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Research Detail

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Md. Faizur Rahman
Department of Animal Science, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

Abdullah Iqbal
Department of Food Technology and Rural Industries, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

Md. Abul Hashem
Department of Animal Science, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh

Akinbode A. Adedeji
Department of Biosystems and Agricultural Engineering, 128 C.E. Barnhart Building, University of Kentucky, Lexington KY 40546, USA

Imaging technique or computer vision (CV) technology has received huge attention as a rapid and non-destructive technique throughout the world for measuring quality attributes of agricultural products including meat and meat products. This study was conducted to test the ability of CV technology to predict the quality attributes of beef. Images were captured from longissimus dorsi muscle in beef at 24 h post-mortem. Traits evaluated were color value (L*, a*, b*), pH, drip loss, cooking loss, dry matter, moisture, crude protein, fat, ash, thiobarbituric acid reactive substance (TBARS), peroxide value (POV), free fatty acid (FFA), total coliform count (TCC), total viable count (TVC) and total yeast-mould count (TYMC). Images were analyzed using the Matlab software (R2015a). Different reference values were determined by physicochemical, proximate, biochemical and microbiological test. All determinations were done in triplicate and the mean value was reported. Data analysis was carried out using the programme Statgraphics Centurion XVI. Calibration and validation model were fitted using the software Unscrambler X version 9.7. A higher correlation found in a* (r=0.65) and moisture (r=0.56) with ‘a*’ value obtained from image analysis and the highest calibration and prediction accuracy was found in lightness (r2c=0.73, r2p=0.69) in beef. Results of this work show that CV technology may be a useful tool for predicting meat quality traits in the laboratory and meat processing industries.

 
  Beef quality, Computer vision technology, Correlation, Calibration, Validation
  Mymensingh town, Bangladesh
  
  
  Farm Mechanization
  Beef, ICT

To conformation parameter of foodstuffs, an effort has been taken to find out the correlation between image value and chemical composition of beef ?through this experiment.

Sample preparation: Beef samples were collected from 45 carcasses between the 12th and 13th ribs (longissimus dorsi muscle) of young zebu bulls. All samples were collected from different abattoirs at Mymensingh town, Bangladesh. The indigenous bulls were around two and a half years of age with a live weight ranges from 250 to 300 kg. Each steak was 2.5 cm thick and weight was around 130 g. Samples were areal-packed and stored in the refrigerator for 24 h at 4°C as it takes at least 24 h to convert muscle into meat. After 24 h samples were removed from the refrigerator and then kept it in a tray for about 10–12 minutes to allow moisture to appear on beef surface. Then the surface of the samples were soaked gently with the help of blotting paper which subsequently used for better color value estimation. Image acquisition: Image acquisition of the sample was performed with the help of imaging system (Computer Vision System) developed locally following the information reported by Iqbal et al. (2010) and Valous et al. (2009). The main components of the developed system are: an illumination source, a color digital camera (Canon IXUS, Model No. 190, Tokyo, Japan), and a computer-supported with an image acquisition software package (Matlab 2015a, The Mathworks, Natick, MA, USA). Images of the samples were captured using the camera of imaging system and were stored in the computer for further processing. An image processing software (Matlab 2015a, The Mathworks, USA) was applied for image analysis. Surface color evaluation: The surface color of the samples were measured in terms of L* (lightness), a* (redness) and b* (yellowness) values using a Chroma meter (CR-400, Konica Minolta, Osaka, Japan) following the guidelines provided by Commission International de I’Eclairage (CIE) system (CIE, 1976). Physico-chemical analysis: pH value recording: The pH value in beef was measured by meat pH meter (Model no. HI99163, Hanna Instruments, Woonsocket, RI, USA). The pH meter was adjusted with pH 7.01 buffer solution before the measurement. The electrodes were rinsed with cleaning solution after use. Drip loss (DL) measurement: For DL measurement 30 g sample was hung with a wire and kept in an air tight plastic container for 24 h. After 24 h the sample was weighed and calculated the difference. It was expressed as percentage (%). Cooking loss (CL) measurement: 30 g beef sample was taken in a poly bag and heated it in water bath until the temperature rises to 71°C in sample. Beef with 71°C was taken out from the water bath and soaked it with tissue paper.  Enumeration of total yeast-mould count (TYMC): For the determination of total yeast and mould counts, 0.1 mL of each ten-fold dilution was transferred and spread on triplicate PDA agar using a sterile pipette for each dilution. The diluted samples were spread as quickly as possible on the surface of the plate with a sterile glass spreader. One sterile spreader was used for each plate. The plates were then kept in an incubator at 25°C for 48–72 h. After incubation, 30–300 colonies were counted with the aid of a colony counter. The average number of colonies in a particular dilution was multiplied by the dilution factor to obtain the yeast and mould count. The results of the yeast and mould count were expressed as the number of organisms of colony-forming units per gram (CFU/g) of sample. Statistical analysis: Descriptive statistical analysis and Pearson correlations between the image data and reference data both were determined using the statistical package, Statgraphics Centurion XV.I. STATPOINT TECHNOLOGIES. Warrenton, Virginia, USA with a significance level of p<.05. The calibration and validation model were fitted using the software Unscrambler X version 9.7.

  Food Science of Animal Resources, v.40(6); 2020 No. , PMC7713771
  
Funding Source:
1.   Budget:  
  

The study aimed at assessing the ability of the CV system to predicts beef quality traits. Samples were analyzed for color, physicochemical, proximate, biochemical and microbiological values by conventional analytical techniques. CV technology has been standardized. After the standardization correlation coefficient was determined between image data and reference data. The calibration model and validation model was applied to find the level of accuracy of the CV technology. The highest calibration and prediction accuracy was found for color lightness (L*), medium accuracy was found in the redness (a*), pH, DL, crude protein and ash content of the sample. However, more samples and trials are to be conducted in future for the development of robust models and getting higher predicting values of the studied parameters.

  Journal
  


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