Statistical models for food authenticity
Citation:
Deirdre Ann Toher, 'Statistical models for food authenticity', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2009, pp 141Download Item:
Abstract:
The authentication of food samples pose a particular problem for regulators. The routine testing of premium food products, most likely to be subject to manipulation for commercial gain, is only feasible if the testing method does not damage the product. Near Infrared (NIR) spectroscopy is one such method that is both fast and non-invasive. However, unlike other spectroscopic methods, peaks in the resulting NIR curves are at imprecise locations, requiring further statistical analysis if it is to be used for the classification of samples.
Three NIR datasets are examined in this thesis - two are related to the identification of adulterated samples, the third is a study on the identification of types of meats. Other commonly available, non-NIR, datasets are used for illustrative purposes.
Author: Toher, Deirdre Ann
Advisor:
Murphy, BrendanDowney, Gerard
Qualification name:
Doctor of Philosophy (Ph.D.)Publisher:
Trinity College (Dublin, Ireland). School of Computer Science & StatisticsNote:
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Full text availableKeywords:
Statistics, Ph.D., Ph.D. Trinity College DublinMetadata
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