Development of an ABICS System for Automatic Colony Counting of Escherichia coli and Enterobacter aerogenes from Smartphone Photographs
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Abstract
Counting colonies of Escherichia coli ATCC25922 (ECA) and Enterobacter aerogenes DMST2720 (EAD) is a crucial step in assessing the quality of food or raw milk. Manual counting typically takes approximately 2 - 5 minutes per Petri dish, depending on the colony density. This study presents an "Android Bacteria Image Counting System" (ABICS) that employs Projection Profile, Circle Hough Transform, and Power Law Transformation image processing techniques to enhance image clarity and accurately count colonies. In experiments using 84 Petri dish images, ABICS demonstrated an average counting accuracy of 90.77% when compared to manual counting, which generally exhibits an error rate of 5 - 10%. Significantly, ABICS required only 3 - 5 seconds per dish for colony counting, which is at least 24 times faster (averaging 35 - 100 times faster) than manual counting. Furthermore, ABICS significantly reduces the analysis time, making it a potentially valuable tool for enhancing efficiency and reducing workload in microbiological analysis.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
เนื้อหาและข้อมูลในบทความที่ลงตีพิมพ์ในวารสารวารสารวิทยาศาสตร์และเทคโนโลยีถือเป็นข้อคิดเห็นและความรับผิดชอบของผู้เขียนบทความโดยตรงซึ่งกองบรรณาธิการวารสาร ไม่จำเป็นต้องเห็นด้วย หรือร่วมรับผิดชอบใด ๆ
บทความ ข้อมูล เนื้อหา รูปภาพ ฯลฯ ที่ได้รับการตีพิมพ์ในวารสารวารสารวิทยาศาสตร์และเทคโนโลยีถือเป็นลิขสิทธิ์ของวารสารวารสารวิทยาศาสตร์และเทคโนโลยีหากบุคคลหรือหน่วยงานใดต้องการนำทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ต่อหรือเพื่อกระทำการใด ๆ จะต้องได้รับอนุญาตเป็นลายลักษณ์อักษรจากวารสารวารสารวิทยาศาสตร์และเทคโนโลยี ก่อนเท่านั้น
References
Below is the provided list of references, reformatted with an additional blank line between each reference, as requested, to enhance readability while preserving the original citation details and adhering to a formal tone.
Badalyan, G., Díaz, C., Bücking, M., & Lipski, A. (2018). Novel sensor platform for rapid detection and quantification of coliforms on food contact surfaces. Journal of Microbiological Methods, 153, 74-83. doi:10.1016/j.mimet.2018.09.009
Sever, B., U.S. Food and Drug Administration (FDA). 2020, Salem Press.
Study of Escherichia coli analysis in frozen seafood by MPN technique / Suksa kan wikhro Escherichia coli nai ahan tha-le chae khaeng duai withi MPN technique. (2001). Bulletin of the Department of Medical Sciences (Thailand), 43(2), 95-101. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&AuthType=sso&db=edsagr&AN=edsagr.TH2005000895&site=eds-live&custid=ns004377
Wouters, J. T. M., Ayad, E. H. E., Hugenholtz, J., & Smit, G. (2002). Microbes from raw milk for fermented dairy products. International Dairy Journal, 12(2-3), 91-109. doi:10.1016/S0958-6946(01)00151-0
Pisuttilap, N., & Saengswetmaneengam, N. (2001). Study of Escherichia coli analysis in frozen seafood by MPN technique / Suksa kan wikhro Escherichia coli nai ahan tha-le chae khaeng duai withi MPN technique. Bulletin of the Department of Medical Sciences (Thailand), 43(2), 95-101.
Sophie, E., Juliane, E., Valérie, E., Sylvie, E. L., & Anne, E. (2015). Bacterial colonies in solid media and foods: a review on their growth and interactions with the micro-environment. Frontiers in Microbiology, 6. doi:10.3389/fmicb.2015.01284
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. doi:10.1016/j.mimet.2018.09.004
Albaradei, S. A., Napolitano, F., Uludag, M., Thafar, M., Napolitano, S., Essack, M., & Gao, X. (2020). Automated Counting of Colony Forming Units Using Deep Transfer Learning From a Model for Congested Scenes Analysis. IEEE Access, 8, 164340-164346. doi:10.1109/ACCESS.2020.3021656
Shamshad, A. (2020). Building Computer Vision Applications Using Artificial Neural Networks: With Step-by-Step Examples in OpenCV and TensorFlow with Python. Berkeley, CA: Apress.
Gonzalez, R. C., & Woods, R. E. (2008). Digital image processing (4th ed., Global edition ed.): Pearson.
Prasad Reddy, P.V.G.D. (2021). Blood vessel extraction in fundus images using hessian eigenvalues and adaptive thresholding. Evolutionary Intelligence, 14(2), 577-582.
Jarvis, B. (2016). Statistical aspects of the microbiological examination of foods (3rd ed.): Elsevier.
