Literature DB >> 22981646

Measurement of meat color using a computer vision system.

Antonio Girolami1, Fabio Napolitano, Daniela Faraone, Ada Braghieri.   

Abstract

The limits of the colorimeter and a technique of image analysis in evaluating the color of beef, pork, and chicken were investigated. The Minolta CR-400 colorimeter and a computer vision system (CVS) were employed to measure colorimetric characteristics. To evaluate the chromatic fidelity of the image of the sample displayed on the monitor, a similarity test was carried out using a trained panel. The panelists found the digital images of the samples visualized on the monitor very similar to the actual ones (P<0.001). During the first similarity test the panelists observed at the same time both the actual meat sample and the sample image on the monitor in order to evaluate the similarity between them (test A). Moreover, the panelists were asked to evaluate the similarity between two colors, both generated by the software Adobe Photoshop CS3 one using the L, a and b values read by the colorimeter and the other obtained using the CVS (test B); which of the two colors was more similar to the sample visualized on the monitor was also assessed (test C). The panelists found the digital images very similar to the actual samples (P<0.001). As to the similarity (test B) between the CVS- and colorimeter-based colors the panelists found significant differences between them (P<0.001). Test C showed that the color of the sample on the monitor was more similar to the CVS generated color than to the colorimeter generated color. The differences between the values of the L, a, b, hue angle and chroma obtained with the CVS and the colorimeter were statistically significant (P<0.05-0.001). These results showed that the colorimeter did not generate coordinates corresponding to the true color of meat. Instead, the CVS method seemed to give valid measurements that reproduced a color very similar to the real one.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 22981646     DOI: 10.1016/j.meatsci.2012.08.010

Source DB:  PubMed          Journal:  Meat Sci        ISSN: 0309-1740            Impact factor:   5.209


  6 in total

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Journal:  J Food Sci Technol       Date:  2017-06-19       Impact factor: 2.701

3.  A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork.

Authors:  Yi Xu; Quansheng Chen; Yan Liu; Xin Sun; Qiping Huang; Qin Ouyang; Jiewen Zhao
Journal:  Korean J Food Sci Anim Resour       Date:  2018-04-30       Impact factor: 2.622

4.  Propolis Extract as Antioxidant to Improve Oxidative Stability of Fresh Patties during Refrigerated Storage.

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Journal:  Foods       Date:  2019-11-24

5.  Short cold exposures during incubation and postnatal cold temperature affect performance, breast meat quality, and welfare parameters in broiler chickens.

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Review 6.  A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies.

Authors:  Yinyan Shi; Xiaochan Wang; Md Saidul Borhan; Jennifer Young; David Newman; Eric Berg; Xin Sun
Journal:  Food Sci Anim Resour       Date:  2021-07-01
  6 in total

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