Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5762610 | Postharvest Biology and Technology | 2017 | 12 Pages |
Abstract
Early detection of internal bruising is one of the major challenges in blueberry postharvest quality sorting processes. The potential of using near infrared (NIR) hyperspectral reflectance imaging (950-1650Â nm) with reduced spectral features was investigated for blueberry internal bruising detection 30Â min to 12Â h after mechanical impact. A least squares support vector machine (LS-SVM) was used to develop classification models to compute the spatial distribution of bruising based on the spectra extracted from regions of interest (ROIs) at four measurement times (30Â min, 2Â h, 6Â h, and 12Â h after mechanical impact). Three feature selection methods were used to select optimum wavelengths or band ratio images for bruising detection. The classification model, developed using optimum wavelengths selected by competitive adaptive reweighted sampling (CARS) (CARS-LS-SVM model) and full spectra (full spectra-LS-SVM), had similar performance in the identification of bruised blueberries. Band ratio images (1235Â nm/1035Â nm) achieved a comparable accuracy with the CARS-LS-SVM model at 6Â h, and higher accuracy than CARS-LS-SVM and full spectra-LS-SVM models at 12Â h. The overall classification accuracies of 77.5%, 83.8%, 92.5%, and 95.0% were obtained by band ratio images for blueberries 30Â min, 2Â h, 6Â h, and 12Â h after impact, respectively. In order to evaluate the performance of the proposed methods, additional validation samples were processed by the detection algorithm. The overall discrimination accuracies for healthy and bruised blueberries in the validation set were 93.3% and 98.0%, respectively, for CARS-LS-SVM model, and 93.3% and 95.9%, respectively, for band ratio images. The overall results indicated that NIR reflectance imaging can detect blueberry internal bruising as early as 30Â min after mechanical impact, and band ratio images computed from two wavelengths showed great potential to detect blueberry internal bruising on the packing line.
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Authors
Shuxiang Fan, Changying Li, Wenqian Huang, Liping Chen,