Distribution of Aflatoxin in Pistachios. 7. Sequential Sampling

Schatzki, Thomas F.

doi:10.1021/jf000342o

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Studies have demonstrated that aflatoxins and other mycotoxins are seldom uniformly distributed throughout most food and feed commodities, their occurrence being uneven and sporadic. It is estimated that only very few nuts are responsible for the overall aflatoxin concentration of a lot (as few as one nut in 10 5 ) 22 and the actual level of contamination in these nuts may span up to eight orders of magnitude. Whitaker et al note that the extreme range in aflatoxin concentrations among individual peanut kernels in a contaminated lot and that variation among replicated samples tended to be large.…”

Section: Case Study Of Aflatoxins In Pistachio Nutsmentioning

confidence: 99%

Empirical versus modelling approaches to the estimation of measurement uncertainty caused by primary sampling

Lyn

Damant

Wood

2007

Analyst

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Measurement uncertainty is a vital issue within analytical science. There are strong arguments that primary sampling should be considered the first and perhaps the most influential step in the measurement process. Increasingly, analytical laboratories are required to report measurement results to clients together with estimates of the uncertainty. Furthermore, these estimates can be used when pursuing regulation enforcement to decide whether a measured analyte concentration is above a threshold value. With its recognised importance in analytical measurement, the question arises of 'what is the most appropriate method to estimate the measurement uncertainty?'. Two broad methods for uncertainty estimation are identified, the modelling method and the empirical method. In modelling, the estimation of uncertainty involves the identification, quantification and summation (as variances) of each potential source of uncertainty. This approach has been applied to purely analytical systems, but becomes increasingly problematic in identifying all of such sources when it is applied to primary sampling. Applications of this methodology to sampling often utilise long-established theoretical models of sampling and adopt the assumption that a 'correct' sampling protocol will ensure a representative sample. The empirical approach to uncertainty estimation involves replicated measurements from either inter-organisational trials and/or internal method validation and quality control. A more simple method involves duplicating sampling and analysis, by one organisation, for a small proportion of the total number of samples. This has proven to be a suitable alternative to these often expensive and time-consuming trials, in routine surveillance and one-off surveys, especially where heterogeneity is the main source of uncertainty. A case study of aflatoxins in pistachio nuts is used to broadly demonstrate the strengths and weakness of the two methods of uncertainty estimation. The estimate of sampling uncertainty made using the modelling approach (136%, at 68% confidence) is six times larger than that found using the empirical approach (22.5%). The difficulty in establishing reliable estimates for the input variable for the modelling approach is thought to be the main cause of the discrepancy. The empirical approach to uncertainty estimation, with the automatic inclusion of sampling within the uncertainty statement, is recognised as generally the most practical procedure, providing the more reliable estimates. The modelling approach is also shown to have a useful role, especially in choosing strategies to change the sampling uncertainty, when required.

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Section: Case Study Of Aflatoxins In Pistachio Nutsmentioning

confidence: 99%

Empirical versus modelling approaches to the estimation of measurement uncertainty caused by primary sampling

Lyn

Damant

Wood

2007

Analyst

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show abstract

“…B. in einer Partie Nüsse, Gewürze oder auch Trockenfrüchte kann sehr heterogen sein. Es kommen punktuell sehr hohe und an anderer Stelle sehr geringe bis hin zu nicht nachweisbaren Belastungen vor (Schatzki, 2000;Whitaker et al, 1993Whitaker et al, und 2005, wodurch die Probenahme einen entscheidenden Beitrag bei der Identifizierung hoher Mykotoxinbelastungen leistet und daher als separater Einzelpunkt bei der Gegenüberstellung der Analyseverfahren Berücksichtigung findet. (Food and Agriculture Organization, 2004).…”

Section: Introductionunclassified

Analytische Methoden für die Kontrolle von Lebens- und Futtermitteln auf Mykotoxine

Biselli

2006

J. Verbr. Lebensm.

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Novel sampling methods for the analysis of mycotoxins and the combination with spectroscopic methods for the rapid evaluation of deoxynivalenol contamination

Stroka¹,

Spanjer²,

Buechler³

et al. 2004

Toxicology Letters

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Distribution of Aflatoxin in Pistachios. 7. Sequential Sampling

Cited by 5 publications

References 5 publications

Empirical versus modelling approaches to the estimation of measurement uncertainty caused by primary sampling

Empirical versus modelling approaches to the estimation of measurement uncertainty caused by primary sampling

Analytische Methoden für die Kontrolle von Lebens- und Futtermitteln auf Mykotoxine

Novel sampling methods for the analysis of mycotoxins and the combination with spectroscopic methods for the rapid evaluation of deoxynivalenol contamination

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