A Nondestructive Method Based on an Artificial Vision for Beef Meat Quality Assesement

The performance of artificial vision as a nondestructive technology has been evaluated in monitoring beef meat quality at a storage temperature of 4°C for more than two weeks. A reference method based on bacteriological measurement is performed in parallel with the artificial vision system to analyse the meat samples. Artificial vision data were collected from color image of meat samples in parallel with data from microbiological analysis for the enumeration of the population dynamics of total viable counts (TVC). Two color models are used to define fresh beef color in this study: the RGB (Red, Green and Blue) and HSI (Hue, Satutation and Intensity) model. Fuzzy ARTMAP artificial neural network based on a classification technique is used to investigate the performance of the artificial vision system in the quality classification of beef meat. The Fuzzy ARTMAP models built classified beef meat samples based on the total microbial population into "unspoiled" (microbial counts < 6 log10 cfu/g) and "spoiled" (microbial counts ≥ 6 log10 cfu/g). Good classification rates are obtained (95.24 %). Finally training and testing an artificial system will be considered as a useful alternative tool for beef meat quality assesement.

[1]  Da-Wen Sun,et al.  Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview , 2012, Critical reviews in food science and nutrition.

[2]  E. Borch,et al.  Bacterial spoilage of meat and cured meat products. , 1996, International journal of food microbiology.

[3]  Seyed Mohammad Ali Razavi,et al.  Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit , 2011 .

[4]  Murat O. Balaban,et al.  Machine Vision Applications to Aquatic Foods: A Review , 2011 .

[5]  Factors influencing the accuracy of the plating method used to enumerate low numbers of viable micro-organisms in food. , 2010, International journal of food microbiology.

[6]  Lone Gram,et al.  Food spoilage--interactions between food spoilage bacteria. , 2002, International journal of food microbiology.

[7]  Abd El-Salam,et al.  Application of Computer Vision Technique on Sorting and Grading ofFruits and Vegetables , 2012 .

[9]  E. Llobet,et al.  Electronic Nose Based on Metal Oxide Semiconductor Sensors as an Alternative Technique for the Spoilage Classification of Red Meat , 2008, Sensors.

[10]  Carlos Esquerre,et al.  Initial studies on the quantitation of bruise damage and freshness in mushrooms using visible-near-infrared spectroscopy. , 2009, Journal of agricultural and food chemistry.

[11]  Da-Wen Sun,et al.  Computer vision technology for food quality evaluation , 2008 .

[12]  Yankun Peng,et al.  Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique. , 2012, Meat science.

[13]  Li Liu,et al.  Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety , 2014, Sensors.

[14]  R. Dennis Cook,et al.  Cross-Validation of Regression Models , 1984 .

[15]  Jiewen Zhao,et al.  Determination of total volatile basic nitrogen (TVB-N) content and Warner–Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy , 2011 .

[16]  Domingo Mery,et al.  Automated Design of a Computer Vision System for Visual Food Quality Evaluation , 2013, Food and Bioprocess Technology.

[17]  Dietrich Knorr,et al.  Characterization of High-Hydrostatic-Pressure Effects on Fresh Produce Using Chlorophyll Fluorescence Image Analysis , 2009 .

[18]  Gauri S. Mittal,et al.  Rapid Detection of Microorganisms Using Image Processing Parameters and Neural Network , 2010 .

[19]  Ahmed Roukhe,et al.  SORTING DATES FRUIT BUNCHES BASED ON THEIR MATURITY USING CAMERA SENSOR SYSTEM , 2013 .

[20]  Da-Wen Sun,et al.  Learning techniques used in computer vision for food quality evaluation: a review , 2006 .

[21]  Eduard Llobet,et al.  Fuzzy ARTMAP based electronic nose data analysis , 1999 .

[22]  Renfu Lu,et al.  Gloss Evaluation of Curved-surface Fruits and Vegetables , 2009 .

[23]  Karl McDonald,et al.  The effect of injection level on the quality of a rapid vacuum cooled cooked beef product , 2001 .

[24]  Jiewen Zhao,et al.  Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. , 2014, Food chemistry.

[25]  A Vyawahare,et al.  Computer Vision System for Colour Measurement - Fundamentals and Applications in Food Industry: A Review , 2013 .

[26]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[27]  R Mahendran,et al.  Application of Computer Vision Technique on Sorting and Grading ofFruits and Vegetables , 2012 .

[28]  Suranjan Panigrahi,et al.  Design and development of a metal oxide based electronic nose for spoilage classification of beef , 2006 .

[29]  J. W. Arnold,et al.  APC values and volatile compounds formed in commercially processed, raw chicken parts during storage at 4 and 13 °C and under simulated temperature abuse conditions† , 2000 .

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

[31]  Federico Pallottino,et al.  Image Analysis Techniques for Automated Hazelnut Peeling Determination , 2010 .

[32]  Da-Wen Sun,et al.  Improving quality inspection of food products by computer vision: a review , 2004 .

[33]  Da-Wen Sun,et al.  Recent advances in the use of computer vision technology in the quality assessment of fresh meats , 2011 .