Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit

Abstract

The development of systems for automatically detecting decay in citrus fruit during quality control is still a challenge for the citrus industry. The feasibility of reflectance spectroscopy in the visible and near infrared (NIR) regions was evaluated for the automatic detection of the early symptoms of decay caused by Penicillium digitatum fungus in citrus fruit. Reflectance spectra of sound and decaying surface parts of mandarins cv. 'Clemenvilla' were acquired in two different spectral regions, from 650 nm to 1050 nm (visible NIR) and from 1000 nm to 1700 nm (NIR), pointing to significant differences in spectra between sound and decaying skin for both spectral ranges. Three different manifold learning methods (principal component analysis, factor analysis and Sammon mapping) were investigated to transform the high-dimensional spectral data into meaningful representations of reduced dimensionality containing the essential information. The low-dimensional data representations were used as input feature vectors to discriminate between sound and decaying skin using a supervised classifier based on linear discriminant analysis. The best classification results were achieved by employing factor analysis on the NIR spectra, yielding a maximum overall classification accuracy of 97.8%, with a percentage of well-classified sound and decaying samples of 100% and 94.4%, respectively. These results lay the foundation for the future implementation of reflectance spectroscopy technology on a commercial fruit sorter for the purpose of detecting decay in citrus fruit.