Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 156, Issue -, Pages 241-248Publisher
ELSEVIER
DOI: 10.1016/j.chemolab.2016.05.005
Keywords
Hyperspectral imaging; NIR; NPLS-DA; Variable selection; Permutation test
Categories
Funding
- Spanish Ministry of Science and Innovation [DPI2011-28112-C04-02, DPI2014-55276-C05-1R]
- INIA [RTA2012-00062-C04-01]
- European FEDER funds
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In this work an N-way partial least squares regression discriminant analysis (NPLS-DA) methodology is developed to detect symptoms of disease caused by Penicillium digitatum in citrus fruits (green mould) using visible/near infrared (VIS/NIR) hyperspectral images. To build the discriminant model a set of oranges and mandarins was infected by the fungus and another set was infiltrated just with water for control purposes. A double cross-validation strategy is used to validate the discriminant models. Finally, permutation testing is used to select a few bands offering the best correct classification rates in the validation set. The discriminant models developed here can be potentially implemented in a fruit packinghouse to detect infected citrus fruits at their arrival from the field with affordable multispectral (3-5 channels) cameras installed in the packinglines. (C) 2016 Elsevier B.V. All rights reserved.
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