Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins

Nowadays, the detection of fruit infected with Penicillium sp. fungi on packing lines is carried out manually under ultraviolet illumination. Ultraviolet sources induce visible fluorescence of essential oils, present in the skin of citrus and which are released by the action of fungi, thus increasin...

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Bibliographic Details
Published in:Journal of food engineering 2008-11, Vol.89 (1), p.80-86
Main Authors: Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., Blasco, J.
Format: Article
Language:English
Subjects:
Biological and medical sciences
CART
Citrus
Feature selection
Food engineering
Food industries
Fruit inspection
Fundamental and applied biological sciences. Psychology
General aspects
Hyperspectral imaging
Image analysis
LDA
Machine vision
Mandarins
Methods of analysis, processing and quality control, regulation, standards
Penicillium
Penicillium digitatum
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Summary:Nowadays, the detection of fruit infected with Penicillium sp. fungi on packing lines is carried out manually under ultraviolet illumination. Ultraviolet sources induce visible fluorescence of essential oils, present in the skin of citrus and which are released by the action of fungi, thus increasing the contrast between sound and rotten skin. This work analyses a set of techniques aimed at detecting rotten citrus without the use of UV lighting. The techniques used include hyperspectral image acquisition, pre-processing and calibration, feature selection and segmentation using linear and non-linear methods for classification of fruits. Different methods such as correlation analysis, mutual information, stepwise, and genetic algorithms based on linear discriminant analysis (LDA) are studied to select the most relevant bands. Image segmentation relies on the combination of efficient band selection techniques and also on pixel classification methods such as classification and regression trees (CART) and LDA. The results were obtained using a large dataset of images of mandarins cv. “Clemenules” by applying the CART method. The hyperspectral computer vision system proposed here is capable of detecting damage caused by Penicillium digitatum in mandarins using a reduced set of optimally selected bands.
ISSN:0260-8774
1873-5770
DOI:10.1016/j.jfoodeng.2008.04.009