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

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.

[1]  Intaek Kim,et al.  Detection of bruise damage of pear using hyperspectral imagery , 2012, 2012 12th International Conference on Control, Automation and Systems.

[2]  Jiewen Zhao,et al.  Detection of Bruise on Pear by Hyperspectral Imaging Sensor with Different Classification Algorithms , 2010 .

[3]  Moon S. Kim,et al.  Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery , 2013 .

[4]  Fei Liu,et al.  Application of Visible and Near-Infrared Hyperspectral Imaging for Detection of Defective Features in Loquat , 2014, Food and Bioprocess Technology.

[5]  Shintaroh Ohashi,et al.  Detection of external insect infestations in jujube fruit using hyperspectral reflectance imaging , 2011 .

[6]  Yong He,et al.  [Study on identification the crack feature of fresh jujube using hyperspectral imaging]. , 2014, Guang pu xue yu guang pu fen xi = Guang pu.

[7]  Di Wu,et al.  [Detection of pear injury based on visible-near infrared spectroscopy and multispectral image]. , 2011, Guang pu xue yu guang pu fen xi = Guang pu.

[8]  Xiuqin Rao,et al.  Detection of common defects on oranges using hyperspectral reflectance imaging , 2011 .

[9]  Jorge Chanona-Pérez,et al.  Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning , 2014 .

[10]  Zhang Chi,et al.  Effective wavelengths determination for detection of slight bruises on apples based on hyperspectral imaging , 2013 .

[11]  Hoonsoo Lee,et al.  Infrared imaging technology for detection of bruising damages of 'Shingo' pear , 2011, Defense + Commercial Sensing.

[12]  Nuria Aleixos,et al.  Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks , 2013, Food and Bioprocess Technology.

[13]  Colm P. O'Donnell,et al.  Identification of mushrooms subjected to freeze damage using hyperspectral imaging. , 2009 .

[14]  Koki Kyo,et al.  Wavelength selection in vis/NIR spectra for detection of bruises on apples by ROC analysis , 2012 .

[15]  Gamal ElMasry,et al.  Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis. , 2013, Talanta.

[16]  F. J. García-Ramos,et al.  Reduction of Mechanical Damage to Apples in a Packing Line Using Mechanical Devices , 2003 .

[17]  Fei Liu,et al.  Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging , 2013 .

[18]  Gamal ElMasry,et al.  Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. , 2012, Analytica chimica acta.

[19]  V. Chelladurai,et al.  Detection of Callosobruchus maculatus (F.) infestation in soybean using soft X-ray and NIR hyperspectral imaging techniques , 2014 .