An appropriate and systematized procedure for validating qualitative methods: Its application in the detection of sulfonamide residues in raw milk

Gondim, Carina de Souza; Coelho, Otávio Augusto Mazzoni; Alvarenga, Ronália Leite; Junqueira, Roberto Gonçalves; Souza, Scheilla Vitorino Carvalho de

doi:10.1016/j.aca.2014.04.050

Cited by 19 publications

(37 citation statements)

References 39 publications

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“…This is in accordance with the implicit statement consensus in the literature that measurements (e.g. area and area ratios), including those made on an LC–MS/MS instrument, can be approximated by a normal distribution 14–16,21 …”

Section: Methodssupporting

confidence: 88%

“…The validation parameters [false negative rate (FNR), false positive rate (FPR), reliability rate (RLR), selectivity rate (SLR) and sensitivity rate (SNR)] are calculated as follows: 14,15,17

italicFNR = \frac{FN}{italicFN + italicTP} \times 100,

italicFPR = \frac{FP}{italicFP + italicTN} \times 100,

italicRLR = \frac{italicTP + italicTN}{n} \times 100 = 100 - italicFPR - italicFNR,

italicSLR = \frac{TN}{italicTN + italicFP} \times 100,

italicSNR = \frac{TP}{italicTP + italicFN} \times 100,

where TN is the number of true negative results, TP is the number of true positive results, FN is the number of false negative results, FP is the number of false positive results and n is the total number of results.…”

Section: Methodsmentioning

confidence: 99%

“…To answer such questions, the behaviour of the response variable (e.g., area ratio, luminescence) in relation to the encoded, binary outcome must be understood. This subject is touched upon sparingly in the literature, 14–16 where diverse validation procedures are suggested.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Qualitative threshold method validation and uncertainty evaluation: A theoretical framework and application to a 40 analytes liquid chromatography–tandem mass spectrometry method

Lemyre

Desharnais

Laquerre³

et al. 2020

Drug Testing and Analysis

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Qualitative methods hold an important place in drug testing, filling central needs in screening and analyses, among others, linked to per se legislation. Nevertheless, the bioanalytical method validation guidelines do not discuss this type of method or describe method validation procedures ill‐adapted to qualitative methods. The output of qualitative methods are typically categorical, binary results, such as presence/absence or above cut‐off/below cut‐off. As the goal of any method validation is to demonstrate fitness for use under production conditions, qualitative validation guidelines should evaluate performance based on discrete, binary results instead of the continuous measurements obtained from the instrument (e.g. area). A tentative validation guideline for threshold qualitative methods was developed by in silico modelling of measurements and derived binary results. This preliminary guideline was applied to a liquid chromatography–tandem mass spectrometry method for 40 analytes, each with a defined threshold concentration. Validation parameters calculated from the analysis of 30 samples spiked above and below the threshold concentration (false negative rate, false positive rate, selectivity rate, sensitivity rate and reliability rate) showed a surprisingly high failure rate. Overall, 13 out of the 40 analytes were not considered validated. A subsequent examination found that this was attributable to an appreciable shift in the standard deviation of the area ratio on a day‐to‐day basis, a previously undescribed and unaccounted‐for behaviour in the qualitative threshold method validation literature. Consequently, the developed guideline was modified and used to validate a qualitative threshold method, based on the binary results for performance evaluation and incorporating measurement uncertainty.

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Section: Methodssupporting

confidence: 88%

“…The validation parameters [false negative rate (FNR), false positive rate (FPR), reliability rate (RLR), selectivity rate (SLR) and sensitivity rate (SNR)] are calculated as follows: 14,15,17

italicFNR = \frac{FN}{italicFN + italicTP} \times 100,

italicFPR = \frac{FP}{italicFP + italicTN} \times 100,

italicRLR = \frac{italicTP + italicTN}{n} \times 100 = 100 - italicFPR - italicFNR,

italicSLR = \frac{TN}{italicTN + italicFP} \times 100,

italicSNR = \frac{TP}{italicTP + italicFN} \times 100,

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Qualitative threshold method validation and uncertainty evaluation: A theoretical framework and application to a 40 analytes liquid chromatography–tandem mass spectrometry method

Lemyre

Desharnais

Laquerre³

et al. 2020

Drug Testing and Analysis

View full text Add to dashboard Cite

show abstract

“…A C C E P T E D ACCEPTED MANUSCRIPT Table 6 Adapted from [37] with permission. Performance parameters obtained for each adulterant, expressed in %.…”

Section: A N U S C R I P Tmentioning

confidence: 98%

“…Other examples of univariate methods based on visual and instrumental signals can be found elsewhere [2,[37][38][39].…”

Section: Cases Studymentioning

confidence: 99%

A tutorial on the validation of qualitative methods: From the univariate to the multivariate approach

López

Callao

Ruisánchez

2015

Analytica Chimica Acta

101

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Assessing limits of detection in qualitative methods: A simple implementation of logistic regression in a web‐based R Shiny application environment and its potential in toxicology and doping control

Garrido

Cavalcanti

Gomes

et al. 2022

Drug Testing and Analysis

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The estimation of limits of detection (LOD) for solely qualitative methods in analytical chemistry may prove challenging because all the approaches with which chemists are familiar require some type of numeric data input. The best model to describe the binary response in these methods (detected/not detected) is a logistic model; however, these models are not easily handled by most of the laboratories and generally demand expensive statistical software packages. In this work, the advantages of applying this approach are discussed and its implementation using commercial spreadsheet software is demonstrated. A free online application based on the R environment using shinyapps was developed and its application was validated and discussed with a dataset of 57 different target compounds analyzed in urine according to the requirements of the World Anti‐Doping Agency (WADA). This tool allows free, extremely quick, and easy determinations of LOD in qualitative analyses as well as the determination of the probabilities of detection in any given concentration.

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An appropriate and systematized procedure for validating qualitative methods: Its application in the detection of sulfonamide residues in raw milk

Cited by 19 publications

References 39 publications

Qualitative threshold method validation and uncertainty evaluation: A theoretical framework and application to a 40 analytes liquid chromatography–tandem mass spectrometry method

Qualitative threshold method validation and uncertainty evaluation: A theoretical framework and application to a 40 analytes liquid chromatography–tandem mass spectrometry method

A tutorial on the validation of qualitative methods: From the univariate to the multivariate approach

Assessing limits of detection in qualitative methods: A simple implementation of logistic regression in a web‐based R Shiny application environment and its potential in toxicology and doping control

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