Importance of spatial and spectral data reduction in the detection of internal defects in food products

Applied Spectroscopy
Xuechen ZhangFarid Boussaid

Abstract

Despite the importance of data reduction as part of the processing of reflection-based classifications, this study represents one of the first in which the effects of both spatial and spectral data reductions on classification accuracies are quantified. Furthermore, the effects of approaches to data reduction were quantified for two separate classification methods, linear discriminant analysis (LDA) and support vector machine (SVM). As the model dataset, reflection data were acquired using a hyperspectral camera in 230 spectral channels from 401 to 879 nm (spectral resolution of 2.1 nm) from field pea (Pisum sativum) samples with and without internal pea weevil (Bruchus pisorum) infestation. We deployed five levels of spatial data reduction (binning) and eight levels of spectral data reduction (40 datasets). Forward stepwise LDA was used to select and include only spectral channels contributing the most to the separation of pixels from non-infested and infested field peas. Classification accuracies obtained with LDA and SVM were based on the classification of independent validation datasets. Overall, SVMs had significantly higher classification accuracies than LDAs (P < 0.01). There was a negative association between pixel reso...Continue Reading

References

Jan 27, 2004·Journal of Chemical Information and Computer Sciences·Douglas M Hawkins
Feb 7, 2008·IEEE Transactions on Neural Networks·V N Vapnik
Jun 12, 2010·Applied Spectroscopy·Christian NansenRand Swanson
Oct 29, 2013·Applied Spectroscopy·Christian NansenJose Roberto Parra

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Citations

Mar 18, 2016·Annual Review of Entomology·Christian Nansen, Norman Elliott
Oct 22, 2016·International Journal of Legal Medicine·Sasha C VossChristian Nansen
Apr 11, 2019·Journal of the Royal Society, Interface·Luigi SeddaMartin J Donnelly
Dec 15, 2015·Pest Management Science·Christian Nansen

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