Sampling and Sample Preparation

Part of the Food Analysis book series (FSTS)


Quality attributes in food products, raw materials, or ingredients are measurable characteristics that need monitoring to ensure that specifications are met. Some quality attributes can be measured online by using specially designed sensors and results obtained in real time (e.g., color of vegetable oil in an oil extraction plant). However, in most cases quality attributes are measured on small portions of material that are taken periodically from continuous processes or on a certain number of small portions taken from a lot. The small portions taken for analysis are referred to as samples, and the entire lot or the entire production for a certain period of time, in the case of continuous processes, is called a population. The process of taking samples from a population is called sampling. If the procedure is done correctly, the measurable characteristics obtained for the samples become a very accurate estimation of the population.


Sampling Plan Tomato Juice Nutrition Label Nonprobability Sampling Meat Mincer 
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5.7 Acknowledgements

The author of this chapter wishes to acknowledge Drs. Andrew Proctor and Jean-François Meullenet, who wrote this chapter for the third edition of the book and offered the use of their chapter content for this fourth edition.

5.8 References

  1. 1.
    Puri SC, Ennis D, Mullen K (1979) Statistical quality control for food and agricultural scientists. G.K. Hall and Co., Boston, MAGoogle Scholar
  2. 2.
    Harris DC (1999) Quantitative chemical analysis, 5th edn. W.H. Freeman and Co., New YorkGoogle Scholar
  3. 3.
    Miller JC (1988) Basic statistical methods for analytical chemistry. I. Statistics of repeated measurements. A review. Analyst 113:1351–1355Google Scholar
  4. 4.
    Horwitz W (1988) Sampling and preparation of samples for chemical examination. J Assoc Off Anal Chem 71:241–245Google Scholar
  5. 5.
    IUPAC (1997) Compendium of chemical terminology, 2nd edn (the “Gold Book”). Compiled by McNaught AD, Wilkinson A. Blackwell Scientific, Oxford. XML on-line corrected version: (2006) created by Nic M, Jirat J, Kosata B; updates compiled by Jenkins A. ISBN 0–9678550–9–8. doi:10.1351/goldbook
  6. 6.
    Weiers RM (2007) Introduction to business statistics, 6th edn. South-Western College, Cincinnati, OHGoogle Scholar
  7. 7.
    NIST/SEMATECH (2009) e-Handbook of statistical methods, chapter 6: process or product monitoring and control.
  8. 8.
    AOAC International (2007) Official methods of analysis, 18th edn, 2005; Current through revision 2, 2007 (On-line). AOAC International, Gaithersburg, MDGoogle Scholar
  9. 9.
    Anonymous (2009) Code of federal regulations. Title 21. US Government Printing Office, Washington, DCGoogle Scholar
  10. 10.
    Baker WL, Gehrke CW, Krause GF (1967) Mechanism of sampler bias. J Assoc Off Anal Chem 50:407–413Google Scholar
  11. 11.
    Anonymous (2009) Code of federal regulations. 21 CFR 101.9 (g), 9 CFR 317.309 (h), 9 CFR 381.409 (h). US Government Printing Office, Washington, DCGoogle Scholar
  12. 12.
    Pomeranz Y, Meloan CE (1994) Food analysis: theory and practice, 3rd edn. Chapman & Hall, New YorkGoogle Scholar
  13. 13.
    Cubadda F, Baldini M, Carcea M, Pasqui LA, Raggi A, Stacchini P (2001) Influence of laboratory homogenization procedures on trace element content of food samples: an ICP-MS study on soft and duram wheat. Food Addit Contam 18:778–787CrossRefGoogle Scholar
  14. 14.
    Kenkel JV (2003) Analytical chemistry for technicians, 3rd edn. CRC, Boca Raton, FLGoogle Scholar
  15. 15.
    Jordan JR (1999) Particle size analysis. Inside Lab Manage 3(7):25–28Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Food ScienceUniversity of ArkansasFayettevilleUSA

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