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Ruggedness testing of an analytical method for pesticide residues in potato

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Abstract

The best demonstration of the ruggedness of a method is monitoring its performance on an ongoing basis as part of the analytical quality control applied in the laboratory. However, an initial demonstration of the ruggedness is often performed as one aspect of the method validation, to give confidence that the method should perform well under normal variations in conditions in routine application. This initial ruggedness testing is typically performed using either multiple replicate analyses or application of design of experiments (DoEs) which minimizes the number of analyses, time and effort required to detect influences on the measurement results. Two DoEs were applied for ruggedness testing for a modified QuEChERS multiresidue method for the detection of pesticide residues in potato by GC–MS/MS. Seven experimental factors were studied using an eight-run Plackett–Burman design replicated three times and an augmented definitive screening design with 34 runs. The relative effectiveness of the two approaches is discussed, in terms of their statistical significance, their cost-effectiveness and the richness of information they provide on the effects of the parameters investigated and the actual robustness of the method being tested.

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References

  1. Codex Alimentarius (2010) Guidelines on good laboratory practice in pesticide residue analysis. CAC/GL 40-1993, Rome. www.fao.org. Accessed 27 Oct 2017

  2. SANTE (2017) SANTE/11813/2017: guidance document on analytical quality control and method validation procedures for pesticides residues analysis in food and feed. https://ec.europa.eu/. Accessed 27 Dec 2017

  3. International Accreditation Service (2015) Guidelines for food testing laboratories. http://www.iasonline.org. Accessed 27 Oct 2017

  4. Eurachem (2014) Magnusson B and Örnemark U (eds) Eurachem guide: the fitness for purpose of analytical methods—a laboratory guide to method validation and related topics, 2nd edn. www.eurachem.org. Accessed 27 Oct 2017

  5. Dejaegher B, Heyden YV (2007) Ruggedness and robustness testing. J Chromatogr A 1158:138–157

    Article  CAS  PubMed  Google Scholar 

  6. Hibbert DB (2012) Experimental design in chromatography: a tutorial review. J Chromatogr B 910:2–13

    Article  CAS  Google Scholar 

  7. Youden WJ, Steiner EH (1975) Statistical manual of the association of official analytical chemists. Association of Official Analytical Chemists, Wasgington, D.C., USA

    Google Scholar 

  8. Plackett RL, Burman JP (1946) The design of optimum multifactorial experiments. Biometrika 33:305–325

    Article  Google Scholar 

  9. Wu CFJ, Hamada M (2000) Experiments: planning, analysis and parameter design optimization. Wiley, New York

    Google Scholar 

  10. Montgomery DC (2012) Design and analysis of experiments, 8th edn. Wiley, New York

    Google Scholar 

  11. Myers RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology: process and product optimization using designed experiments, 4th edn. Wiley, New York

    Google Scholar 

  12. Boggia R, Borgogni C, Hysenaj V, Leardi R, Zunin P (2014) Direct GC–(EI)MS determination of fatty acid alkyl esters in olive oils. Talanta 119:60–67

    Article  CAS  PubMed  Google Scholar 

  13. Ma L, Wang L, Tang J, Yang Z (2016) Optimization of arsenic extraction in rice samples by Plackett–Burman design and response surface methodology. Food Chem 204:283–288

    Article  CAS  PubMed  Google Scholar 

  14. Karageorgou E, Samanidou V (2014) Youden test application in robustness assays during method validation. J Chromatogr A 1353:131–139

    Article  CAS  PubMed  Google Scholar 

  15. Hartmann C, Smeyers-Verbeke J, Massart DL, McDowall RD (1998) Validation of bioanalytical chromatographic methods. Pharm Biomed Anal 17:193–218

    Article  CAS  Google Scholar 

  16. Sofer G, Zabriskie DW (eds) (2000) Biopharmaceutical process validation. Marcel Dekker, New York

    Google Scholar 

  17. Jones B, Nachtsheim CJ (2011) A class of three-level designs for definitive screening in the presence of second order effects. J Qual Technol 43:1–15

    Article  Google Scholar 

  18. Erler A, de Mas N, Ramsey P, Henderson G (2013) Efficient biological process characterization by definitive screening designs: the formaldehyde treatment of a therapeutic protein as a case study. Biotech Lett 35:323–329

    Article  CAS  Google Scholar 

  19. Renzi P, Kronig C, CarloneA Eröksüz S, Berkessel A, Bella M (2014) Kinetic resolution of oxazinones: rational exploration of chemical space through the design of experiments. Chem Eur J 20:11768–11775

    Article  CAS  PubMed  Google Scholar 

  20. Olsen RE, Bartholomew CH, Enfield DB, Lawson JS, Rohbock N, Scott BS, Woodfield BF (2014) Optimizing the synthesis and properties of Al-modified anatase catalyst supports by statistical experimental design. J Porous Mater 21:827–837

    Article  CAS  Google Scholar 

  21. Libbrecht W, Deruyck F, Poelman H, Verberckmoes A, Thybaut J, De Clercq J, Van Der Voort P (2015) Optimization of soft templated mesoporous carbon synthesis using definitive screening design. Chem Eng J 259:126–134

    Article  CAS  Google Scholar 

  22. Tai M, Ly A, Leung I, Nayar G (2015) Efficient high-throughput biological process characterization: definitive screening design with the Ambr250 bioreactor system. Biotechnol Prog 31:1388–1395

    Article  CAS  PubMed  Google Scholar 

  23. Fidaleo M, Lavecchia R, Petrucci E, Zuorro A (2016) Application of a novel definitive screening design to decolorization of an azo dye on boron-doped diamond electrodes. Int J Environ Sci Technol 13:835–842

    Article  CAS  Google Scholar 

  24. Goos P (2016) Discussion of “21st century screening experiments: what, why, and how”. Qual Eng 28:111–114

    Article  Google Scholar 

  25. Patil MV (2017) Multi response simulation and optimization of gas tungsten arc welding. Appl Math Model 42:540–553

    Article  Google Scholar 

  26. Codex Alimentarius (2017) Guidelines on performance criteria for methods of analysis for the determination of pesticides residues in food and feed. CAC/GL 90-2017, Rome. www.fao.org. Accessed 27 Oct 2017

  27. European Standard (2008) EN 15662 foods of plant origin—determination of pesticide residues using GC–MS and/or LC–MS/MS following acetonitrile extraction/partitioning and cleanup by dispersive SPE-QuEChERS method

  28. SAS. JMP 12 data analysis software. SAS Institute Inc., Cary

  29. Vazquez-Alcocer A, Goos P, Schoen ED (2016) Two-level designs constructed by concatenating orthogonal arrays of strength three. Working paper, University of Antwerp, Faculty of Applied Economics (RPS-2016-011)

  30. (2016) MATLAB version 9.1. The MathWorks Inc., Natick

  31. Kutner MH, Nachtsheim C, Neter J, Li W (2005) Section 9.4 applied linear statistical models, 5th edn. McGraw-Hill, Irwin

    Google Scholar 

  32. Li X, Sudarsanam N, Frey DD (2006) Regularities in data from factorial experiments. Complexity 11:32–45

    Article  Google Scholar 

  33. Ockuly RA, Weese ML, Smucker BJ, Edwards DJ, Chang L (2017) Response surface experiments: a meta-analysis. Chemometr Intell Lab Syst 164:64–75

    Article  CAS  Google Scholar 

  34. Sector field mass spectrometry for elemental and isotopic analysis, new developments in mass spectrometry by Thomas Prohaska (editor), Johanna Irrgeher (editor), Andreas Zitek (editor), Norbert Jakubowski (editor), Simon Gaskell (editor), ISBN-10 1849733929

  35. Burns DT, Danzer K, Townshend A (2009) A tutorial discussion of the use of the terms “robust” and “rugged” and the associated characteristics of “robustness” and “ruggedness” as used in descriptions of analytical procedures. J Assoc Public Anal 37:40–60

    Google Scholar 

  36. Amadeo I, Mauro LV, Ortí E (2011) Determination of robustness and optimal work conditions for a purification process of a therapeutic recombinant protein using response surface methodology. Biotechnol Prog 27:724–732

    Article  CAS  PubMed  Google Scholar 

  37. Boqué R, Maroto A, Riu J, Rius FX (2002) Validation of analytical methods. Grasas Aceites 53(1):128–143

    Google Scholar 

  38. Heyden YV, Nijhuis A, Smeyer S, Verbeke J, Vandeginste BGM, Massart DL (2001) Guidance for robustness/ruggedness tests in method validation. J Pharm Biomed Anal 24:723–753

    Article  Google Scholar 

  39. Stefanelli P, Generali T, Girolimetti S, Barbin D (2013) Internal quality control as a tool for planning a robustness study regarding a multiresidue method for pesticides found in olive oil. Accred Qual Assur 18:313–322

    Article  CAS  Google Scholar 

  40. Konieczka P (2007) The role of and place of method validation in the quality assurance and quality control (QA/QC) system. Crit Rev Anal Chem 37(3):173–190

    Article  CAS  Google Scholar 

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Correspondence to Britt Maestroni.

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Maestroni, B., Vazquez, A.R., Avossa, V. et al. Ruggedness testing of an analytical method for pesticide residues in potato. Accred Qual Assur 23, 303–316 (2018). https://doi.org/10.1007/s00769-018-1335-7

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  • DOI: https://doi.org/10.1007/s00769-018-1335-7

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