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Appl. Sci. 2016, 6(12), 450;

Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Mechanical and Electrical Engineering Institution, East China Jiaotong University, Nanchang 330013, China
Authors to whom correspondence should be addressed.
Academic Editor: Kuanglin Kevin Chao
Received: 5 October 2016 / Revised: 8 December 2016 / Accepted: 15 December 2016 / Published: 19 December 2016
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Hyperspectral imaging technology was employed to detect slight bruises on Korla pears. The spectral data of 60 bruised samples and 60 normal samples were collected by a hyperspectral imaging system. To select the characteristic wavelengths for detection, several chemometrics methods were used on the raw spectra. Firstly, principal component analysis (PCA) was conducted on the spectra ranging from 420 to 1000 nm of all samples. Considering that the reliability of the first two PCs was more than 90%, five characteristic wavelengths (472, 544, 655, 688 and 967 nm) were selected by the loading plot of PC1 and PC2. Then, each of the wavelength variables was considered as an independent classifier for bruised/normal classification, and all classifiers were evaluated by the receiver operating characteristic (ROC) analysis. Two wavelengths (472 and 967 nm) with the highest values under the curve (0.992 and 0.980) were finally selected for modeling. The classifying model was built by partial least squares discriminant analysis (PLS-DA) and the bruised/normal classification accuracy of the modeling set (45 damaged samples and 45 normal samples) and prediction set (15 damaged samples and 15 normal samples) was 98.9% and 100%, respectively, which is similar to that of the PLS-DA model based on the whole spectral range. The result shows that it is feasible to select characteristic wavelengths for the detection of slight bruises on pears by the methods combining the PCA and ROC analysis. This study can lay a foundation for the development of an online detection system for slight bruise detection on pears. View Full-Text
Keywords: pear; damage detection; hyperspectral imaging; characteristic wavelength pear; damage detection; hyperspectral imaging; characteristic wavelength

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Jiang, H.; Zhang, C.; He, Y.; Chen, X.; Liu, F.; Liu, Y. Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging. Appl. Sci. 2016, 6, 450.

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