Abstract
Mikrobiyolojik çalışmalarda başlangıç bakteri sayısının bilinmesi esastır. Klasik plaka sayım yöntemi en güvenilir yöntemlerden biridir ancak zaman alıcıdır. Bu çalışma, bulanıklığa dayalı hızlı bir yöntem sunmaktadır. Bu yöntemle, sıvı kültürler için büyüme ortamında kör olarak bakteri içermeyen büyüme ortamı kullanılarak doğrudan bir ölçüm yapılabilir. Bu çalışma Excel 2010’da spektrofotometrik verilerle bakteri sayısının hesaplanmasında kullanılacak hesap makinasının nasıl oluşturulacağını anlatmaktadır. Çalışmada spektrofotometrik ölçümlerin kontrolü için standart olarak McFarland standardı ve yayma plaka yöntemi kullanılmıştır. Denemede kullanılan bakteriler uygun besi ortamında büyütülmüş, spektrofotometrik ölçümleri gerçekleştirilmiştir. Daha sonra Excel uygulaması kullanılarak bulanıklık ve bakteri sayısı arasındaki linear ilişkiyi belirlemek için biyoanalitik yaklaşımla hesap makinası oluşturulmuştur. Yapılan ölçümlerdeki absorbans verileri hesap makinasına girilerek bakteri sayıları hesaplanmış, bulunan bakteri sayları kültürel yöntemler kullanılarak doğrulanmıştır. Sonuç olarak, sıvı besi ortamlarından bakteri sayısının hesaplanmasında kullanılabilecek Excel tabanlı bir hesap makinası oluşturulmuştur.
References
- Almeida, A. M., Castel-Branco, M. M., & Falcão, A. C. (2002). Linear regression for calibration lines revisited: Weighting schemes for bio analytical methods. Journal of Chromatography. B., 774(2), 215–222. doi:10.1016/S1570-0232(02)00244-1
- Bressolle, F., Bromet-Petit, M., & Audran, M. (1996). Validation of liquid chromatographic and gas chromatographic methods. Applications to pharmacokinetics. Journal of Chromatography B: Biomedical Sciences and Applications. 686(1), 3–10. doi:10.1016/S0378-4347(96)00088-6
- Cuadros-Rodríguez, L., García-Campaña, A. M., & Bosque-Sendra, J. M. (1996). Statistical estimation of linear calibration range. Analytical Letters, 29(7), 1231-1239. doi:10.1080/00032719608001471
- Edwards, A. (2019). How to do a linear calibration curve in excel. https://www.howtogeek.com/399883/how-to-do-a-linear-calibration-curve-in-excel/ Last accessed on 20.06.2020.
- Escher, B. I., Neale, P. A., & Villeneuve, D. L. (2018). The advantages of linear concentration–response curves for in vitro bioassays with environmental samples. Environmental Toxicology and Chemistry, 37(9), 2273-2280. doi:10.1002/etc.4178
- Farhat, N., Hammes, F., Prest, E., & Vrouwenvelder, J. (2018). A uniform bacterial growth potential assay for different water types. Water Research, 142, 227-235. doi:10.1016/j.watres.2018.06.010
- Gu, H., Liu, G., Wang, J., Aubry, A. F., & Arnold, M. E. (2014). Selecting the correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm in bio analytical LC-MS/MS assays and impacts of using incorrect weighting factors on curve stability, data quality, and assay performance. Analytical Chemistry, 86(18), 8959–8966. doi:10.1021/ac5018265
- Hayashi, Y., Matsuda, R., Ito, K., Nishimura, W., Imai, K., & Maeda, M. (2005). Detection limit estimated from slope of calibration curve: an application to competitive ELISA. Analytical Sciences, 21(2), 167-169. doi:10.2116/analsci.21.167
- Isenberg, H. D. (2004). McFarland Standards. Clinical Microbiology Procedures Handbook, vol 2. DC, USA: ASM Press.
- McFarland, J. (1907). The Nephelometer: An instrument for estimating the number of bacteria in suspensions used for calculating the opsonic index and for vaccines. Journal of the American Medical Association, 49(14), 1176–1178. doi:10.1001/jama.1907.25320140022001f
- Moosavi, S. M., & Ghassabian, S. (2018). Linearity of Calibration Curves for Analytical Methods: A Review of Criteria for Assessment of Method Reliability. In M. T. Stauffer (Ed.), Calibration and Validation of Analytical Methods - A Sampling of Current Approaches. IntechOpen. https://doi.org/10.5772/intechopen.72932
- Pesti, G. M., Billard, L., Wu, S. B., Swick, R. A., Nguyen, T. T. H., & Morgan, N. (2022). Abductive statistical methods improve the results of calibration curve bioassays: An example of determining zinc bioavailability in broiler chickens. Animal Nutrition, 10, 294-304. doi:10.1016/j.aninu.2022.04.008
- Prichard, L., & Barwick, V. (2003). Preparation of Calibration Curves: A Guide to Best Practice. Teddington, UK: VAM. doi:10.13140/RG.2.2.36338.76488
- Sofalvi, S., & Schueler, H. E. (2021). Assessment of bioanalytical method validation data utilizing heteroscedastic seven-point linear calibration curves by EZSTATSG1 customized microsoft excel template. Journal of Analytical Toxicology, 45(8), 772-779. doi:10.1093/jat/bkab047
- Zapata, A., & Ramirez-Arcos, S. (2015). A comparative study of McFarland turbidity standards and the densimat photometer to determine bacterial cell density. Current Microbiology, 70, 907-909. doi:10.1007/s00284-015-0801-2
Abstract
It is essential to know the initial bacterial count in microbiological studies. The classical plate count method is one of the most reliable methods. However, it is time-consuming. This study offers a fast method based on turbidity. This study describes how to create a calculator to be used for calculation of the bacteria concentration with spectrophotometric data in Excel 2010. In the study, McFarland standard and spread plate methods were used as standards for the control of spectrophotometric measurements. Bacteria used in the experiment were grown in suitable media and spectrophotometric measurements were performed. Then, using the Excel application, a calculator was created with a bio analytical approach to determine the linear relationship between turbidity and bacterial count. Bacteria counts were calculated by entering the absorbance data into the calculator, and the results were verified using cultural methods. As a result, an Excel-based calculator was created that can be used to calculate the number of bacteria from broth media.
References
- Almeida, A. M., Castel-Branco, M. M., & Falcão, A. C. (2002). Linear regression for calibration lines revisited: Weighting schemes for bio analytical methods. Journal of Chromatography. B., 774(2), 215–222. doi:10.1016/S1570-0232(02)00244-1
- Bressolle, F., Bromet-Petit, M., & Audran, M. (1996). Validation of liquid chromatographic and gas chromatographic methods. Applications to pharmacokinetics. Journal of Chromatography B: Biomedical Sciences and Applications. 686(1), 3–10. doi:10.1016/S0378-4347(96)00088-6
- Cuadros-Rodríguez, L., García-Campaña, A. M., & Bosque-Sendra, J. M. (1996). Statistical estimation of linear calibration range. Analytical Letters, 29(7), 1231-1239. doi:10.1080/00032719608001471
- Edwards, A. (2019). How to do a linear calibration curve in excel. https://www.howtogeek.com/399883/how-to-do-a-linear-calibration-curve-in-excel/ Last accessed on 20.06.2020.
- Escher, B. I., Neale, P. A., & Villeneuve, D. L. (2018). The advantages of linear concentration–response curves for in vitro bioassays with environmental samples. Environmental Toxicology and Chemistry, 37(9), 2273-2280. doi:10.1002/etc.4178
- Farhat, N., Hammes, F., Prest, E., & Vrouwenvelder, J. (2018). A uniform bacterial growth potential assay for different water types. Water Research, 142, 227-235. doi:10.1016/j.watres.2018.06.010
- Gu, H., Liu, G., Wang, J., Aubry, A. F., & Arnold, M. E. (2014). Selecting the correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm in bio analytical LC-MS/MS assays and impacts of using incorrect weighting factors on curve stability, data quality, and assay performance. Analytical Chemistry, 86(18), 8959–8966. doi:10.1021/ac5018265
- Hayashi, Y., Matsuda, R., Ito, K., Nishimura, W., Imai, K., & Maeda, M. (2005). Detection limit estimated from slope of calibration curve: an application to competitive ELISA. Analytical Sciences, 21(2), 167-169. doi:10.2116/analsci.21.167
- Isenberg, H. D. (2004). McFarland Standards. Clinical Microbiology Procedures Handbook, vol 2. DC, USA: ASM Press.
- McFarland, J. (1907). The Nephelometer: An instrument for estimating the number of bacteria in suspensions used for calculating the opsonic index and for vaccines. Journal of the American Medical Association, 49(14), 1176–1178. doi:10.1001/jama.1907.25320140022001f
- Moosavi, S. M., & Ghassabian, S. (2018). Linearity of Calibration Curves for Analytical Methods: A Review of Criteria for Assessment of Method Reliability. In M. T. Stauffer (Ed.), Calibration and Validation of Analytical Methods - A Sampling of Current Approaches. IntechOpen. https://doi.org/10.5772/intechopen.72932
- Pesti, G. M., Billard, L., Wu, S. B., Swick, R. A., Nguyen, T. T. H., & Morgan, N. (2022). Abductive statistical methods improve the results of calibration curve bioassays: An example of determining zinc bioavailability in broiler chickens. Animal Nutrition, 10, 294-304. doi:10.1016/j.aninu.2022.04.008
- Prichard, L., & Barwick, V. (2003). Preparation of Calibration Curves: A Guide to Best Practice. Teddington, UK: VAM. doi:10.13140/RG.2.2.36338.76488
- Sofalvi, S., & Schueler, H. E. (2021). Assessment of bioanalytical method validation data utilizing heteroscedastic seven-point linear calibration curves by EZSTATSG1 customized microsoft excel template. Journal of Analytical Toxicology, 45(8), 772-779. doi:10.1093/jat/bkab047
- Zapata, A., & Ramirez-Arcos, S. (2015). A comparative study of McFarland turbidity standards and the densimat photometer to determine bacterial cell density. Current Microbiology, 70, 907-909. doi:10.1007/s00284-015-0801-2
Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Early Pub Date | April 29, 2023 |
| Publication Date | April 30, 2023 |
| Submission Date | June 20, 2022 |
| Published in Issue | Year 2023 Volume: 28 Issue: 1 |
