Six sigma Quality Metrics to evaluate Selenium, Zinc and Iron data

Abstract

Identifying the most signification sources of artificial differences in clinical and food data quality is essential for those involved, in estimating and comparing nutrient intake based on data from different sources.In this work six sigma methodology and metrics are applied to food composition data and clinical data to evaluate selenium, zinc and iron data quality. Trace elements in fish, milk and in serum were plotted in method device charts using maximum tolerances applicable to food composition organizations with acceptance criteria defined by USDA and Clinical Laboratory Improvement Amendments requirements which have been tabulated by the USA Federal Government.The approach was applied directly on laboratory data obtained for determination of selenium zinc and iron content in fish milk and serum. The Measurement Device Chart was constructed by representing the allowable inaccuracy and allowable imprecision of each programme. With the same methodology and using the definition of separate quality rules (tolerance limits) that are accepted in food and in clinical analysis, it was possible to compare and measure the quality of both types of data.This information could enable users of food data to identify values that have acceptable quality for use in evaluation of relationships between food intake and chronic diseases, for example in multicenter epidemiological studies.Grant Funding Source: EuroFIR