Abstract
The study of food analysis involves a considerable amount of time learning principles, methods, and instrument operations and perfecting various techniques. These areas are extremely important, but much of our effort would be for naught if there were no ways to evaluate the data obtained from the various analytical assays. Having a good understanding of the data and how to interpret the data are critical to good decision making, whether in the food industry or in a research laboratory. This chapter focuses on statistical methods to evaluate the data obtained from analytical techniques. The chapter primarily covers how to evaluate replicate analyses of the same sample for accuracy and precision, but attention also is given to the determination of best line fits for standard curve data. A section of the chapter describes sensitivity and limit of detection as related to various analytical methods and regulatory agency policies. Additional information includes the proper use of significant figures, rules for rounding off numbers, and use of various test to reject grossly aberrant individual values.
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Change history
23 July 2019
An error in the production process unfortunately led to publication of the book before incorporating the below corrections. This has now been corrected and approved by the Editor.
References
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Acknowledgment
The author wishes to thank Ryan Deeter for his contributions in preparation of the content on quality control measures.
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Smith, J.S. (2017). Evaluation of Analytical Data. In: Nielsen, S.S. (eds) Food Analysis. Food Science Text Series. Springer, Cham. https://doi.org/10.1007/978-3-319-45776-5_4
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DOI: https://doi.org/10.1007/978-3-319-45776-5_4
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