Categorization of pork quality using Gabor filter-based hyperspectral imaging technology

Liu, L.; Ngadi, Michael; Prasher, Shiv O.; Gariépy, C.

doi:10.1016/j.jfoodeng.2010.03.001

Cited by 73 publications

(37 citation statements)

References 13 publications

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“…Similarly, Liu et al (2010) achieved an overall classification accuracy of 84% and 72% with and without Gabor features, respectively, in classifying pork based on quality using hyperspectral images.…”

Section: Assessment Of the Tenderness Classification Modelsmentioning

confidence: 89%

Hyperspectral imaging of ribeye muscle on hanging beef carcasses for tenderness assessment

Naganathan

Cluff

Samal

et al. 2015

Computers and Electronics in Agriculture

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Section: Assessment Of the Tenderness Classification Modelsmentioning

confidence: 89%

Hyperspectral imaging of ribeye muscle on hanging beef carcasses for tenderness assessment

Naganathan

Cluff

Samal

et al. 2015

Computers and Electronics in Agriculture

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“…The second step in the analysis consisted of spectral data reduction by identifying the 20 spectral bands with highest contribution to the classification of reflectance profiles from weevil infested (INF = 1) and non-infested (INF = 0) field peas. Linear discriminant analysis (LDA) (Fisher, 1936) has been used widely in classifications of food products based on hyperspectral imaging (Park et al, 2007;Gcdmez-Sanchis et al, 2008;Nansen et al, 2008;Gowen et al, 2009;Liu et al, 2010;Kalkan et al, 2011;Shahin and Symons, 2011;Baranowski et al, 2012). Consequently, stepwise LDA (PROC STEPDISC) was used to select the ''best'' 20 spectral bands.…”

Section: Data Processing and Analysismentioning

confidence: 99%

“…That is, either through spectral band ratios or coefficients, it is the relative relationship between reflectance values in different spectral bands that determine whether a pixel or the average reflectance profile of an object is classified as ''a'' or ''b''. Linear discriminant analysis (Fisher, 1936) has been used successfully in many hyperspectral image based classifications of food products (Park et al, 2007;Gcdmez-Sanchis et al, 2008;Nansen et al, 2008;Gowen et al, 2009;Liu et al, 2010;Kalkan et al, 2011;Shahin and Symons, 2011). An alternative classification approach is possible, when hyperspectral imaging data are acquired, because each pixel is associated with a relative coordinate (x and y) within the image cube (Nansen, 2012).…”

Section: Introductionmentioning

confidence: 99%

Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

Nansen

Zhang

Aryamanesh

et al. 2014

Journal of Food Engineering

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In this study, we acquired 72 (training data) and 30 (independent validation) high-spatial resolution (7 by 7 pixels per mm 2 ) hyperspectral imaging data [240 spectral bands from 392 to 889 nm (spectral resolution = 2.1 nm)] from samples of field peas (Pisum sativum) with and without pea weevil (Bruchus pisorum) infestation. The reflectance data were analyzed with linear discriminant analysis (LDA) or either reflectance values only or of a combination of reflectance values and variogram parameters (derived from variogram analysis) from a single spectral band (782 nm). All examined classification models were assessed based on sensitivity (ability to positively detect infestation), specificity (ability to positively detect noninfestation), and accurate classification of 30 samples of independent validation data. Highest classification performance was obtained with a combination of reflectance values in two spectral bands (641 nm and 868 nm) and variogram parameters derived from 782 nm. This classification model was associated with a sensitivity of 94.7% and a specificity of 100%. In addition, all 30 independent validation data were accurately classified (100%). For comparison, traditional linear discriminant analyses of 108,351 reflectance profiles from individual pixels in the 72 samples or average reflectance profiles from the 72 samples classified the validation data with 84.0% and 83.3% accuracy, respectively. We are unaware of any reflectance-based classification system, which -based on reflectance data acquired in only three channels (spectral bands) -can provide this level of sensitivity and specificity and classification of independent validation data of internal defects in food products. Accurate and reliable classification of food objects based reflectance values in only a few spectral bands would likely imply low computer processing requirements and rapid data analysis. Thus, we believe that the current classification method may be useful for quality control systems of a wide range of food products.

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“…These technological improvements have opened up new areas, especially, in ground based applications [2,3], where the images are taken close enough to the subject to obtain a relatively detailed view. Examples are inspection tasks such as medical imaging [4,5], quality control in processing plants [6,7], surveillance tasks, etcetera)…”

Section: Introductionmentioning

confidence: 99%

An Evolved Cellular Automata Based Approach to Hyperspectral Image Processing

Priego

Souto

Bellas

et al. 2013

Advanced Techniques for Knowledge Engineering and Innovative Applications

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Abstract. This chapter addresses the problem of processing Hyperspectral images (HI) in real time. It is a relevant problem as working with these types of images usually involves processing very large quantities of data due to their high spectral and spatial resolutions. To achieve real time performance requires resorting to extremely distributed architectures, such as Graphic processing units (GPUs). Most of the algorithms that have been developed for Hyperspectral image (HIS) processing are currently sequential and too cumbersome to achieve the necessary parallelism. A very promising approach for solving this problem is using Cellular Automata (CA) based algorithms as this is an intrinsically distributed computational paradigm that would adapt very snugly to these high performance architectures. The main problem of CAs is that it is necessary to endow them with rule sets which, through iterative cycles of interactions among cells, provide the final desired result. Determining these rule sets is non-trivial. In fact it is a highly difficult task, especially in complex processing operations. Here, the objective is to highlight how this problem can be solved through the use of evolutionary techniques. The proposed algorithm has been named Evolving Cellular Automata (ECA) and it has been tested in two standard operations within hyperspectral imaging: edge detection and segmentation. ECA is applied over synthetic and real HISs and the results are compared to those of well-established techniques, confirming the successful response of this approach and its potential for execution in GPU type architectures. This allows their execution in real time even when the size of the images (both spectrally and spatially) or the speed at which they are taken is large.

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Categorization of pork quality using Gabor filter-based hyperspectral imaging technology

Cited by 73 publications

References 13 publications

Hyperspectral imaging of ribeye muscle on hanging beef carcasses for tenderness assessment

Hyperspectral imaging of ribeye muscle on hanging beef carcasses for tenderness assessment

Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

An Evolved Cellular Automata Based Approach to Hyperspectral Image Processing

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