Accreditation and Quality Assurance

, Volume 23, Issue 5, pp 303–316 | Cite as

Ruggedness testing of an analytical method for pesticide residues in potato

  • Britt MaestroniEmail author
  • Alan R. Vazquez
  • Valeria Avossa
  • Peter Goos
  • Veronica Cesio
  • Horacio Heinzen
  • Joerg Riener
  • Andrew Cannavan
Practitioner's Report


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.


Design of experiments Plackett–Burman design Definitive screening design Method validation Ruggedness and robustness testing 

Supplementary material

769_2018_1335_MOESM1_ESM.docx (82 kb)
Supplementary material 1 (DOCX 81 kb)


  1. 1.
    Codex Alimentarius (2010) Guidelines on good laboratory practice in pesticide residue analysis. CAC/GL 40-1993, Rome. Accessed 27 Oct 2017
  2. 2.
    SANTE (2017) SANTE/11813/2017: guidance document on analytical quality control and method validation procedures for pesticides residues analysis in food and feed. Accessed 27 Dec 2017
  3. 3.
    International Accreditation Service (2015) Guidelines for food testing laboratories. Accessed 27 Oct 2017
  4. 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. Accessed 27 Oct 2017
  5. 5.
    Dejaegher B, Heyden YV (2007) Ruggedness and robustness testing. J Chromatogr A 1158:138–157CrossRefPubMedGoogle Scholar
  6. 6.
    Hibbert DB (2012) Experimental design in chromatography: a tutorial review. J Chromatogr B 910:2–13CrossRefGoogle Scholar
  7. 7.
    Youden WJ, Steiner EH (1975) Statistical manual of the association of official analytical chemists. Association of Official Analytical Chemists, Wasgington, D.C., USAGoogle Scholar
  8. 8.
    Plackett RL, Burman JP (1946) The design of optimum multifactorial experiments. Biometrika 33:305–325CrossRefGoogle Scholar
  9. 9.
    Wu CFJ, Hamada M (2000) Experiments: planning, analysis and parameter design optimization. Wiley, New YorkGoogle Scholar
  10. 10.
    Montgomery DC (2012) Design and analysis of experiments, 8th edn. Wiley, New YorkGoogle Scholar
  11. 11.
    Myers RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology: process and product optimization using designed experiments, 4th edn. Wiley, New YorkGoogle Scholar
  12. 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 119:60–67CrossRefPubMedGoogle Scholar
  13. 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 204:283–288CrossRefPubMedGoogle Scholar
  14. 14.
    Karageorgou E, Samanidou V (2014) Youden test application in robustness assays during method validation. J Chromatogr A 1353:131–139CrossRefPubMedGoogle Scholar
  15. 15.
    Hartmann C, Smeyers-Verbeke J, Massart DL, McDowall RD (1998) Validation of bioanalytical chromatographic methods. Pharm Biomed Anal 17:193–218CrossRefGoogle Scholar
  16. 16.
    Sofer G, Zabriskie DW (eds) (2000) Biopharmaceutical process validation. Marcel Dekker, New YorkGoogle Scholar
  17. 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 43:1–15CrossRefGoogle Scholar
  18. 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 35:323–329CrossRefGoogle Scholar
  19. 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 20:11768–11775CrossRefPubMedGoogle Scholar
  20. 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 21:827–837CrossRefGoogle Scholar
  21. 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 259:126–134CrossRefGoogle Scholar
  22. 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 31:1388–1395CrossRefPubMedGoogle Scholar
  23. 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 13:835–842CrossRefGoogle Scholar
  24. 24.
    Goos P (2016) Discussion of “21st century screening experiments: what, why, and how”. Qual Eng 28:111–114CrossRefGoogle Scholar
  25. 25.
    Patil MV (2017) Multi response simulation and optimization of gas tungsten arc welding. Appl Math Model 42:540–553CrossRefGoogle Scholar
  26. 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. Accessed 27 Oct 2017
  27. 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 methodGoogle Scholar
  28. 28.
    SAS. JMP 12 data analysis software. SAS Institute Inc., CaryGoogle Scholar
  29. 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)Google Scholar
  30. 30.
    (2016) MATLAB version 9.1. The MathWorks Inc., NatickGoogle Scholar
  31. 31.
    Kutner MH, Nachtsheim C, Neter J, Li W (2005) Section 9.4 applied linear statistical models, 5th edn. McGraw-Hill, IrwinGoogle Scholar
  32. 32.
    Li X, Sudarsanam N, Frey DD (2006) Regularities in data from factorial experiments. Complexity 11:32–45CrossRefGoogle Scholar
  33. 33.
    Ockuly RA, Weese ML, Smucker BJ, Edwards DJ, Chang L (2017) Response surface experiments: a meta-analysis. Chemometr Intell Lab Syst 164:64–75CrossRefGoogle Scholar
  34. 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 1849733929Google Scholar
  35. 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 37:40–60Google Scholar
  36. 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 27:724–732CrossRefPubMedGoogle Scholar
  37. 37.
    Boqué R, Maroto A, Riu J, Rius FX (2002) Validation of analytical methods. Grasas Aceites 53(1):128–143Google Scholar
  38. 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 24:723–753CrossRefGoogle Scholar
  39. 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 18:313–322CrossRefGoogle Scholar
  40. 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):173–190CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Britt Maestroni
    • 1
    Email author
  • Alan R. Vazquez
    • 2
  • Valeria Avossa
    • 1
  • Peter Goos
    • 2
    • 3
  • Veronica Cesio
    • 4
  • Horacio Heinzen
    • 4
  • Joerg Riener
    • 5
  • Andrew Cannavan
    • 1
  1. 1.Food and Environmental Protection Laboratory, Joint FAO/IAEA Division of Nuclear Applications in Food and Agriculture, Department of Nuclear Sciences and ApplicationsInternational Atomic Energy AgencyViennaAustria
  2. 2.University of AntwerpAntwerpBelgium
  3. 3.KU LeuvenLouvainBelgium
  4. 4.GACT, Faculty of ChemistryUdelarMontevideoUruguay
  5. 5.Agilent TechnologiesSanta ClaraUSA

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