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Contaminant Detection in the Visual Inspection of Seed Samples

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  • C. G. G. Aitken
  • J. Shaw
  • M. Talbot

Abstract

The quality of a seed lot is determined, in part, by the amount of contaminants in a sample. One way of automating the process of identifying contaminants is to take shape and size measurements of each item in the sample. The process of detecting contaminants from such data may be viewed as a multivariate statistical outlier problem in which contaminants are considered as outliers in a sample of normal seeds. Pettit has developed Bayesian diagnostics for multivariate normal distributions with multivariate normal prior distributions; an extension is described here in which the prior distribution for the mean is other than normal and is represented by a kernel density estimate. The performances of the normal distribution and extended methods are compared in an application to the seed testing problem.

Suggested Citation

  • C. G. G. Aitken & J. Shaw & M. Talbot, 1995. "Contaminant Detection in the Visual Inspection of Seed Samples," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(4), pages 431-440, December.
    • Handle: RePEc:bla:jorssc:v:44:y:1995:i:4:p:431-440
    DOI: 10.2307/2986136
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