Sonlu Kantitatif Hassasiyet Sınırları ile Muayene Özelliklerinin İzlenmesinde Değiştirilmiş Q-Kontrol Şemasının Uygulanması: Kapsül Kabuğunda Bioburden Numaralandırması Örneği

Öz

Yönetim karar verme desteğine ek olarak, süreçleri kontrol etmek, izlemek ve iyileştirmek için çeşitli iş alanlarında istatistiksel süreç kontrolü (SPC) metodolojilerinin uygulanması giderek daha önemli hale geldi. Bununla birlikte, pek çok inceleme özelliğinin sınırlı nicelik sınırları vardır ve bunun ötesinde sonuçlar, belirli seyreltme seviyesinden sonra sağlık bakım ürünlerindeki mikrobiyolojik yükün test edilmesi gibi " daha yüksek" olarak rapor edilir. 1:10, 1:50 veya 1: 100. Bu vaka çalışması, boş sert jelatin kapsülün ardışık teslimatlarının kalitesinin biyolojik yükünün izlenmesi için Laney tarafından değiştirilmiş öznitelik işlem-davranış şeması ile birleştirilmiş kalite skoru eğilim tablosu konseptinin uygulanmasını gösterdi. Mikrobiyolojik veri tabanı, Toplam Canlı Aerobik Sayımın (TVAC) ve Toplam Maya ve Küf Sayımının (TYMC) hem spesifikasyon limitlerine hem de minimum hassasiyet limitine göre aralıklara bölünmüştür. Her segmente düşük biyolojik yük değerinden başlayarak daha yüksek eşiğe kadar bir puan atandı. Veri seti modelinin ön araştırması, kaydın sıradan kontrol grafiğinin oluşturulması için herhangi bir varsayılan dağılımı takip etmediğini gösterdi. Veriler doğru (pozitif olarak) çarpıktır ve Poisson, binom veya normal dağılımı takip etmeye yönelik belirgin bir eğilim yoktur. Laney-Kalite çizelgesi, sapkın sonuçlar tespit edilerek CFU'lar sıralandıkça biyolojik yük içeriğini gösterdi.

Anahtar Kelimeler

• Quality Score Chart,
• SPC,
• Truncated Distribution
• TVAC
• TYMC

Implementation of Modified Q-Control Chart in Monitoring of Inspection Characteristics with Finite Quantification Sensitivity Limits: A Case Study of Bioburden Enumeration in Capsule Shell

Öz

Application of statistical process control (SPC) methodologies has become increasingly crucial in various business fields to control, monitor and improve processes, in addition to the support of the management decision-making. However, many inspection characteristics have limited quantification limits beyond which results are reported as either “< lower than” or “> higher than” such as testing of microbiological burden in healthcare products after definite dilution level ex. 1:10, 1:50 or 1:100. The present case study demonstrated the application of combined quality score trending chart concept with Laney-modified attribute process-behavior chart for monitoring the bioburden the quality of successive deliveries of empty hard gelatin capsule. Microbiological database was segmented into intervals based on both the specification limits and the minimum sensitivity limit of the Total Viable Aerobic Count (TVAC) and Total Yeast and Mold Count (TYMC). Each segment was assigned a score starting for low bioburden value to the higher threshold. Preliminary investigation of the dataset pattern showed that the record did not follow any presumed distribution for construction of the ordinary control chart. Data are right (positively) skewed with no apparent tendency to follow Poisson, binomial or normal distribution. Laney-Quality chart demonstrated bioburden contents as CFUs ranks with aberrant results spotted.

Anahtar Kelimeler

• Quality Score Chart
• SPC
• Truncated Distribution
• TVAC
• TYMC

Kaynakça

1. [1]. Geis, Philip A., ed. Cosmetic microbiology: A practical approach. CRC Press, 2020.
2. [2]. Knotkova, H., Pappagallo, M. Adjuvant analgesics. Anesthesiology clinics, 2007, 25 (4),775-86.
3. [3]. Burke, L., and Ryan, A., The Complex Relationship between Cost and Quality in US Health Care, AMA Journal Of Ethics, 2014, 16, 124-130.
4. [4]. Eissa, M.E. Drug Recall Monitoring and Trend Analysis: A Multidimensional Study. Global Journal on Quality and Safety in Healthcare, 2019, 2 (2),34-9.
5. [5]. Potdar, Mr Manohar A. Pharmaceutical quality assurance. Pragati Books Pvt. Ltd., 2006.
6. [6]. He, T.T., Ung, C.O.L., Hu, H. and Wang, Y.T. Good manufacturing practice (GMP) regulation of herbal medicine in comparative research: China GMP, cGMP, WHO-GMP, PIC/S and EU-GMP. European Journal of Integrative Medicine, 2015, 7 (1),55-66.
7. [7]. Das, A. Testing and statistical quality control in textile manufacturing. Process Control in Textile Manufacturing, Woodhead Publishing, 2013, 41-78.
8. [8]. Western, E., Statistical quality control handbook. Western Electric Co., 1956, 25-8.

9. [9]. Wheeler, Donald J., Advanced topics in statistical process control, Vol. 470. SPC press, Knoxville, TN, 1995.
10. [10]. Wheeler, D. J. and Chambers, D.S., Understanding Statistical Process Control, Second Edition, SPC Press, Inc., 1992.
11. [11]. Eissa, M.E., Application of Laney control chart in assessment of microbiological quality of oral pharmaceutical filterable products, Bangladesh Journal of Scientific and Industrial Research, 2017, 52 (3), 239-246.
12. [12]. Nelson, L.S., The Shewhart control chart—tests for special causes, Journal of quality technology, 1984, 16 (4), 237-239.
13. [13]. Chakraborti, S., Van de Wiel, M.A., A nonparametric control chart based on the Mann-Whitney statistic. Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2008.
14. [14]. Xie, M., Goh, T.N. and Ranjan, P., Some effective control chart procedures for reliability monitoring, Reliability Engineering & System Safety, 77 (2),143-150, 2002.
15. [15]. Essam Eissa, M., Monitoring of Cryptosporidium spp. Outbreaks Using Statistical Process Control Tools and Quantitative Risk Analysis Based on NORS Long-term Trending, Microbiology Journal, 2017, 9 (1), 1-7.
16. [16]. Kaminsky, F.C., Benneyan, J.C., Davis, R.D. and Burke, R.J., Statistical control charts based on a geometric distribution, Journal of Quality Technology, 1992, 24(2), 63-69.
17. [17]. Prett, D.M. and García, C.E., Fundamental process control: Butterworths series in chemical engineering, Butterworth-Heinemann, Stoneham, MA, USA, 2013.
18. [18]. Automotive Industry Action Group, Fundamental Statistical Process Control, AIAG, Southfield, MI, 1991.
19. [19]. Zaciewski, R.D., Attribute control charts: opportunities for application, Quality Digest, 1995 , 15 (5), 36 -41.
20. [20]. Eissa, M., and Mahmoud, A., Evaluation of Microbial Recovery from Raw Materials for Pharmaceutical Use, J.Food Pharm.Sci., 2016, 4 (1), 6-11.
21. [21]. Appling, D., 2008. Software Review of Prism 5, Journal of the American Chemical Society, 2008, 130 (18), 6056-6056.
22. [22]. Wakefield, D., McLaughlin, K., Larson, R. and Farber, E., The Minitab manual, Prentice Hall, Boston, MA, 2012.
23. [23]. James, J., Gentry, S., Majors, M. and Davis, R., Turn Around Time for Lower Respiratory Gram Stain and Culture across Hospital Shifts, 2020.
24. [24]. Kenett, R., Zacks, S. and Amberti, D., Modern industrial statistics, John Wiley, Sussex, 2014.
25. [25]. Chiu, W.K., Economic design of attribute control charts, Technometrics, 1975, 17 (1), 81-87.
26. [26]. Eissa, M.E., Application of attribute control chart in the monitoring of the physical properties of solid dosage forms, Journal of Progressive Research in Modern Physics and Chemistry (JPRMPC), 2018, 3 (1),104-113.
27. [27]. Eissa, M.E., Mahmoud, A.M., Nouby, A.S. and Farag, M.S., Research Article A Case of Rapidly Declining Contamination of Antimalarial Tablet by Stenotrophomonas maltophilia, Sch. Acad. J. Pharm., 2015, 4(7), 347-350.
28. [28]. Lehrman, K.H., Control Charts for Variables and Attributes with Process Safety Analyses, Quality Engineering, 1991, 4 (2), 243-318.
29. [29]. Vukov, A., McClave, J. and Sincich, T., Minitab manual, Prentice Hall, Upper Saddle River, NJ, 2003.
30. [30]. Eissa, M.E., Variable and attribute control charts in trend analysis of active pharmaceutical components: Process efficiency monitoring and comparative study, Experimental Medicine (EM), 1 (1), 2018, 32-44.
31. [31]. Ryan, T.P. and Schwertman, N.C., Optimal limits for attributes control charts, Journal of Quality Technology, 1997, 29 (1), 86-98.
32. [32]. Mohammed, M.A., Panesar, J.S., Laney, D.B. and Wilson, R., Statistical process control charts for attribute data involving very large sample sizes: a review of problems and solutions, BMJ quality & safety, 2013, 22 (4), 362-368.
33. [33]. Allen, T.T. Software overview and methods review: Minitab. In Introduction to Engineering Statistics and Lean Six Sigma, Springer, London, 2019.
34. [34]. Eissa, M.E., APPLICATION OF STATISTICAL Process Control On Analysis Of Surgery-Related Infection Record: An Extended Study Of Three Selected Countries Using Statistical Software, Current Trends in Pharmaceutical Research, 2019, 6 (1), 18-36.
35. [35]. Newton, I., Minitab cookbook, Packt Publishing Ltd, , Birmingham, UK, 2014.
36. [36]. Eissa, M.E. and Hamed, H.S., Application of statistical quality control tools for monitoring of pharmaceutical product properties, Biological Sciences-PJSIR, 2019, 62 (1), 39-48.
37. [37]. Berti-Equille, L., Measuring and modelling data quality for quality-awareness in data mining, In Quality measures in data mining, Springer, Berlin, Heidelberg, 2007.
38. [38]. Hassan, A. and Nawaz, M., Microbiological and physicochemical assessments of groundwater quality at Punjab, Pakistan, African Journal of Microbiology Research, 2014, 8(28), 2672-2681.
39. [39]. Duncan, S. and Ho, J., Estimation of viable spores in Bacillus atrophaeus (BG) particles of 1 to 9 μm size range, CLEAN–Soil, Air, Water, 2008, 36 (7), 584-592.
40. [40]. Bai, J. and Ng, S., Tests for skewness, kurtosis, and normality for time series data, Journal of Business & Economic Statistics, 2005, 23 (1), 49-60.
41. [41]. Motulsky, H.J. and Brown, R.E., Detecting outliers when fitting data with nonlinear regression–a new method based on robust nonlinear regression and the false discovery rate, BMC bioinformatics, 2006, 7(1), 1-20.
42. [42]. Fleming, M.C. and Nellis, J.G., The essence of statistics for business. Prentice Hall, New York, 1996.
43. [43]. Moon, J., Theory and Application of J Charts for Holistic Risk Based Statistical Adverse Event Trending, International Journal of Health and Economic Development, 2018, 4(1), 9-31.
44. [44]. Woodall, W.H., The use of control charts in health-care and public-health surveillance, Journal of Quality Technology, 38 (2), 2006, 89-104.
45. [45]. Chakraborti, S. and Eryilmaz, S., A nonparametric Shewhart-type signed-rank control chart based on runs, Communications in Statistics—Simulation and Computation, 2007, 36(2), 335-356.
46. [46]. Bachik, H., Kamaruddin, S., Khan, Z.A. and Suhail, A., A Methodology for Ranking of Alarms in Control Charts, Jurnal Mekanikal, 2005, 20(2), 52-67.