DOI: https://doi.org/10.15588/1607-3274-2019-1-16

THE METHOD OF MULTIVARIATE STATISTICAL ANALYSIS OF THE TIME MULTIVARIATE CRITICAL QUALITY ATTRIBUTES OF MANUFACTURE PROCESS WITH THE DATA FACTORIZATION

Ye. V. Havrylko, O. A. Kurchenko, I. V. Tereshchenko, A. I. Tereshchenko

Abstract


Context. This paper presents a method for solving the problem of product’s quality assurance at the stage of the initial
manufacture process design in accordance with the process-analytical technology for the design of modern certified manufacturing –
QbD. The method uses the information technologies of multivariate statistical analysis (MSA) to evaluate the influence of time
multivariate critical process parameters (CPPs) on the time product critical quality attributes (CQAs). Preparatory transformation of
clusters of critical process (manufacture process) parameters into factors of product critical quality attributes was carried out.
Objective. To disclose the method of multivariate statistical analysis for assessing the character and features of the influence of time multivariate critical process parameters on time multivariate critical quality attributes at the design stage of the manufacture process.
Method. The method consistently uses: statistical procedures of exploratory multivariate data analysis; transformation the homogeneous observed values matrices of CPPs and product CQAs into data frame (table) with factorized data; construction the regression trees of multivariate CPPs with a multivariate responses (CQAs). The method is implemented the R language packages software.
Results. Factorized time multivariate CPPs make it possible to use methods of multivariate statistical analysis for evaluating the influence of CPPs factors on the time multivariate CQAs.
Conclusions. This method of statistical analysis, together with statistical multivariate canonical analysis, represents an up-to-date information technology for detailed estimation the influence of time multivariate CPPs objects and some CPPs components on CQAs.


Keywords


quality-by-design; critical quality attributes; critical process parameters; design of experiment; multivariate statistical analysis

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GOST Style Citations


1. Deming W. E. Out of the crisis, Cambridge, Mass:
Massachusetts Institute of Technology, Center for Advanced
Engineering Study; Cambridge: Cambridge University Press,
1986, 507 p.
2. Lionberger R. A., Lee S. L., Lee L. M., Raw A., X. Yu.
Lawrence Quality by Design: Concepts for ANDAs, American
Association of Pharmaceutical Scientists journal. Jun. 2008,
10(2), pp. 268–276. DOI: 10.1208/s12248-008-9026-7
3. ICH Q8. Pharmaceutical Development. [Electronic resource].
Access mode:
http://www.ich.org/products/guidelines/quality/qualitysingle/
article/pharmaceutical-development.html
4. Eshan S. J. A Case Study on Quality Function Deployment
(QFD), International Organization of Scientific Research
Journal of Mechanical and Civil Engineering (IOSR-JMCE),
Nov-Dec, 2012, Volume 3, Issue 6, pp. 27–35.
https://doi.org/10.9790/1684-0362735
5. Zhang L., Mao S. Application of quality by design in the current
drug development, Shenyang Pharmaceutical University,
Wenhua Road, Shenyang, China: Asian journal of
pharmaceutical sciences, 2017 (12), No.103, pp. 1–8.
https://doi.org/10.1016/j.ajps.2016.07.006
6. Guidance for Industry PAT A Framework for Innovative
Pharmaceutical Development, Manufacturing, and Quality
Assurance. [Electronic resource]. Access mode:
http://www.fda.gov/cder/OPS/PAT.htm
7. ISO 9001: 2015. Quality management systems. [Electronic
resource]. Access mode:
https://www.iso.org/standard/62085.html
8. ISO 22000: 2005 Food safety management systems. [Electronic
resource]. Access mode:
https://www.iso.org/standard/35466.html
9. Legendre P. Short course on Advanced spatial ecology.
Research Center of Plant Ecology and Biodiversity
Conservation, Institute of Botany, Chinese Academy of Sciences,
1–5 July 2006, pp. 575–600. [Electronic resource]. Access
mode:
http://biol09.biol.umontreal.ca/PLcourses/Legendre_Canonical.
pdf
10. Rao S., Samant P., Kadampatta A., Shenoy R. An Overview of
Taguchi Method: Evolution, Concept and Interdisciplinary
Applications, International Journal of Scientific & Engineering
Research, October 2013, Vol. 4, Issue 10, pp. 621–626.
[Electronic resource]. Access mode:
https://www.ijser.org/onlineResearchPaperViewer.aspx?An-
Overview-of-Taguchi-Method-Evolution-Concept-and-
Interdisciplinary-Applications.pdf
11. Coghlan A. A Little Book of R For Multivariate Analysis,
Release 0.1, February 19 2017, 47 p. [Electronic resource],
Access mode: http://a-little-book-of-r-for-timeseries.
readthedocs.org/
12. Food and Drug Administration. Final Report on Pharmaceutical
cGMPs for the 21st Century. A Risk Based Approach.
[Electronic resource]. Access mode:
https://www.fda.gov/downloads/drugs/developmentapprovalpro
cess/manufacturing/questionsandanswersoncurrentgoodmanufac
turingpracticescgmpfordrugs/ucm176374.pdf
13. [Kannissery P., Tahir M. A., Charoo N. A., Ansari S. H., Ali
J.]Pharmaceutical product development: A quality by design
approach, International Journal of Pharmaceutical
Investigation, Jul-Sep 2016, 6(3), pp. 129–138. DOI:
10.4103/2230-973X.187350.
14. ISO 31000 Risk management. [Electronic resource]. Access
mode: https://www.iso.org/iso-31000-risk-management.html
15. Guidance for Industry. PAT – A Framework for Innovative
Pharmaceutical Development, Manufacturing, and Quality
Assurance. [Electronic resource]. Access mode:
https://www.fda.gov/downloads/drugs/guidances/ucm070305.p
df
16. Mohamed I. Progressive Modeling: The Process, the Principles,
and the Applications, Procedia Computer Science, 2013, Vol.
16, pp. 39–48. https://doi.org/10.1016/j.procs.2013.01.005
17. Araujo F., Pimentel P., Fogliatto F. S. Variable selection
methods in multivariate statistical process control: A systematic
literature review, Department of Industrial Engineering,
Federal University of Rio Grande do Sul, 90035-190 Porto
Alegre, RS, Brazil, Computers & Industrial Engineering.
January 2018, Vol. 115, pp. 603–619.
https://doi.org/10.1016/j.cie.2017.12.006
18. Göhler S. M., Husung S., Howard T. J. The Translation between
Functional Requirements and Design Parameters for Robust
Design, Procedia College International pour la Recherche en
Productique (CIRP), 2016, Vol. 43, pp. 106–111.
https://doi.org/10.1016/j.procir.2016.02.028
19. Gausemeier J., Gaukstern T., Tschirner C. Systems Engineering
Management Based on a Discipline-Spanning System Model,
Procedia Computer Science, 2013, Vol. 16, pp. 303–312.
https://doi.org/10.1016/j.procs.2013.01.032
20. MacCalman A., Kwak H., McDonald M., Upton S. Capturing
Experimental Design Insights in Support of the Model-based
System Engineering Approach, Procedia Computer Science,
2015, Vol. 44, pp. 315–324.
https://doi.org/10.1016/j.procs.2015.03.030
21. Kaiser L., Bremer C., Dumitrescu R. Exhaustiveness of Systems
Structures in Model-Based Systems Engineering for
Mechatronic Systems, Procedia Technology, 2016, Vol. 26, pp.
428–435. https://doi.org/10.1016/j.protcy.2016.08.055
22. Mac Calman A., Lesinski G., Goerger S. Integrating External
Simulations Within the Model-Based Systems Engineering
Approach Using Statistical Metamodels, Procedia Computer
Science, 2016, Vol. 95, pp. 436–441.
https://doi.org/10.1016/j.procs.2016.09.309
23. Lemazurier L., Chapurlat V., Grossetête A. // An MBSE
Approach to Pass from Requirements to Functional
Architecture, International Federation of Accountants (IFAC)-
Papers OnLine, July 2017, Vol. 50, Issue 1, pp. 7260–7265.
https://doi.org/10.1016/j.ifacol.2017.08.1376
24. Clement S., Register A., Wise R. Key Enablers for Leveraging
Non-Development Items in a System, Procedia Computer
Science, 2015, Vol. 44, pp. 164–173.
https://doi.org/10.1016/j.procs.2015.03.009
25. Cohen J., Cohen P., West S. G., Aiken L. S. Applied multiple
regression/correlation analysis for the behavioral sciences.
Mahwah, New Jersey, US: Lawrence Erlbaum Associates
Publishers, Third Edition, 2003, 703 p.
https://doi.org/10.1177/019394598000200320
26. Azevedo A., Santos M. F. KDD, SEMMA and CRISP-DM: a
parallel overview, IADIS European Conference on Data
Mining, Amsterdam, the Netherlands 22–27 July 2008,
Proceedings of the IADIS European Conference on Data
Mining, 2008, pp. 182–185. [Electronic resource]. Access
mode:
https://pdfs.semanticscholar.org/7dfe/3bc6035da527deaa72007a
27cef94047a7f9.pdf







Copyright (c) 2019 Ye. V. Havrylko, O. A. Kurchenko, I. V. Tereshchenko, A. I. Tereshchenko

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