Study on classification of pork quality using hyperspectral imaging technique

Shan Zeng; Jun Bai; Haibin Wang

doi:10.1117/12.2205727

17 December 2015 Study on classification of pork quality using hyperspectral imaging technique

Shan Zeng, Jun Bai, Haibin Wang

Proceedings Volume 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis; 981110 (2015) https://doi.org/10.1117/12.2205727
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

The relative problems’ research of chilled meat, thawed meat and spoiled meat discrimination by hyperspectral image technique were proposed, such the section of feature wavelengths, et al. First, based on 400 ~ 1000nm range hyperspectral image data of testing pork samples, by K-medoids clustering algorithm based on manifold distance, we select 30 important wavelengths from 753 wavelengths, and thus select 8 feature wavelengths (454.4, 477.5, 529.3, 546.8, 568.4, 580.3, 589.9 and 781.2nm) based on the discrimination value. Then 8 texture features of each image under 8 feature wavelengths were respectively extracted by two-dimensional Gabor wavelets transform as pork quality feature. Finally, we build a pork quality classification model using the fuzzy C-mean clustering algorithm. Through the experiment of extracting feature wavelengths, we found that although the hyperspectral images between adjacent bands have a strong linear correlation, they show a significant non-linear manifold relationship from the entire band. K-medoids clustering algorithm based on manifold distance used in this paper for selecting the characteristic wavelengths, which is more reasonable than traditional principal component analysis (PCA). Through the classification result, we conclude that hyperspectral imaging technology can distinguish among chilled meat, thawed meat and spoiled meat accurately.

Citation Download Citation

Shan Zeng, Jun Bai, and Haibin Wang "Study on classification of pork quality using hyperspectral imaging technique", Proc. SPIE 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis, 981110 (17 December 2015); https://doi.org/10.1117/12.2205727

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KEYWORDS

Hyperspectral imaging

Image quality

Feature extraction

Distance measurement

Image classification

Principal component analysis

Calibration

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