Mobile application for automatic bacterial density estimation
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Published
May 12, 2023
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Abstract
The traditional method for calculating the concentration of viable bacteria in a pure source is to use serial dilutions. This conventional method takes more than 72 hr and involves a series of complex steps that must be done by a microbiologist, including culturing the colonies. In contrast, this study utilizes a combination of image processing and machine learning developed into a mobile application that can estimate the concentration of viable bacteria by simply taking a picture, substantially reducing the time required. To create this new estimation model, a series of image processing techniques optimize and standardize a dataset of photographed test tubes containing pure bacterial suspension, culminating in the delimiting of the Turbidity Testing Zone (TTZ), which is uniform across all the test tube photos. Bacterial concentration is correlated with suspension turbidity, so statistical data from the pixels within the TTZ is analyzed using four machine learning algorithms to find the optimal estimating model. The finished model becomes the foundation of the Viable Bacteria Image Estimating System (VBIES) android application, which enables any user to easily and conveniently determine the concentration of viable bacteria in a test tube with an accuracy of 97.57%. In contrast to the several days required by the traditional methods, the VBIES application estimates the concentration of viable bacteria in only 3-5 seconds.
Keywords: estimating bacterial concentration, image processing, machine learning, mobile application, decision tree learning
References
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doi.org/10.1016/j.jcyt.2019.06.005
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Atmaja, R. D., Murti, M. A., Halomoan, J., & Suratman, F. Y. (2016). An image processing method to convert RGB image into binary. Indonesian Journal of Electrical Engineering and Computer Science, 3(2), 377-382-382. http://dx.doi.org/10.11591/ijeecs.v3.i2.pp377-382
Beal, J., Farny, N. G., Haddock-Angelli, T., Selvarajah, V., Baldwin, G. S., Buckley-Taylor, R., . . . Emory. (2020). Robust estimation of bacterial cell count from optical density. Communications Biology, 3(1), 512. http://dx.doi.org/10.1038/s42003-020-01127-5
Beck, V. L. (2017). Linear Regression: Models, Analysis, and Applications. Hauppauge, New York: Nova Science.
Bovik, A. C. (2009). The essential guide to image processing (2nd ed.). USA: Academic Press.
Boyle, B. H. (2011). Support Vector Machines: Data Analysis, Machine Learning, and Applications. New York: Nova Science.
Cangliang, S., & Yifan, Z. (2022). Introductory Microbiology Lab Skills and Techniques in Food Science. London: Academic Press.
Cheewaprakobkit, P. (2019). Predicting Student Academic Achievement by Using the Decision Tree and Neural Network Techniques. Human Behavior, Development and Society, 12(2), 34-43.
Dong, M., Yang, S., Yang, X., Xu, M., Hu, W., Wang, B., . . . Sun, G. (2022). Water quality drives the distribution of freshwater cable bacteria. The Science of the Total Environment, 841, 156468. http://dx.
doi.org/10.1016/j.scitotenv.2022.156468
Godoy, A. C., Santos, O. O., Nakano, A. Y., Siepmann, D. A. B., Schneider, R., & Pfrimer, F. W. D. (2018). Snapshots Analyses for Turbidity Measurements in Water. Water, Air, and Soil Pollution, 229(12), 1-11. http://dx.doi.org/10.1007/s11270-018-4034-4
Jayapal, M., Jagadeesan, H., Krishnasamy, V., Shanmugam, G., Muniyappan, V., Chidambaram, D., & Krishnamurthy, S. (2022). Demonstration of a plant-microbe integrated system for treatment of real-time textile industry wastewater. Environmental Pollution, 302, 119009. http://dx.doi.org/10.1016/j.
envpol.2022.119009
Jena, M., & Dehuri, S. (2020). Decision Tree for Classification and Regression: A State-of-the Art Review. Informatica, 44(4), 291. http://dx.doi.org/10.31449/inf.v44i4.3023
Kanan, C., & Cottrell, G. W. (2012). Color-to-Grayscale: Does the Method Matter in Image Recognition?. PLoS ONE, 7(1), 1-7. http://dx.doi.org/10.1371/journal.pone.0029740
Kumar, L., Afzal, M. S., & Ahmad, A. (2022). Prediction of water turbidity in a marine environment using machine learning: A case study of Hong Kong. Regional Studies in Marine Science, 52, 102260. http://dx.doi.org/10.1016/j.rsma.2022.102260
Lior, R., & Oded, Z. M. (2008). Data Mining With Decision Trees: Theory And Applications. Singapore: World Scientific.
Maturin, L., & Peeler, J. T. (2001). Laboratory Methods (Food). In BAM Chapter 3: Aerobic Plate Count. Berlin: Silver Spring.
Mohammadiun, S., Hu, G., Alavi Gharahbagh, A., Mirshahi, R., Li, J., Hewage, K., & Sadiq, R. (2021). Optimization of integrated fuzzy decision tree and regression models for selection of oil spill response method in the Arctic. Knowledge-Based Systems, 213, 106676. http://dx.doi.org/10.1016/j.
knosys.2020.106676
Moore-Colyer, M. J. S., & Fillery, B. G. (2012). The effect of three different treatments on the respirable particle content, total viable count and mould concentrations in hay for horses. EAAP Scientific Series, 132(1), 101-106.
Novik, G., Savich, V., & Meerovskaya, O. (2018). Geobacillus Bacteria: Potential Commercial Applications in Industry, Bioremediation, and Bioenergy Production. London: IntechOpen.
Osano, E., Bessho, M., Asai, Y., & Takei, M. (1979). Evaluation of diluents for the recovery of total viable microorganisms in human oral material (author's transl). Aichi Gakuin Daigaku Shigakkai shi, 17(1), 7-18.
Pare, S., Kumar, A., Singh, G. K., & Bajaj, V. (2020). Image Segmentation Using Multilevel Thresholding: A Research Review. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44(1), 1-29. http://dx.doi.org/10.1007/s40998-019-00251-1
Prockop, D. J. E., Bunnell, B. A. E., Phinney, D. G. E., Pochampally, R., & Walker, J. P. E. (2008). Colony Forming Unit Assays for MSCs. Totowa, N J: Humana Press.
Raveendran, S., Edavoor, P. J., Kumar, Y. B. N., & Vasantha, M. H. (2018). Design and Implementation of Reversible Logic based RGB to Gray scale Color Space Converter. United States: IEEE.
Renganathan, S., & Olubukola Oluranti, B. (2021). Utilization of Microbial Consortia as Biofertilizers and Biopesticides for the Production of Feasible Agricultural Product. Biology, 10(1111), 1111-1111. http://dx.doi.org/10.3390/biology10111111
Sarrafzadeh, H., Aghajari, G., & Shanbehzadeh, J. (2010, 2010/01/01/). A Text Localization Algorithm in Color Image via New Projection Profile. New Zealand: Australia/Oceania.
Shetty, D. K., Acharya, U. D., Narendra, V. G., & Prajual, P. J. (2020). Intelligent System to Evaluate the Quality of DRC using Image Processing and then Categorize using Artificial Neural Network (ANN). Indian Journal of Agricultural Research, 54(6), 716-723. http://dx.doi.org/10.18805/IJARe.A-5374
Tao, S., Luze, Z., & Long-Fei, W. (2014). A Method for Quantitative Determination of the Number of Magnetosomes in Magnetotactic Bacteria by a Spectrophotometer. IEEE Transactions on Magnetics, 50(11), 1-4. http://dx.doi.org/10.1109/TMAG.2014.2323953
Taylor, R. H., Allen, M. J., & Geldreich, E. E. (1983). Standard plate count: A comparison of pour plate and spread plate methods. Journal (American Water Works Association), 75(1), 35-37. http://dx.doi.org/
10.1002/j.1551-8833.1983.tb05055.x
Tomasiewicz, D. M., Hotchkiss, D. K., Reinbold, G. W., Read, R. B., & Hartman, P. A. (1980). The Most Suitable Number of Colonies on Plates for Counting 1. Journal of food protection, 43(4), 282-286. http://dx.doi.org/10.4315/0362-028X-43.4.282
Velier, M., Chateau, A. N. N. E. L. I. N. E., Malenfant, C., Ouffai, S., Calmels, B., Chabannon, C., & LemariÉ, C. (2019). Validation of a semi automatic device to standardize quantification of Colony-Forming Unit (CFU) on hematopoietic stem cell products. Cytotherapy, 21(8), 820-823. http://dx.
doi.org/10.1016/j.jcyt.2019.06.005
Yunxia, W., Lijuan, J., Cuizhi, L. I., Zhiyong, L. U., & Lijun, L. I. U. (2021). Evaluation on Uncertainty of Detection Results of Aerobic Plate Count. Asian Agricultural Research, 13(6), 59-62. http://dx.doi.
org/10.19601/j.cnki.issn1943-9903.2021.06.014
Zgurovsky, M., Sineglazov, V., & Chumachenko, E. (2021). Artificial Intelligence Systems Based on Hybrid Neural Networks. Switzerland: Springer Cham.
Zhu, G., Yan, B., Xing, M., & Tian, C. (2018). Automated counting of bacterial colonies on agar plates based on images captured at near-infrared light. Journal of Microbiological Methods, 153, 66-73. http://dx.doi.org/10.1016/j.mimet.2018.09.004
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How to Cite
TAITHONG, Phongsatorn et al.
Mobile application for automatic bacterial density estimation.
Naresuan University Journal: Science and Technology (NUJST), [S.l.], v. 31, n. 2, p. 13-32, may 2023.
ISSN 2539-553X.
Available at: <https://www.journal.nu.ac.th/NUJST/article/view/Vol-31-No-2-2023-13-32>. Date accessed: 09 may 2024.
doi: https://doi.org/10.14456/nujst.2023.12.