Hyperspectral imaging combined with multivariate analysis and band math for detection of common defects on peaches (Prunus persica)

Common defects are divided into two different types.For artificial defects, multivariate analysis were used for wavelengths selection.For non-artificial defect and stem detection, band math were constructed.The uneven lightness distribution was also investigated in this paper. Automatic detection of common defects on peaches by using imaging system is still a challenge due to the high variability of peach surface color, the similarity between the defects and stem, as well as the uneven distribution of lightness on peaches. In order to detect the common defects on peaches using hyperspectral imaging, defects were divided into two different types: artificial defects and non-artificial defects. For artificial defect detection, a two-step multivariate analysis method (Monte Carlo-Uninformative Variable Elimination and successful projections algorithm) was conducted in the spectral domain for the discriminant wavelength (DW) selection, and then minimum noise fraction (MNF) transform was conducted on the images at DWs for image processing and artificial defect detection. For the candidate non-artificial defect detection, a pair of two characteristic wavelengths at 925nm and 726nm was selected by analyzing the full spectra of sound and non-artificial defective regions, and then a band math equation was constructed for differentiating the non-artificial defect regions and stems from the sound and physical damage regions, and the candidate non-artificial defects (including non-artificial defects and stems) could be segmented by using a simple threshold method. In order to distinguish the stem from the segmented candidate non-artificial defect regions, another band math equation was constructed based on another pair of two characteristic wavelengths at 650nm and 675nm for stem identification. Additionally, the uneven lightness distribution in the spectral images was also investigated and eliminated by the band math methods. The overall classification accuracy of 93.3% for the 120 samples indicated that the selected DWs and proposed method were suitable and efficient for the common defect detection. The limitation of our research is the static inspection in one single view.

[1]  Baohua Zhang,et al.  Detection of Early Rottenness on Apples by Using Hyperspectral Imaging Combined with Spectral Analysis and Image Processing , 2015, Food Analytical Methods.

[2]  Jiang-Bo Li,et al.  [Principles and applications of hyperspectral imaging technique in quality and safety inspection of fruits and vegetables]. , 2014, Guang pu xue yu guang pu fen xi = Guang pu.

[3]  Stephen R. Delwiche,et al.  Hyperspectral near-infrared imaging for the detection of physical damages of pear , 2014 .

[4]  Da-Wen Sun,et al.  Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review , 2012, Critical reviews in food science and nutrition.

[5]  Sumio Kawano,et al.  Automatic image analysis and spot classification for detection of fruit fly infestation in hyperspectral images of mangoes , 2013 .

[6]  Pankaj B. Pathare,et al.  Colour Measurement and Analysis in Fresh and Processed Foods: A Review , 2012, Food and Bioprocess Technology.

[7]  W. Cai,et al.  A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra , 2008 .

[8]  Zou Xiaobo,et al.  In-line detection of apple defects using three color cameras system , 2010 .

[9]  Songyot Nakariyakul,et al.  Classification of internally damaged almond nuts using hyperspectral imagery , 2011 .

[10]  Da-Wen Sun,et al.  Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry , 2014, Food and Bioprocess Technology.

[11]  Chun-Chieh Yang,et al.  Development of multispectral imaging algorithm for detection of frass on mature red tomatoes , 2014 .

[12]  Moon S. Kim,et al.  Multispectral detection of organic residues on poultry processing plant equipment based on hyperspectral reflectance imaging technique , 2007 .

[13]  Yong He,et al.  Identification of crack features in fresh jujube using Vis/NIR hyperspectral imaging combined with image processing , 2014 .

[14]  Piotr Baranowski,et al.  Classification models of bruise and cultivar detection on the basis of hyperspectral imaging data , 2014 .

[15]  Baohua Zhang,et al.  Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of ‘Ya’ Pear , 2014, Food Analytical Methods.

[16]  Renfu Lu,et al.  Hyperspectral and multispectral imaging for evaluating food safety and quality , 2013 .

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

[18]  Liang Gong,et al.  Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier , 2015 .

[19]  Pengcheng Nie,et al.  Application of Time Series Hyperspectral Imaging (TS-HSI) for Determining Water Distribution Within Beef and Spectral Kinetic Analysis During Dehydration , 2013, Food and Bioprocess Technology.

[20]  Ning Wang,et al.  Early detection of apple bruises on different background colors using hyperspectral imaging , 2008 .

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

[22]  Baohua Zhang,et al.  Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review , 2014 .

[23]  Di Wu,et al.  Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. , 2013, Talanta.

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

[25]  Josse De Baerdemaeker,et al.  Bruise detection on ‘Jonagold’ apples using hyperspectral imaging , 2005 .

[26]  Di Wu,et al.  Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh. , 2013, Talanta.

[27]  Fernando López-García,et al.  Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach , 2010 .

[28]  Roberto Kawakami Harrop Galvão,et al.  A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm , 2008 .