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


Zhang, X and Nansen, C and Aryamanesh, N and Yan, G and Boussaid, F, Importance of spatial and spectral data reduction in the detection of internal defects in food products, Applied Spectroscopy, 69, (4) pp. 473-480. ISSN 0003-7028 (2015) [Refereed Article]

Copyright Statement

Copyright 2015 Society for Applied Spectroscopy

DOI: doi:10.1366/14-07672


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 resolution and classification accuracy, while spectral binning equivalent to up to 98% data reduction had negligible effect on classification accuracies. This study supports the potential use of reflection-based technologies in the quality control of food products with internal defects, and it highlights that spatial and spectral data reductions can (1) improve classification accuracies, (2) vastly decrease computer constraints, and (3) reduce analytical concerns associated with classifications of large and high-dimensional datasets.

Item Details

Item Type:Refereed Article
Keywords:machine vision, spatial resolution, spectral resolution, pea weevil, field pea, reflection data, hyperspectral imaging
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Crop and pasture production
Research Field:Crop and pasture improvement (incl. selection and breeding)
Objective Division:Plant Production and Plant Primary Products
Objective Group:Horticultural crops
Objective Field:Field grown vegetable crops
UTAS Author:Zhang, X (Mr Xuechen Zhang)
ID Code:108675
Year Published:2015
Web of Science® Times Cited:12
Deposited By:Tasmanian Institute of Agriculture
Deposited On:2016-05-02
Last Modified:2016-08-04

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