Hyperspectral imaging for food applications

Food quality analysis is a key area where reliable, nondestructive and accurate measures are required. Hyperspectral imaging is a technology which meets all of these requirements but only if appropriate signal processing techniques are implemented. In this paper, a discussion of some of these state-of-the-art processing techniques is followed by an explanation of four different applications of hyperspectral imaging for food quality analysis: shelf life estimation of baked sponges; beef quality prediction; classification of Chinese tea leaves; and classification of rice grains. The first two of these topics investigate the use of hyperspectral imaging to produce an objective measure about the quality of the food sample. The final two studies are classification problems, where an unknown sample is assigned to one of a previously defined set of classes.

[1]  Shutao Li,et al.  Novel Two-Dimensional Singular Spectrum Analysis for Effective Feature Extraction and Data Classification in Hyperspectral Imaging , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Di Wu,et al.  Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part II: Applications , 2013 .

[3]  Jun Wang,et al.  Fast Implementation of Singular Spectrum Analysis for Effective Feature Extraction in Hyperspectral Imaging , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Zhe Liu,et al.  Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging. , 2014, Applied optics.

[5]  Chein-I Chang,et al.  Automatic spectral target recognition in hyperspectral imagery , 2003 .

[6]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

[7]  S. Marshall,et al.  Comparison between near infrared spectroscopy and hyperspectral imaging in predicting beef eating quality , 2014 .

[8]  Rui Yan,et al.  Dual-ellipse fitting approach for robust gait periodicity detection , 2012, Neurocomputing.

[9]  S. Prasher,et al.  Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn , 2003 .

[10]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Stephen Marshall,et al.  Quality-assured fingerprint image enhancement and extraction using hyperspectral imaging , 2011, ICDP.

[12]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[13]  Stephen Marshall,et al.  Effective Feature Extraction and Data Reduction in Remote Sensing Using Hyperspectral Imaging [Applications Corner] , 2014, IEEE Signal Processing Magazine.

[14]  Alex Smola,et al.  Kernel methods in machine learning , 2007, math/0701907.

[15]  G. Hemke,et al.  Prediction of pork quality using visible/near-infrared reflectance spectroscopy. , 2006, Meat science.

[16]  S. Marshall,et al.  Prediction of lamb eating quality using hyperspectral imaging , 2015 .

[17]  Junwei Han,et al.  Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing , 2014 .

[18]  Ieee Staff.  2017 25th European Signal Processing Conference (EUSIPCO) , 2017 .

[19]  Stephen Marshall,et al.  Quantitative Prediction of Beef Quality Using Visible and NIR Spectroscopy with Large Data Samples Under Industry Conditions , 2015 .