Scenarios of human errors and their quantification in multi-residue analysis of pesticides in fruits and vegetables

Kuselman, Ilya; Goldshlag, Paulina; Pennecchi, Francesca

doi:10.1007/s00769-014-1071-6

Cited by 7 publications

(13 citation statements)

References 11 publications

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“…The influence of a judgment change on the score value increases with decreasing I [39]. Thus, for I = 36 scenarios, as in Annex A, Examples 1 and 3, changing p i = 3 ↔ 9 leads to a 1.9 % shift in the P * value.…”

Section: Variabilitymentioning

confidence: 90%

“…An analyte may not be included in the local list of monitored pesticides (target analytes), as it happened with isofenphos-methyl, unauthorized in Europe, but detected in Germany in sweet peppers from Spain [39]. Attempts at identification of such an analyte using the local pesticide database may lead to a rule-based mistake.…”

Section: A-2-2-2 Rule-based Mistakes K =mentioning

confidence: 99%

“…However, an analyst may be interested to know which step m is less protected from errors, with the intent to improve it. A score m q * (%) of the quality system effectiveness at step m of the chemical analysis was developed for this purpose [39]:…”

Section: Effectivenessmentioning

confidence: 99%

“…Two other GC systems, equipped with flame photometric (FPD) and halogen selective (XSD) detectors, respectively, are used to verify the results obtained by GC/MS screening, and also to detect those analytes that exhibit a better response to either XSD or FPD than can be achieved with MS in full scan mode. LC-amenable pesticides are determined by combining an ultra-performance LC system with an advanced tandem quadrupole MS system, operated in multiple reaction monitoring mode with electrospray ionization [39].…”

Section: A-2-1 Introduction and Main Steps Of The Analysismentioning

confidence: 99%

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IUPAC/CITAC Guide: Classification, modeling and quantification of human errors in a chemical analytical laboratory (IUPAC Technical Report)

Kuselman¹,

Pennecchi

2016

Pure and Applied Chemistry

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Abstract:The classification, modeling, and quantification of human errors in routine chemical analysis are described. Classifications include commission errors (mistakes and violations) and omission errors (lapses and slips) in different scenarios at different steps of the chemical analysis. A Swiss cheese model is used to characterize error interaction with a laboratory quality system. The quantification of human errors in chemical analysis, based on expert judgments, i.e. on the expert(s) knowledge and experience, is applied. A Monte Carlo simulation of the expert judgments was used to determine the distributions of the error quantification scores (scores of likelihood and severity, and scores of effectiveness of a laboratory quality system against the errors). Residual risk of human error after the error reduction by the laboratory quality system and consequences of this risk for quality and measurement uncertainty of chemical analytical results are discussed. Examples are provided using expert judgments on human errors in pH measurement of groundwater, multiresidue analysis of pesticides in fruits and vegetables, and elemental analysis of geological samples by inductively coupled plasma mass spectrometry.

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Section: Variabilitymentioning

confidence: 90%

Section: A-2-2-2 Rule-based Mistakes K =mentioning

confidence: 99%

Section: Effectivenessmentioning

confidence: 99%

Section: A-2-1 Introduction and Main Steps Of The Analysismentioning

confidence: 99%

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IUPAC/CITAC Guide: Classification, modeling and quantification of human errors in a chemical analytical laboratory (IUPAC Technical Report)

Kuselman¹,

Pennecchi

2016

Pure and Applied Chemistry

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“…The HOS approach was initially used for the analysis and planning of systems for preventing terrorist and criminal attacks. Kuselman et al ., however, employed it for the prevention of HEs in analytical chemistry, and here, it is used to translate the desired improvement in HEs into the relative importance of prevention strategies. We also use the mean square error (MSE) method to support selection of vital prevention strategies.…”

Section: Introductionmentioning

confidence: 99%

Matrix Approach to Analysis of Human Errors and their Prevention by Quality Engineering and Managerial Tools

Bashkansky

Dror

2015

Qual. Reliab. Engng. Int.

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We apply well‐known quality engineering matrix techniques such as quality function deployment; Teoriya Resheniya Izobretatelskikh Zadatch (TRIZ); and failure mode, effects, and criticality analysis for characterizing, mapping, and preventing human error (or, at least, reducing damage caused by errors). Human errors (‘WHATs’, in the language of quality function deployment) are classified according to 10 characteristics, while 20 typical types (or protective layers)—‘HOWs’—in quality assurance systems are proposed for preventing/stopping/minimizing to some extent damage caused by the error. During the analysis of a specific system, any error is estimated according to its likelihood and severity, and every protective layer receives a score according to its effectiveness in preventing errors. Synergy or antagonism between protective layers may also be taken into account when calculating the effectiveness. The approach facilitates evaluation and comparison of the effectiveness of different quality assurance systems dealing with human errors. The authors emphasize the need to create a ‘recipe book’ based on a historical database, which will enable, after characterizing the potential human errors according to the 10 criteria mentioned earlier, application of the optimal prevention efforts. The proposed approach is illustrated by an example of product delivery errors analysis. Copyright © 2015 John Wiley & Sons, Ltd.

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The Impact of Human Errors on the Estimation of Uncertainty of Measurements in Water Monitoring

Kmiecik¹

2017

Advances in Human Error, Reliability, Resilience, and Performance

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Scenarios of human errors and their quantification in multi-residue analysis of pesticides in fruits and vegetables

Cited by 7 publications

References 11 publications

IUPAC/CITAC Guide: Classification, modeling and quantification of human errors in a chemical analytical laboratory (IUPAC Technical Report)

IUPAC/CITAC Guide: Classification, modeling and quantification of human errors in a chemical analytical laboratory (IUPAC Technical Report)

Matrix Approach to Analysis of Human Errors and their Prevention by Quality Engineering and Managerial Tools

The Impact of Human Errors on the Estimation of Uncertainty of Measurements in Water Monitoring

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