Utilization of image processing to quantitate surface metmyoglobin on fresh beef

Area of Science:

• Food Science
• Meat Science
• Colorimetry

Background:

• Meat color is a critical indicator of freshness and quality.
• Metmyoglobin (metMb) formation causes undesirable browning in fresh beef.
• Factors like ascorbic acid and mechanically recovered neck bone lean (MRNL) influence beef color.

Purpose of the Study:

• To evaluate the efficacy of image processing in predicting surface metmyoglobin (metMb) percentage in ground beef.
• To investigate the impact of ascorbic acid and MRNL on beef color variation.
• To establish a predictive model for metMb using image analysis parameters.

Main Methods:

• Ground beef patties with varying ascorbic acid and MRNL content were prepared.
• Patties underwent six days of simulated retail display to induce color changes.
• Surface color was measured using established methods, and image processing parameters (hue, saturation, intensity) were recorded.

Main Results:

• Image processing parameters (hue, saturation, intensity) were used to develop a prediction equation for metMb percentage.
• The developed model, utilizing hue, saturation, and intensity, accounted for 93% of the variation in surface metMb.
• Statistical criteria (RMSE, R-square, Mallow's Cp) were employed to select the optimal predictive model.

Conclusions:

• Image processing provides an objective and accurate method for measuring surface metmyoglobin in fresh ground beef.
• Color parameters derived from image analysis are effective predictors of metMb levels.
• This technology holds potential for quality control and shelf-life assessment in the meat industry.