An Improved Method of Detecting Pork Freshness Based on Computer Vision in On-line System

On the basis of computer vision, this paper studied and developed an on-line detection system for pork freshness, which include the overall design of the system scheme, the hardware design and functions, the software functions and detection algorithm. The systematical hardware is composed of image acquisition unit, light source unit, control unit, drive transmission device and computer. For the software implementation of the collected images, the processes include three steps as following: 1) Otsu algorithm was applied to remove the disturbance of background and other noises. 2) The fat areas were eliminated according to color difference between pork fat and the muscle. 3) A new Color Region Ratio (CRR) feature extraction method applying color- layering approach was proposed for the identification of pork freshness. The testing of 100 samples have shown that the CRR feature is highly correlated with pork freshness, reaching 88 % detection accuracy, and it is feasible to use CRR feature to detect the pork freshness. Copyright © 2014 IFSA Publishing, S. L.

[1]  Fidel Toldrá,et al.  Low-frequency dielectric spectrum to determine pork meat quality , 2010 .

[2]  Mirosław Słowiński,et al.  Lightness of the color measured by computer image analysis as a factor for assessing the quality of pork meat. , 2011, Meat science.

[3]  Fidel Toldrá,et al.  Microwave dielectric spectroscopy for the determination of pork meat quality , 2010 .

[4]  Wei Wang,et al.  [Rapid nondestructive detection of water content in fresh pork based on spectroscopy technique combined with support vector machine]. , 2012, Guang pu xue yu guang pu fen xi = Guang pu.

[5]  R. Lu,et al.  Development of a multispectral imaging prototype for real-time detection of apple fruit firmness , 2007 .

[6]  F. Teuscher,et al.  Application of computer image analysis to measure pork marbling characteristics. , 2005, Meat science.

[7]  Da-Wen Sun,et al.  Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. , 2009, Meat science.

[8]  Gamal ElMasry,et al.  Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. , 2013, Food chemistry.

[9]  Petr Dejmek,et al.  Calibrated color measurements of agricultural foods using image analysis , 2006 .

[10]  Daniel Cozzolino,et al.  Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy , 2006 .

[11]  Fang Cheng,et al.  [Online determination of pH in fresh pork by visible/near-infrared spectroscopy]. , 2010, Guang pu xue yu guang pu fen xi = Guang pu.

[12]  Douglas Fernandes Barbin,et al.  Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging , 2013 .

[13]  Yaohua Hu,et al.  Detection of Total Volatile Basic Nitrogen (TVB-N) in Pork Using Fourier Transform Near-Infrared (FT-NIR) Spectroscopy and Cluster Analysis for Quality Assurance , 2012 .

[14]  Da-Wen Sun,et al.  Recent developments in the applications of image processing techniques for food quality evaluation , 2004 .