Erena Tariku, Belay Abera, Hailu Demelash, Asefa Bezuayehu Gutema, Geleta Mulatu, Deme Tesfaye
Department of Food Science and Applied Nutrition, Bioprocessing and Biotechnology Center of Excellence, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 16417, Ethiopia.
Food Science and Nutrition Research Directorate, Ethiopian Institute of Agricultural Research, Addis Ababa P.O. Box 64, Ethiopia.
J Imaging. 2024 May 28;10(6):130. doi: 10.3390/jimaging10060130.
Meat characterized by a high marbling value is typically anticipated to display enhanced sensory attributes. This study aimed to predict the marbling scores of rib-eye, steaks sourced from the Longissimus dorsi muscle of different cattle types, namely Boran, Senga, and Sheko, by employing digital image processing and machine-learning algorithms. Marbling was analyzed using digital image processing coupled with an extreme gradient boosting (GBoost) machine learning algorithm. Meat texture was assessed using a universal texture analyzer. Sensory characteristics of beef were evaluated through quantitative descriptive analysis with a trained panel of twenty. Using selected image features from digital image processing, the marbling score was predicted with R (prediction) = 0.83. Boran cattle had the highest fat content in sirloin and chuck cuts (12.68% and 12.40%, respectively), followed by Senga (11.59% and 11.56%) and Sheko (11.40% and 11.17%). Tenderness scores for sirloin and chuck cuts differed among the three breeds: Boran (7.06 ± 2.75 and 3.81 ± 2.24, respectively), Senga (5.54 ± 1.90 and 5.25 ± 2.47), and Sheko (5.43 ± 2.76 and 6.33 ± 2.28 Nmm). Sheko and Senga had similar sensory attributes. Marbling scores were higher in Boran (4.28 ± 1.43 and 3.68 ± 1.21) and Senga (2.88 ± 0.69 and 2.83 ± 0.98) compared to Sheko (2.73 ± 1.28 and 2.90 ± 1.52). The study achieved a remarkable milestone in developing a digital tool for predicting marbling scores of Ethiopian beef breeds. Furthermore, the relationship between quality attributes and beef marbling score has been verified. After further validation, the output of this research can be utilized in the meat industry and quality control authorities.
通常认为大理石花纹值高的肉会具有更好的感官特性。本研究旨在通过数字图像处理和机器学习算法，预测不同牛种（即博拉牛、森加牛和谢科牛）背最长肌的肋眼牛排的大理石花纹得分。使用数字图像处理结合极端梯度提升（GBoost）机器学习算法分析大理石花纹。使用通用纹理分析仪评估肉的质地。通过由20名经过培训的人员组成的小组进行定量描述分析，评估牛肉的感官特性。利用数字图像处理中选定的图像特征，预测大理石花纹得分，R（预测值）=0.83。博拉牛的里脊和肩胛肉切块中脂肪含量最高（分别为12.68%和12.40%），其次是森加牛（11.59%和11.56%）和谢科牛（11.40%和11.17%）。三个品种的里脊和肩胛肉切块的嫩度得分有所不同：博拉牛（分别为7.06±2.75和3.81±2.24）、森加牛（5.54±1.90和5.25±2.47）、谢科牛（5.43±2.76和6.33±2.28 N/mm）。谢科牛和森加牛具有相似的感官特性。与谢科牛（2.73±1.28和2.90±1.52）相比，博拉牛（4.28±1.43和3.68±1.21）和森加牛（2.88±0.69和2.83±0.98）的大理石花纹得分更高。该研究在开发用于预测埃塞俄比亚牛肉品种大理石花纹得分的数字工具方面取得了显著进展。此外，质量属性与牛肉大理石花纹得分之间的关系已得到验证。经过进一步验证后，本研究的成果可应用于肉类行业和质量控制部门。
