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Two-stage application of the optimised uncertainty method: a practical assessment
Jennifer A Lyn1, Michael H Ramsey, Andrew P Damant
1Centre for Environmental Research, School of Life Sciences, University of Sussex, Falmer, Brighton, UKBN1 9QJ.
The Analyst
|August 13, 2005
Summary
The optimised uncertainty method assesses if sampling is fit for purpose. Applying it to butter quality testing identified cost savings, though sampling theory limitations require more data for routine use.
Area of Science:
- Food science and technology
- Analytical chemistry
- Quality control
Background:
- Routine sampling and analytical procedures require robust uncertainty estimation.
- The optimised uncertainty (OU) method provides a framework for assessing fitness for purpose based on financial loss.
- Sampling theory offers predictive capabilities for optimizing sampling protocols.
Purpose of the Study:
- To iteratively apply the OU methodology to a case study of wholesale butter quality indicators.
- To assess the fitness for purpose of current sampling protocols for moisture, fat, SNF, PV, and FFA.
- To explore the application of Gy's sampling theory for optimizing sampling uncertainty.
Main Methods:
- Iterative application of the optimised uncertainty (OU) method.
- Case study involving wholesale butter and five quality indicators (moisture, fat, SNF, PV, FFA).
- Utilisation of Gy's sampling theory to revise sampling protocols and predict uncertainty reduction.
Main Results:
- Sub-optimal sampling uncertainty (s(samp)) identified for moisture and peroxide value (PV) with 3-fold composite samples.
- Predicted cost savings exceeding £2000 per 20-tonne batch by increasing sample mass.
- Achieved reduction in s(samp) was limited by practical sampling theory constraints.
Conclusions:
- The OU methodology effectively identifies weaknesses in routine protocols and assesses fitness for purpose.
- Optimising sampling uncertainty can lead to significant financial savings in quality control.
- Successful routine application of sampling theory necessitates comprehensive prior knowledge of the sampling target.
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