). Jung, D. U. et al. Optimization of medium composition for enhanced cellulose production by mutant Penicillium brasilianum KUEB15 using statistical method. J. Ind. Eng. Chem , 25 , 145–150 https://doi.org/10.1016/j.jiec.2014.10.026 (2015). Yang, X. et al. Improved production of 1,3-propanediol from biodiesel-derived crude glycerol by Klebsiella pneumoniae in fed-batch fermentation. Chem. Eng. J , 349 , 25–36 https://doi.org/10.1016/j.cej.2018.05.042 (2018). Cha, S. H. et al. Morphological characteristics of Cordyceps sinensis 16 and production of mycelia and exo-biopolymer from molasses in submerged culture. J. Ind. Eng. Chem , 12 , 115–120 (2006). Kim, J. C., Lim, J. S., Kim, J. M., Kim, C. & Kim, S. W. Relationship between morphology and viscosity of the main culture broth of Cephalosporium acremonium M25. Korea-Aust. Rheol. J , 17 (1), 15–20 (2005). Lim, J. S. et al. Relationship between fractal dimension and morphological features of Cephalosporium acremonium M25 in a 30-l bioreactor culture. J. Microbiol. Biotechnol , 15 (5), 971–976 (2005). Papagianni, M. Fungal morphology and metabolite production in submerged mycelial processes. Biotechnol. Adv , 22 , 189–259 https://doi.org/10.1016/j.biotechadv.2003.09.005 (2004). Veiter, L., Rajamanickam, V. & Herwig, C. The filamentous fungal pellet—relationship between morphology and productivity. Appl. Microbiol. Biotechnol , 102 , 2997–3006 https://doi.org/10.1007/s00253-018-8818-7 (2018). Doran, P. M. Bioprocess engineering principles. Elsevier. Netherlands(1995). O'Toole, D. K. Methods for the direct and indirect assessment of the bacterial content of milk. J. Appl. Bacteriol , 55 , 187–201 https://doi.org/10.1111/j.1365-2672.1983.tb01315.x (1983). Manan, M. A. & Webb, C. Estimating fungal growth in submerged fermentation in the presence of solid particles based on colour development. Biotechnol. Biotechnol. Equip , 32 (3), 618–627 https://doi.org/10.1080/13102818.2018.1440974 (2018). Paul, G. C. & Thomas, C. R. Characterisation of mycelial morphology using image analysis. In: Schügerl K. (eds) Relation Between Morphology and Process Performances. Advances in Biochemical Engineering/Biotechnology, vol 60.Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0102278 (1998). Lee, S. K. et al. Improved cordycepin production by Cordyceps militaris KYL05 using casein hydrolysate in submerged conditions. Biomolecules , 9 , 461 https://doi.org/10.3390/biom9090461 (2019). Ou, F., McGoverin, C., Swift, S. & Vanholsbeeck, F. Absolute bacterial cell enumeration using flow cytometry. J. Appl. Microbiol , 123 (2), 464–477 https://doi.org/10.1111/jam.13508 (2017). Zalewski, K. & Buchholz, R. Morphological analysis of yeast cells using an automated image processing system. J. Biotechnol , 48 , 43–49 https://doi.org/10.1016/0168-1656(96)01503-9 (1996). Wang, R. et al. Analysis of methods for quantifying yeast cell concentration in complex lignocellulosic fermentation processes. Sci. Rep , 11 , 11293 https://doi.org/10.1038/s41598-021-90703-8 (2021). Posch, A. E., Herwig, C. & Spadiut, O. Science-based bioprocess design for filamentous fungi. Trends Biotechnol , 31 (1), 37–44 https://doi.org/10.1016/j.tibtech.2012.10.008 (2013). Harms, P., Kostov, Y. & Rao, G. Bioprocess monitoring. Curr. Opin. Biotechnol , 13 (2), 124–127 https://doi.org/10.1016/S0958-1669(02)00295-1 (2002). Abdul Manan, M. & Webb, C. Estimating fungal growth in submerged fermentation in the presence of solid particles based on colour development. Biotechnol. Biotechnol. Equip , 32 (3), 618–627 https://doi.org/10.1080/13102818.2018.1440974 (2018). Borzani, W. A weighing method to identify the microbial growth phases in solid-state fermentation tests. World J. Microbiol. Biotechnol , 16 (7), 601–605 https://doi.org/10.1023/A:1008970720794 (2000). Duan, Y., Wang, L. & Chen, H. Digital image analysis and fractal-based kinetic modelling for fungal biomass determination in solid-state fermentation. Biochem. Eng. J , 67 , 60–67 https://doi.org/10.1016/j.bej.2012.04.020 (2012). Díaz, B. H. C. et. al. Morphological characterization of the growing front of Rhizopus oligosporus in solid media. J. Food Eng , 101 (3), 309–317 https://doi.org/10.1016/j.jfoodeng.2010.06.028 (2010). Rajković, K. M., Milošević, N. T., Otašević, S., Jeremić, S. & Arsenijević, V. A. Aspergillus fumigatus branching complexity in vitro: 2D images and dynamic modeling. Comput. Biol. Med , 104 , 215–219 https://doi.org/10.1016/j.compbiomed.2018.11.022 (2019). Papagianni, M. Quantification of the fractal nature of mycelial aggregation in Aspergillus niger submerged cultures. Microb. Cell Fact , 5 (1), 1–13 https://doi.org/10.1186/1475-2859-5-5 (2006). Wucherpfennig, T., Lakowitz, A. & Krull, R. Comprehension of viscous morphology—evaluation of fractal and conventional parameters for rheological characterization of Aspergillus niger culture broth. J. Biotechnol , 163 (2), 124–132 https://doi.org/10.1016/j.jbiotec.2012.08.027 (2013). Ehgartner, D., Herwig, C. & Fricke, J. Morphological analysis of the filamentous fungus Penicillium chrysogenum using flow cytometry—the fast alternative to microscopic image analysis. Appl. Microbiol. Biotechnol , 101 (20), 7675–7688 https://doi.org/10.1007/s00253-017-8475-2 (2017). Veiter, L. & Herwig, C. The filamentous fungus Penicillium chrysogenum analysed via flow cytometry—a fast and statistically sound insight into morphology and viability. Appl. Microbiol. Biotechnol , 103 (16), 6725–6735 https://doi.org/10.1007/s00253-019-09943-4 (2019). Hagedorn, A., Legge, R. L. & Budman, H. Evaluation of spectrofluorometry as a tool for estimation in fed-batch fermentations. Biotechnol. Bioeng , 83 (1), 104–111 https://doi.org/10.1002/bit.10649 (2003). Lantz, A. E., Jørgensen, P., Poulsen, E., Lindemann, C. & Olsson, L. Determination of cell mass and polymyxin using multi-wavelength fluorescence. J. Biotechnol , 121 (4), 544–554 https://doi.org/10.1016/j.jbiotec.2005.08.007 (2006). Boehl, D., Solle, D., Hitzmann, B. & Scheper, T. Chemometric modelling with two-dimensional fluorescence data for Claviceps purpurea bioprocess characterization. J. Biotechnol , 105 (1-2), 179–188 https://doi.org/10.1016/S0168-1656(03)00189-5 (2003). Haack, M. B., Eliasson, A. & Olsson, L. On-line cell mass monitoring of Saccharomyces cerevisiae cultivations by multi-wavelength fluorescence. J. Biotechnol , 114 (1-2), 199–208 https://doi.org/10.1016/j.jbiotec.2004.05.009 (2004). Brook, R. J. & Arnold, G. C. Applied regression analysis and experimental design (CRC Press, 2018). https://doi.org/10.1201/9781315137674 Leatherbarrow, R. J. Using linear and non-linear regression to fit biochemical data. Trends Biochem. Sci , 15 (12), 455–458 https://doi.org/10.1016/0968-0004(90)90295-M (1990). Lee, J. H. Significant impact of casein hydrolysate to overcome the low consumption of glycerol by Klebsiella aerogenes ATCC 29007 and its application to bioethanol production. Energy Conv. Manag , 221 , 113181 https://doi.org/10.1016/j.enconman.2020.113181 (2020). Lee, K. H. Statistical optimization of alkali pretreatment to improve sugars recovery from spent coffee grounds and utilization in lactic acid fermentation. Processes , 9 (3), 494 https://doi.org/10.3390/pr9030494 (2021). Cox, P. W., Paul, G. C. & Thomas, C. R. Image analysis of the morphology of filamentous micro-organisms., 144 , 817–827 https://doi.org/10.1099/00221287-144-4-817 (1998). Bangdiwala, S. I. Regression: simple linear. Int. J. Inj. Control Saf , 25 (1), 113–115 https://doi.org/10.1080/17457300.2018.1426702 (2018). George, D. & Mallery, P. IBM SPSS statistics 26 step by step: A simple guide and reference. Routledge. https://doi.org/10.4324/9780429056765 (2019). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Dec, 2021 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 09 Nov, 2021 Reviews received at journal 07 Nov, 2021 Reviewers agreed at journal 27 Oct, 2021 Reviews received at journal 20 Oct, 2021 Reviewers agreed at journal 20 Oct, 2021 Reviewers invited by journal 10 Oct, 2021 Editor assigned by journal 10 Oct, 2021 Editor invited by journal 29 Sep, 2021 Submission checks completed at journal 29 Sep, 2021 First submitted to journal 23 Sep, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-933868","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":54887685,"identity":"6b6a2d96-0102-4f19-86df-600d282501a2","order_by":0,"name":"Soo Kweon Lee","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Soo","middleName":"Kweon","lastName":"Lee","suffix":""},{"id":54887686,"identity":"af6b3585-5661-40ca-a47f-ab7c89346414","order_by":1,"name":"Ju Hun Lee","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Ju","middleName":"Hun","lastName":"Lee","suffix":""},{"id":54887687,"identity":"623694b2-6060-462c-aa43-c31a60800d4b","order_by":2,"name":"Hyeong Ryeol Kim","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Hyeong","middleName":"Ryeol","lastName":"Kim","suffix":""},{"id":54887688,"identity":"13f971b4-bb06-4d80-867d-1bf86b2998ce","order_by":3,"name":"Youngsang Chun","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Youngsang","middleName":"","lastName":"Chun","suffix":""},{"id":54887689,"identity":"e11eb3b8-8535-446d-89f6-c30d61336e2a","order_by":4,"name":"Ja Hyun Lee","email":"","orcid":"","institution":"Dongyang Mirae University","correspondingAuthor":false,"prefix":"","firstName":"Ja","middleName":"Hyun","lastName":"Lee","suffix":""},{"id":54887690,"identity":"5982f5e8-0132-4db0-ae61-382df130bf8c","order_by":5,"name":"Chulhwan Park","email":"","orcid":"","institution":"Kwangwoon University","correspondingAuthor":false,"prefix":"","firstName":"Chulhwan","middleName":"","lastName":"Park","suffix":""},{"id":54887691,"identity":"b487d8e2-a20c-48ce-be1b-aad52f8dcef7","order_by":6,"name":"Hah Young Yoo","email":"","orcid":"","institution":"Sangmyung University","correspondingAuthor":false,"prefix":"","firstName":"Hah","middleName":"Young","lastName":"Yoo","suffix":""},{"id":54887692,"identity":"726213e2-7886-400b-9275-fc896f1a22ff","order_by":7,"name":"Seung Wook Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACCWYQWZEAFzAgUssZkrSACMY2UrRItnMnf+adlyZn3t78gOFHDYOxeQMBLdLMvNukebflGMucOWbA2HOMwUzmAAEtckAtzLnbKhJnSCQYMPA2MNhIEHIYUMvmz7lzgFrkn39g/EuMFqDDNkjnNuQAbeExYAbaYkZQi2Qz0C9/jqUZS/DkFByWOSZhTFCLxPmzmz/OqEmWk2A/vvHhmxobwxmEtKCAA9B4GgWjYBSMglFAKQAAOkk1NcNFjp8AAAAASUVORK5CYII=","orcid":"","institution":"Korea University","correspondingAuthor":true,"prefix":"","firstName":"Seung","middleName":"Wook","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2021-09-24 03:29:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-933868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-933868/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-021-03512-4","type":"published","date":"2021-12-01T13:02:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":14160140,"identity":"567c1492-854c-4b17-b108-f021ef4ee3b0","added_by":"auto","created_at":"2021-09-30 17:03:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43304,"visible":true,"origin":"","legend":"Schematic diagram showing direct and indirect cell quantification methods.","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-933868/v1/633b4e2d3c923ecd2543de23.png"},{"id":14160387,"identity":"3562db76-f3a1-461e-b980-b88d2b755fae","added_by":"auto","created_at":"2021-09-30 17:06:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":912449,"visible":true,"origin":"","legend":"Original microscopic image of mycelial growth according to culture time of C. militaris KYL05 and transformed image for measuring intensity with Image J.","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-933868/v1/7b65965565b8a5f15584c617.png"},{"id":14160143,"identity":"241ed4c1-9e30-420b-b3cf-c1c4f560fdd0","added_by":"auto","created_at":"2021-09-30 17:03:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92947,"visible":true,"origin":"","legend":"Correlation between microscopic image intensity (MII) of transformed images and dry cell weight (DCW) from quantification of mycelial growth.","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-933868/v1/be69094a0c1042d802029c37.png"},{"id":14160141,"identity":"c1a37a7b-cdf5-45f1-80de-14ae7f03df84","added_by":"auto","created_at":"2021-09-30 17:03:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":608630,"visible":true,"origin":"","legend":"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).","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-933868/v1/d95f45c8d00a8d630e98003a.png"},{"id":14160144,"identity":"7bab0626-260c-48c6-be56-0e191cac48c7","added_by":"auto","created_at":"2021-09-30 17:03:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":65421,"visible":true,"origin":"","legend":"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).","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-933868/v1/9364ed518a4ad56dbcc25353.png"},{"id":16547815,"identity":"93158b65-ec6e-45c4-99a2-509c1eeb7f79","added_by":"auto","created_at":"2021-12-17 13:02:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1793808,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-933868/v1/d77f3e4b-0ab5-4135-99e0-5500c416fd5e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rapid and Concise Quantification of Mycelial Growth by Microscopic Image Intensity Model and Application to Mass Cultivation of Fungi","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicroorganisms have played an important role as producers in various bio-industries such as food, cosmetics, pharmaceuticals, biomaterials, and fuels\u003csup\u003e1,2,3,4\u003c/sup\u003e. In particular, in the bio-industry, microbial fermentation produces not only food, but also a variety of supplements such as antioxidants, flavors, colorants, preservatives, and sweeteners\u003csup\u003e5,6\u003c/sup\u003e. According to the BCC Market Research Report on Fermentation Industry, the global market for bioproducts (petroleum, natural gas, plastics/polymers, composites, pharmaceuticals, chemicals, and power) was estimated at $9.7 trillion in 2020. It will increase at a compounded annual growth rate (CAGR) of 4.8% to reach nearly $12.3 trillion by 2025. In particular, the global market for fermented products (excluding biofuels and biopolymers) is expected to grow at a CAGR of 17.7% over the next five years to reach $69 billion by 2025. The bio-industry growth is due to the rapid development of fundamental life sciences and advanced biotechnology, such as genetic engineering, process engineering, mass production, and purification\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFermentation is a metabolic process that causes chemical changes in organic substrates through enzymatic actions of microorganisms\u003csup\u003e8\u003c/sup\u003e. During microbial fermentation in a bioreactor, environmental factors such as temperature, dissolved oxygen, pH, agitation rate, and monitoring of cell and nutrient concentrations are very important in mass production\u003csup\u003e9,10\u003c/sup\u003e. In particular, it is well known that the shape and concentration of cells during fermentation can affect the productivity of targeted metabolites\u003csup\u003e11, 12, 13, 14, 15\u003c/sup\u003e. Therefore, in fermentation, an understanding of the correlation between the growth of microorganisms and the production of metabolites is required, and various cell quantification techniques have been developed.\u003c/p\u003e\n\u003cp\u003eIn general, cell quantification is divided into direct and indirect measurements. Fig.\u0026nbsp;1 shows a schematic diagram of cell quantification, including representative examples of direct and indirect techniques. The most well-known direct methods are microscopic cell count, plate medium, and dry cell weight (DCW) measurements. Indirect methods include ATP bioluminescence measurements, turbidity measurements, and spectrophotometric measurements\u003csup\u003e16, 17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAmong direct methods, the DCW method is useful by measuring the weight of filamentous fungi that do not grow in a certain form\u003csup\u003e18\u003c/sup\u003e. However, before weighing the sample, it must be centrifuged and dried. Therefore, the analysis takes a long time. In addition, real-time monitoring is difficult. On the other hand, the indirect method has a relatively short analysis time, and real-time monitoring is relatively easy. Though, most of the applicable samples are limited to microbes with uniform shapes such as \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, and yeast. It is difficult to apply an indirect method to filamentous fungi or mycelium that grow in the shape of a branch. In addition, if the sample contains non-cellular or colored substances, it may interfere with the measurement and decreases the accuracy of the result\u003csup\u003e16, 19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRecently, technologies that overcome deficiencies of direct and indirect methods have been reported. However, most reports have applied fractals to analyze mycelial growth and develop models through correlation with metabolites produced\u003csup\u003e12, 13\u003c/sup\u003e. Those models could be used to understand the characteristics of cells growing in complex shapes. However, they are not suitable for quantitative analysis. Therefore, fast and accurate cell quantification techniques applied to the bio-industry for fermenting fungal mycelium are needed.\u003c/p\u003e\n\u003cp\u003eIn this study, a concise image analysis model was designed for quantifying fungal mycelium more quickly and accurately. A microscopic image intensity (MII) model was designed to analyze the correlation between the intensity value of hyphae morphological image and the weight of dry cells. It was based on the linear regression model targeting \u003cem\u003eCordyceps militaris\u003c/em\u003e, a filamentous fungus with a non-uniform cell shape. This strain is an improved strain for the production of cordycepin as a functional biomaterial in our previous study\u003csup\u003e20\u003c/sup\u003e. Its optimal production conditions have been determined. Finally, the developed MII model was evaluated by comparing predicted and experimental values of mycelial growth of \u003cem\u003eC. militaris\u003c/em\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eScreening of mycelial growth.\u003c/strong\u003e In our previous study, \u003cem\u003eC. militaris\u003c/em\u003e was first employed to produce cordycepin, known as a bioactive substance. As the most effective producer, strain KYL05 was finally selected. Culture conditions and nutrient compositions were determined based on cordycepin production. A medium composition containing 2% glucose and 2% casein hydrolysate was found to be the most effective for its production\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In this process, numerous repeated experiments were performed to derive the optimum conditions. The concentration of the final product, cordycepin, was analyzed relatively faster using HPLC. However, growth measurement is a major delay factor in the analysis of fermentation profiling due to the long drying time for the preparation of dry cell weight. Therefore, a rapid quantification technique of cell density is needed for applications such as scale-up and process optimization.\u003c/p\u003e\n\u003cp\u003eReported methods are suitable for measuring the density of cells that appear round or oval in shapes, such as bacteria and yeast\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, it is difficult to apply the DCW method to the mycelium of fungi that grow in complex shapes. To solve this problem, a new model was suggested and investigated using \u003cem\u003eC. militaris\u003c/em\u003e KYL05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the fermentation process, most cells existed in the form of spore. It was observed that the amount of mycelium rapidly increased at around 3 days. At this point, cells had grown in the form of spore and elongated hyphae. From the third day, more hyphae began to be observed than spores. On the 4 days, most of the mycelium grew into complex and elongated branches and spores. The shape of this mycelium was maintained up to 6 days. More mycelium in the form of a pellet rather than a spore began to be observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between microscopic image intensity \u0026amp; DCW.\u003c/strong\u003e Microscopic hyphal images collected over 6 days were transformed to determine their respective intensity values and used to investigate the relationship between mycelial mass and morphological changes. Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the hyphae intensity of \u003cem\u003eC. militaris\u003c/em\u003e KYL05 in a submerged culture for 6 days. During fermentation, DCW was increased, and the intensity of mycelium also increased. The mycelium of concentration was gradually increased between the 2 and 3 days. The intensity value also increased from 35.01 to 42.22. In addition, on the 4, 5, and 6 days, intensity values increased to 61.72, 63.76, and 67.99, respectively. So, the intensity value showed the same pattern as the DCW value, and it could be inferred that there is a correlation between them. DCW and microscopic image intensity values of samples were collected during fermentation. Based on their correlations, the MII model was established (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMII model verification by SPSS program.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUnstandardized coefficients\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStandardized coefficients\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003et (p)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eF (p)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStandard error\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Constant)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.960\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1156.825\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.941\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.982\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.970\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.012\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eVerification of model.\u003c/strong\u003e For accurate verification, collected data were analyzed in the SPSS program by simple regression analysis and summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Analyzed contents showed an equation of Y = 70.095 + 5.982X. According to ANOVA, F value of 1156.825 (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) was obtained, indicating that the MII model was a suitable model. Also, the coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) was 0.941, showing an explanatory power of 94.1%. This proved that 95% of the variance of actual DCW measurement (a dependent variable) could be explained by the intensity (an independent variable). According to the analysis, the significance probability (\u003cem\u003ep\u003c/em\u003e) was less than 0.05, confirming that DCW could be measured by intensity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDegree of accuracy.\u003c/strong\u003e The mycelium intensity and actual DCW values were randomly compared to confirm the model's applicability (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The image intensity value of sample (A) was measured to be 23.9 and the DCW was 270.1 mg/L. The actual DCW value was 240.5 mg/L, confirming an accuracy of 89%. The image intensity of sample (B) was 38.645 and the expected DCW was 383.9 mg/L. The actual DCW value was 409.9 mg/L, showing an accuracy of 93.7%. For sample (C), the intensity value was 50.845 and DCW was 438.4 mg/L, showing an accuracy of 91.7%. These results confirmed that the fungal mycelium could be quantified through the MII model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of dilution factors.\u003c/strong\u003e In addition, to investigate the effect of the dilution factor on the intensity value, various dilution factors (2, 5, 10, 10\u003csup\u003e2\u003c/sup\u003e, and 10\u003csup\u003e3\u003c/sup\u003e) were applied to the \u003cem\u003eC. militaris\u003c/em\u003e fermented samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). As a result, it was confirmed that the \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e values in (B), (C), and (D) decreased from 0.8973 to 0.8606 and 0.8023, respectively. Hence, that culture samples at dilutions of 10, 10\u003csup\u003e2\u003c/sup\u003e, and 10\u003csup\u003e3\u003c/sup\u003e were not suitable for analysis through the MII model. On the contrary, (A) showed that the \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e value was measured as high as 0.9493 between dilution factors 2 to 5. Therefore, if a dilution factor of 2 to 5 is applied to the sample, it is expected that the accurate measurement of DCW through the MII model will be possible.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to design a new model for quantifying fungal mycelium. As a result, we have suggested a model that can measure mycelium immediately and accurately. Likewise, several similar models have been reported in various industries to determine mold shape and quantity \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Actually, productivity, which is considered the most important factor from an industrial perspective, is related to mycelium's shape and quantity\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Therefore, several types of methods have been reported for analyzing fungal mycelium. These image analysis methods were compared in detail with our model, and it was summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSummary of various image analytical methods for fungal growth.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStrain\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCulture type\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eImage acquisition\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eImage transformation \u0026amp; analical method\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eApplication\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePenicillium decumbens\u003c/em\u003e JU-A10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolid-state\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDigital camera\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMatlab,\u003c/p\u003e\n\u003cp\u003eFractal dimension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOn-line determination of fungal growth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuan et al.\u003c/p\u003e\n\u003cp\u003e(2012)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eRhizopus oligosporus\u003c/em\u003e NRRL-2710\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolid-state\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStereomicroscope\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImage J,\u003c/p\u003e\n\u003cp\u003eFractal dimension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCharacterization of fungal growth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u0026iacute;az et al.\u003c/p\u003e\n\u003cp\u003e(2010)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAspergillus fumigatus\u003c/em\u003e PL-12/10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubmerged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMicroscopy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImage J,\u003c/p\u003e\n\u003cp\u003eFractal dimension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMeasure the fungal growth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRajković et al.\u003c/p\u003e\n\u003cp\u003e(2019)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eCephalosporium acremonium\u003c/em\u003e M25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubmerged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMicroscopy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImage-Pro,\u003c/p\u003e\n\u003cp\u003eFractal dimension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePredicted fungal growth \u0026amp; Cephalosporin C (CPC) productions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKim et al.\u003c/p\u003e\n\u003cp\u003e(2005)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAspergillus niger\u003c/em\u003e PM1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubmerged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMicroscopy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImage J,\u003c/p\u003e\n\u003cp\u003eFractal dimension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuantify of fungal growth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePapagianni (2006)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAspergillus niger\u003c/em\u003e SKAn1015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubmerged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMicroscopy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImage J,\u003c/p\u003e\n\u003cp\u003eFractal dimension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCharacterization of fungal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWucherpfennig et al. (2013)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePenicillium chrysogenum\u003c/em\u003e P-14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubmerged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFlow-cytometry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFluorescence,\u003c/p\u003e\n\u003cp\u003eMatlab\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFast measurement of fungal growth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEhgartner et al. (2017)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePenicillium chrysogenum\u003c/em\u003e P-14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubmerged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFlow-cytometry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFluorescence,\u003c/p\u003e\n\u003cp\u003eMatlab\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimate of the relationship between fungal morphology, viability, and productivity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVeiter et al. (2019)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAlcaligenes eutrophus\u003c/em\u003e NCIMB 11599\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubmerged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFluorescence spectroscopy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMatlab\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePredicted fungal growth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHagedorn et al. (2003)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eBacillus polymyxa\u003c/em\u003e POL4-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubmerged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFluorescence spectroscopy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFluorophore\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOn-line monitoring of fungal growth \u0026amp; antibiotic polymyxin B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLantz et al. (2006)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eClaviceps purpurea\u003c/em\u003e 1029 NS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubmerged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFluorescence spectroscopy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFluorophore\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBioprocess monitoring of fungal growth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBoehl et al. (2003)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e CEN.PK.113-7D\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubmerged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFluorescence spectroscopy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFluorophore\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimation of the fungal growth during cultivations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHaack et al. (2004)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eCordyceps militaris\u003c/em\u003e KYL05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubmerged\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMicroscopy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImage J,\u003c/p\u003e\n\u003cp\u003eIBM SPSS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eImmediately measurement of cell mass\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThis study\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn general, fungal fermentation methods can be divided into two types: solid cultivation and submerged cultivation. It is well known that the quantification of fungal mycelium is difficult in a solid medium\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. There are some methods of harvesting colonies and measuring the suspension by spectroscopy to solve this problem. However, their reproducibility and accuracy are low. Recently, several studies have been conducted to improve these problems. Duan et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e have used \u003cem\u003ePenicillium decumbens\u003c/em\u003e JU-A10 strain to construct a model to quantify the hyphae matrix's morphological changes in solid fermentation. The amount of fungal mycelium in the solid medium was predicted through the validation of the proposed model. The relative error was 0.54\u0026ndash;5.22% for biomass and 0.45\u0026ndash;3.89% for fractal dimension. Matlab and fractal dimensions were used for mycelium image transformation and analysis in that study\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. D\u0026iacute;az et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e have also reported the same type of culture condition for characterizing macro and micro structural development of \u003cem\u003eRhizopus oligosporus\u003c/em\u003e NRRL-2710 colonies growing on solid media in Petri dishes through image processing and fractal dimension. They found that growth of the colony front was useful for evaluating parameters of fungal development such as the number of tips and the average hypha length\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOther types of methods for microscopic observation of fungal mycelium in submerged cultivation have also been reported. Rajković et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e have used fractal analysis of microscopic images (FAMI) to measure fractal dimensions (D). Obtained data of D were modeled for the prediction of the growth rate of \u003cem\u003eAspergillus fumigatus\u003c/em\u003e PL-12/10\u003csup\u003e30\u003c/sup\u003e. Kim et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and Lim et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e have also investigated the relationship between the morphology and rheological properties of \u003cem\u003eCephalosporium acremonium\u003c/em\u003e M25 in a 2.5L bioreactor by fractal dimension based on Cephalosporin C (CPC), a secondary metabolite. Likewise, \u003cem\u003eAspergillus niger\u003c/em\u003e PM1 and SKAn1015 have been investigated using ImageJ and fractal dimensions to quantify and characterize mycelium growth\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. These methods and models are useful for the prediction of mycelial form and mycelial development. However, they are not suitable for mycelial quantification.\u003c/p\u003e\n\u003cp\u003eFilamentous fungi have a wide variety of morphological forms in submerged culture. These could appear as dispersed hyphae, interwoven mycelial aggregates, or denser hyphal aggregates, the so-called pellets\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In such cases, flow cytometry (FC) is a useful method for analyzing mycelial aggregates in the form of pellets. FC is a technique used to detect and measure physical and chemical properties of the population of cells or particles. Tens of thousands of cells can be tested quickly. Matlab could be used for data analysis. In fact, this method is suitable for analyzing mycelial aggregates in the form of pellets but not for other types of hyphae\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSimilarly, fluorescence spectroscopy is a type of electromagnetic spectroscopy that can analyze the fluorescence of a sample. This is a method that employs the fluorescence of a sample by excitation of electrons of a specific compound molecule and emitting light\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. According to Boehl et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, this method is useful for measuring the productivity of mycelium quantity and protein or alkaloid concentration\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. However, this method could be disturbed by substances other than mycelium during sample analysis, resulting in low accuracy. In addition, it is difficult to measure mycelium that is not in a uniform shape. A method of measuring cell mass by calculating the fluorescence intensity value of \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e using multiple wavelengths has also been reported\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, it was not suitable for mycelium quantification for the same reason.\u003c/p\u003e\n\u003cp\u003eIn this study, an MII model was developed based on the intensity of a microscopic image through simple linear regression analysis. Compared to previously reported methods, it could greatly save time for analyzing the amount of mycelium. The simple regression analysis applied to verify the model can be applied to various fields based on experience and intuition. It can grasp patterns and relationships and convert them into useful information without using experimental data\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Through this method, the amount of mycelium can be predicted with an accuracy of more than 94%. However, the model's accuracy has only been demonstrated for \u003cem\u003eC. militaris\u003c/em\u003e KYL05 species. Further studies are needed using other species. Nevertheless, based on these results, we are confident that the MII model will enable hyphae monitoring when applying complex bioprocesses for fungal fermentation. It will provide basic information for controlling large-scale fermentation processes in the future.\u003c/p\u003e\n\u003cp\u003eIn conclusion, a rapid and concise quantification of the \u003cem\u003eCordyceps\u003c/em\u003e mycelium was required, and MII based analytical method was applied in this study. The prediction model was derived through the correlation between MII and DCW during fermentation of \u003cem\u003eC. militaris\u003c/em\u003e KYL05. The MII model was validated by applying a simple linear regression analysis in the SPSS program, as a result, statistical significance (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.941, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) was confirmed. Therefore, by analyzing the image intensity of the mycelium collected through a microscope, it is possible to rapidly estimate the DCW. In addition, validation using randomly selected samples showed high accuracy, suggesting that the MII model enables rapid analysis of DCW during fungal fermentation in the bio-industry.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eMicroorganisms.\u003c/strong\u003e In our previous work, \u003cem\u003eC. militaris\u003c/em\u003e KCTC6064 was purchased from the Korea collection for type cultures (Jeongeup-si, Jeollabuk-do, Korea). The wild-type of \u003cem\u003eC. militaris\u003c/em\u003e KCTC6064 was mutated by ultraviolet irradiation. The \u003cem\u003eC. militaris\u003c/em\u003e KYL05 strain was then obtained\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This strain was used in the present study. Each month, organisms were transferred to potato dextrose agar slants to maintain storage culture.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCulture conditions of\u003c/strong\u003e \u003cspan class=\"BoldItalic\"\u003eC. militaris.\u003c/span\u003e The basal seed medium was potato dextrose broth (PDB; composition, 4 g/L potato starch, and 20 g/L glucose). The seed culture was performed in a 250 mL Erlenmeyer flask containing 50 ml of the basal seed medium. Culture was performed at 25\u0026deg;C with pH 6 for 3 days in a shaking incubator (200 rpm). The main medium was made with the following ingredients: 20 g/L casein hydrolysate, 20 g/L glucose, 0.1 g/L KH\u003csub\u003e2\u003c/sub\u003ePO4, 0.2 g/L K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e∙3H\u003csub\u003e2\u003c/sub\u003eO, and 0.2 g/L MgSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO\u003csup\u003e20,\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The inoculum (4%, v/v) of seed broth of \u003cem\u003eC. militaris\u003c/em\u003e KYL05 was transferred into the main medium. The cultivation was performed in a 250 mL Erlenmeyer flask containing 50 ml of broth main medium at 25\u0026deg;C for 6 days in a shaking incubator (150 rpm)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of dry cell weight.\u003c/strong\u003e Cell growth was monitored every 24 h. After sampling, dry cell weight (DCW) was measured. After cultivation, the cultural broth was centrifuged at 8,000 \u0026times; g for 30 min at 4\u0026deg;C. The sediment was then washed with distilled water. DCW was measured by samples weight through a pre-weighed filter paper (Whatman GF/C) and dried in a vacuum oven for 48 h at 60\u0026deg;C\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage transformation from optical microscope image of\u003c/strong\u003e \u003cspan class=\"BoldItalic\"\u003eC. militaris.\u003c/span\u003e Images of \u003cem\u003eC. militaris\u003c/em\u003e KYL05 were captured at 24 h intervals during 6 days of cultivation using a microscope (Olympus BX51 model, Japan). The color (24 bits) images of the whole colonies obtained through the microscopy were converted to greyscale (8 bits) maps to black and white images using Image J program (version 1.46) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imagej.nih.gov/ij/download.html\u003c/span\u003e\u003c/span\u003e). It was automatically selected for the best range of given the image's intensity values based on the percentage of the total number of pixel values from the lowest to highest pixel value. At 8 bits, the gray level range was 0 to 255. The thresholding process was applied to each image by manually adjusting the level to 154\u003csup\u003e43\u003c/sup\u003e. In the 8-bit image of the border, the mycelia and media from the image of the growing front of the colony were virtually separated using the programs subtract background tool (digital filter). Contrast was then enhanced, followed by thresholding to 180 in the gray-scale and dilated using the dilate tool. The identification of pixels not belonging to the mycelium was done using media filters and tools to find the maximum. In order to remove noise from the original optical microscope image, a transformed image was obtained by removing pixels not belonging to the mycelium. Intensity from each transformed image was measured with the ImageJ program\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSimple linear regression model between the intensity of transformed image and DCW.\u003c/strong\u003e The IBM Statistical Package for the Social Sciences (SPSS) Statistical 27.0.0 program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ibm.com/kr-ko/analytics/spss-statistics-software\u003c/span\u003e\u003c/span\u003e) was used to evaluate variables of image intensity. Among several statistical methods, a simple linear regression model that could analyze the relationship with the dependent variable by considering only one independent variable was used with the following equation (\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\text{y}={\\beta }_{0}+{\\beta }_{1}x+\\text{ϵ}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere y was the predicted value of the dependent variable (y) for any given value of the independent variable (x); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e\u003c/span\u003e was the intercept, the predicted value of y when the x was 0; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}\\)\u003c/span\u003e\u003c/span\u003e was the regression coefficient (expect y to change as x increases); \u003cem\u003ex\u003c/em\u003e was the independent variable (the variable expected to influence y); and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\in\\)\u003c/span\u003e\u003c/span\u003e value was the error of the estimate, or the variation in our estimate of the regression coefficient\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThrough this, the mean, standard deviation, and residual variables were analyzed, along with the 95% confidence interval. In addition, the hypothesis was tested through the analysis of variance (ANOVA). The significance level was at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.K.L. conducted experiments and analyzed the data with the help of J.H.L. (Ju Hun Lee), H.R.K. and Y.C., S.K.L. and H.Y.Y. wrote the draft of the manuscript. J.H.L. (Ja Hyun Lee), C.P., and H.Y.Y. funding acquisition and project administration. S.W.K. conceptualization, supervision, validation, and writing\u0026mdash;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (NRF-2019R1A2C1006793 and NRF-2020R1C1C1005060).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAn, H. E., Lee, K. H., Jang, Y. W., Kim, C. B. \u0026amp; Yoo, H. Y. Improved glucose recovery from Sicyos angulatus by NaOH pretreatment and application to bioethanol production. \u003cem\u003eProcesses\u003c/em\u003e, \u003cb\u003e9\u003c/b\u003e (2), 245 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/pr9020245\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J. H. \u003cem\u003eet al.\u003c/em\u003e Continuous production of bioethanol using microalgal sugars extracted from Nannochloropsis gaditana. \u003cem\u003eKorean J. Chem. Eng\u003c/em\u003e, \u003cb\u003e36\u003c/b\u003e (1), 71\u0026ndash;76 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11814-018-0173-y\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, K. H. \u003cem\u003eet al.\u003c/em\u003e Improvement of enzymatic glucose conversion from chestnut shells through optimization of KOH pretreatment. \u003cem\u003eInt. J. Environ. Res. Public Health\u003c/em\u003e, \u003cb\u003e18\u003c/b\u003e (7), 3772 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph18073772\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRagauskas, A. J. \u003cem\u003eet al.\u003c/em\u003e The path forward for biofuels and biomaterials., \u003cb\u003e311\u003c/b\u003e, 484\u0026ndash;489 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1114736\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCouto, S. R. \u0026amp; Sanrom\u0026aacute;na, M. A. Application of solid-state fermentation to food industry\u0026mdash;A review. \u003cem\u003eJ. Food Eng\u003c/em\u003e, \u003cb\u003e76\u003c/b\u003e (3), 291\u0026ndash;302 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jfoodeng.2005.05.022\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMussagy, C. U., Winterburn, J. \u0026amp; Santos-Ebinuma, V. C. \u0026amp; Pereira JFB Production and extraction of carotenoids produced by microorganisms. \u003cem\u003eAppl. Microbiol. Biotechnol\u003c/em\u003e, \u003cb\u003e103\u003c/b\u003e, 1095\u0026ndash;1114 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00253-018-9557-5\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBCC Research. Biorefinery Products; Global Markets. BCC Research. EGY117C. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17457300.2018.1426702\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMachida, M. Progress of Aspergillus oryzae Genomics. \u003cem\u003eAdv. Appl. Microbiol\u003c/em\u003e, \u003cb\u003e51\u003c/b\u003e, 81\u0026ndash;106 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0065-2164(02)51002-9\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung, D. U. \u003cem\u003eet al.\u003c/em\u003e Optimization of medium composition for enhanced cellulose production by mutant Penicillium brasilianum KUEB15 using statistical method. \u003cem\u003eJ. Ind. Eng. Chem\u003c/em\u003e, \u003cb\u003e25\u003c/b\u003e, 145\u0026ndash;150 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jiec.2014.10.026\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, X. \u003cem\u003eet al.\u003c/em\u003e Improved production of 1,3-propanediol from biodiesel-derived crude glycerol by Klebsiella pneumoniae in fed-batch fermentation. \u003cem\u003eChem. Eng. J\u003c/em\u003e, \u003cb\u003e349\u003c/b\u003e, 25\u0026ndash;36 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cej.2018.05.042\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCha, S. H. \u003cem\u003eet al.\u003c/em\u003e Morphological characteristics of Cordyceps sinensis 16 and production of mycelia and exo-biopolymer from molasses in submerged culture. \u003cem\u003eJ. Ind. Eng. Chem\u003c/em\u003e, \u003cb\u003e12\u003c/b\u003e, 115\u0026ndash;120 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, J. C., Lim, J. S., Kim, J. M., Kim, C. \u0026amp; Kim, S. W. Relationship between morphology and viscosity of the main culture broth of Cephalosporium acremonium M25. \u003cem\u003eKorea-Aust. Rheol. J\u003c/em\u003e, \u003cb\u003e17\u003c/b\u003e (1), 15\u0026ndash;20 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim, J. S. \u003cem\u003eet al.\u003c/em\u003e Relationship between fractal dimension and morphological features of Cephalosporium acremonium M25 in a 30-l bioreactor culture. \u003cem\u003eJ. Microbiol. Biotechnol\u003c/em\u003e, \u003cb\u003e15\u003c/b\u003e (5), 971\u0026ndash;976 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapagianni, M. Fungal morphology and metabolite production in submerged mycelial processes. \u003cem\u003eBiotechnol. Adv\u003c/em\u003e, \u003cb\u003e22\u003c/b\u003e, 189\u0026ndash;259 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biotechadv.2003.09.005\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeiter, L., Rajamanickam, V. \u0026amp; Herwig, C. The filamentous fungal pellet\u0026mdash;relationship between morphology and productivity. \u003cem\u003eAppl. Microbiol. Biotechnol\u003c/em\u003e, \u003cb\u003e102\u003c/b\u003e, 2997\u0026ndash;3006 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00253-018-8818-7\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoran, P. M. Bioprocess engineering principles. Elsevier. Netherlands(1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Toole, D. K. Methods for the direct and indirect assessment of the bacterial content of milk. \u003cem\u003eJ. Appl. Bacteriol\u003c/em\u003e, \u003cb\u003e55\u003c/b\u003e, 187\u0026ndash;201 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2672.1983.tb01315.x\u003c/span\u003e\u003c/span\u003e (1983).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManan, M. A. \u0026amp; Webb, C. Estimating fungal growth in submerged fermentation in the presence of solid particles based on colour development. \u003cem\u003eBiotechnol. Biotechnol. Equip\u003c/em\u003e, \u003cb\u003e32\u003c/b\u003e (3), 618\u0026ndash;627 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13102818.2018.1440974\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul, G. C. \u0026amp; Thomas, C. R. Characterisation of mycelial morphology using image analysis. In: Sch\u0026uuml;gerl K. (eds) Relation Between Morphology and Process Performances. Advances in Biochemical Engineering/Biotechnology, vol\u0026nbsp;60.Springer, Berlin, Heidelberg. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BFb0102278\u003c/span\u003e\u003c/span\u003e (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, S. K. \u003cem\u003eet al.\u003c/em\u003e Improved cordycepin production by Cordyceps militaris KYL05 using casein hydrolysate in submerged conditions. \u003cem\u003eBiomolecules\u003c/em\u003e, \u003cb\u003e9\u003c/b\u003e, 461 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biom9090461\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOu, F., McGoverin, C., Swift, S. \u0026amp; Vanholsbeeck, F. Absolute bacterial cell enumeration using flow cytometry. \u003cem\u003eJ. Appl. Microbiol\u003c/em\u003e, \u003cb\u003e123\u003c/b\u003e (2), 464\u0026ndash;477 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jam.13508\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZalewski, K. \u0026amp; Buchholz, R. Morphological analysis of yeast cells using an automated image processing system. \u003cem\u003eJ. Biotechnol\u003c/em\u003e, \u003cb\u003e48\u003c/b\u003e, 43\u0026ndash;49 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0168-1656(96)01503-9\u003c/span\u003e\u003c/span\u003e (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, R. \u003cem\u003eet al.\u003c/em\u003e Analysis of methods for quantifying yeast cell concentration in complex lignocellulosic fermentation processes. \u003cem\u003eSci. Rep\u003c/em\u003e, \u003cb\u003e11\u003c/b\u003e, 11293 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-90703-8\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePosch, A. E., Herwig, C. \u0026amp; Spadiut, O. Science-based bioprocess design for filamentous fungi. \u003cem\u003eTrends Biotechnol\u003c/em\u003e, \u003cb\u003e31\u003c/b\u003e (1), 37\u0026ndash;44 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tibtech.2012.10.008\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarms, P., Kostov, Y. \u0026amp; Rao, G. Bioprocess monitoring. \u003cem\u003eCurr. Opin. Biotechnol\u003c/em\u003e, \u003cb\u003e13\u003c/b\u003e (2), 124\u0026ndash;127 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0958-1669(02)00295-1\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdul Manan, M. \u0026amp; Webb, C. Estimating fungal growth in submerged fermentation in the presence of solid particles based on colour development. \u003cem\u003eBiotechnol. Biotechnol. Equip\u003c/em\u003e, \u003cb\u003e32\u003c/b\u003e (3), 618\u0026ndash;627 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13102818.2018.1440974\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorzani, W. A weighing method to identify the microbial growth phases in solid-state fermentation tests. \u003cem\u003eWorld J. Microbiol. Biotechnol\u003c/em\u003e, \u003cb\u003e16\u003c/b\u003e (7), 601\u0026ndash;605 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1008970720794\u003c/span\u003e\u003c/span\u003e (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan, Y., Wang, L. \u0026amp; Chen, H. Digital image analysis and fractal-based kinetic modelling for fungal biomass determination in solid-state fermentation. \u003cem\u003eBiochem. Eng. J\u003c/em\u003e, \u003cb\u003e67\u003c/b\u003e, 60\u0026ndash;67 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bej.2012.04.020\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026iacute;az, B. H. C. et. al. Morphological characterization of the growing front of Rhizopus oligosporus in solid media. \u003cem\u003eJ. Food Eng\u003c/em\u003e, \u003cb\u003e101\u003c/b\u003e (3), 309\u0026ndash;317 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jfoodeng.2010.06.028\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajković, K. M., Milošević, N. T., Otašević, S., Jeremić, S. \u0026amp; Arsenijević, V. A. Aspergillus fumigatus branching complexity in vitro: 2D images and dynamic modeling. \u003cem\u003eComput. Biol. Med\u003c/em\u003e, \u003cb\u003e104\u003c/b\u003e, 215\u0026ndash;219 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compbiomed.2018.11.022\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapagianni, M. Quantification of the fractal nature of mycelial aggregation in Aspergillus niger submerged cultures. \u003cem\u003eMicrob. Cell Fact\u003c/em\u003e, \u003cb\u003e5\u003c/b\u003e (1), 1\u0026ndash;13 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1475-2859-5-5\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWucherpfennig, T., Lakowitz, A. \u0026amp; Krull, R. Comprehension of viscous morphology\u0026mdash;evaluation of fractal and conventional parameters for rheological characterization of Aspergillus niger culture broth. \u003cem\u003eJ. Biotechnol\u003c/em\u003e, \u003cb\u003e163\u003c/b\u003e (2), 124\u0026ndash;132 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbiotec.2012.08.027\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhgartner, D., Herwig, C. \u0026amp; Fricke, J. Morphological analysis of the filamentous fungus Penicillium chrysogenum using flow cytometry\u0026mdash;the fast alternative to microscopic image analysis. \u003cem\u003eAppl. Microbiol. Biotechnol\u003c/em\u003e, \u003cb\u003e101\u003c/b\u003e (20), 7675\u0026ndash;7688 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00253-017-8475-2\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeiter, L. \u0026amp; Herwig, C. The filamentous fungus Penicillium chrysogenum analysed via flow cytometry\u0026mdash;a fast and statistically sound insight into morphology and viability. \u003cem\u003eAppl. Microbiol. Biotechnol\u003c/em\u003e, \u003cb\u003e103\u003c/b\u003e (16), 6725\u0026ndash;6735 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00253-019-09943-4\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagedorn, A., Legge, R. L. \u0026amp; Budman, H. Evaluation of spectrofluorometry as a tool for estimation in fed-batch fermentations. \u003cem\u003eBiotechnol. Bioeng\u003c/em\u003e, \u003cb\u003e83\u003c/b\u003e (1), 104\u0026ndash;111 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/bit.10649\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLantz, A. E., J\u0026oslash;rgensen, P., Poulsen, E., Lindemann, C. \u0026amp; Olsson, L. Determination of cell mass and polymyxin using multi-wavelength fluorescence. \u003cem\u003eJ. Biotechnol\u003c/em\u003e, \u003cb\u003e121\u003c/b\u003e (4), 544\u0026ndash;554 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbiotec.2005.08.007\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoehl, D., Solle, D., Hitzmann, B. \u0026amp; Scheper, T. Chemometric modelling with two-dimensional fluorescence data for Claviceps purpurea bioprocess characterization. \u003cem\u003eJ. Biotechnol\u003c/em\u003e, \u003cb\u003e105\u003c/b\u003e (1-2), 179\u0026ndash;188 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0168-1656(03)00189-5\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaack, M. B., Eliasson, A. \u0026amp; Olsson, L. On-line cell mass monitoring of Saccharomyces cerevisiae cultivations by multi-wavelength fluorescence. \u003cem\u003eJ. Biotechnol\u003c/em\u003e, \u003cb\u003e114\u003c/b\u003e (1-2), 199\u0026ndash;208 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbiotec.2004.05.009\u003c/span\u003e\u003c/span\u003e (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrook, R. J. \u0026amp; Arnold, G. C. \u003cem\u003eApplied regression analysis and experimental design\u003c/em\u003e (CRC Press, 2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1201/9781315137674\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeatherbarrow, R. J. Using linear and non-linear regression to fit biochemical data. \u003cem\u003eTrends Biochem. Sci\u003c/em\u003e, \u003cb\u003e15\u003c/b\u003e (12), 455\u0026ndash;458 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0968-0004(90)90295-M\u003c/span\u003e\u003c/span\u003e (1990).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J. H. Significant impact of casein hydrolysate to overcome the low consumption of glycerol by Klebsiella aerogenes ATCC 29007 and its application to bioethanol production. \u003cem\u003eEnergy Conv. Manag\u003c/em\u003e, \u003cb\u003e221\u003c/b\u003e, 113181 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enconman.2020.113181\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, K. H. Statistical optimization of alkali pretreatment to improve sugars recovery from spent coffee grounds and utilization in lactic acid fermentation. \u003cem\u003eProcesses\u003c/em\u003e, \u003cb\u003e9\u003c/b\u003e (3), 494 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/pr9030494\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCox, P. W., Paul, G. C. \u0026amp; Thomas, C. R. Image analysis of the morphology of filamentous micro-organisms., \u003cb\u003e144\u003c/b\u003e, 817\u0026ndash;827 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1099/00221287-144-4-817\u003c/span\u003e\u003c/span\u003e (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBangdiwala, S. I. Regression: simple linear. \u003cem\u003eInt. J. Inj. Control Saf\u003c/em\u003e, \u003cb\u003e25\u003c/b\u003e (1), 113\u0026ndash;115 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17457300.2018.1426702\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorge, D. \u0026amp; Mallery, P. IBM SPSS statistics 26 step by step: A simple guide and reference. Routledge. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4324/9780429056765\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Microscopic, Cordyceps, dry cell weight (DCW), microscopic image intensity (MII) ","lastPublishedDoi":"10.21203/rs.3.rs-933868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-933868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe 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 \u003cem\u003eCordyceps \u003c/em\u003emycelium. 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 (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2 \u003c/sup\u003e\u0026nbsp;= 0.941, \u003cem\u003ep\u003c/em\u003e \u0026lt;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.\u003c/p\u003e","manuscriptTitle":"Rapid and Concise Quantification of Mycelial Growth by Microscopic Image Intensity Model and Application to Mass Cultivation of Fungi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-09-30 17:02:59","doi":"10.21203/rs.3.rs-933868/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-11-09T16:21:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-11-07T21:46:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"03fd0317-711b-4392-803d-0ef703d4751e","date":"2021-10-27T19:04:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-10-20T13:07:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1de72f0c-94b7-4777-b332-d7740c6c6683","date":"2021-10-20T11:08:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-10-10T10:27:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-10-10T10:14:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-09-29T07:17:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-09-29T07:15:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2021-09-24T03:15:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90dca020-1cda-475c-b8ce-9f449a356935","owner":[],"postedDate":"September 30th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":7555127,"name":"Biotechnology and Bioengineering"},{"id":7555128,"name":"General Microbiology"},{"id":7555129,"name":"Biological Chemistry"}],"tags":[],"updatedAt":"2021-12-17T13:02:09+00:00","versionOfRecord":{"articleIdentity":"rs-933868","link":"https://doi.org/10.1038/s41598-021-03512-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2021-12-01 13:02:09","publishedOnDateReadable":"December 1st, 2021"},"versionCreatedAt":"2021-09-30 17:02:59","video":"","vorDoi":"10.1038/s41598-021-03512-4","vorDoiUrl":"https://doi.org/10.1038/s41598-021-03512-4","workflowStages":[]},"version":"v1","identity":"rs-933868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-933868","identity":"rs-933868","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}