牛肉在冷藏过程中表面结构会随水分迁移与组织变化而发生动态演变,而现有评价方法多依赖破坏性理化指标,对表面纹理信息的系统量化利用不足。该研究以4 ℃冷藏条件下的牛背最长肌为对象,选取0~30 h中的10个贮藏时间点,每个时间点分析3块独立样品(即共30块牛排),通过机器视觉和灰度共生矩阵构建对牛肉表面纹理特征的分析方法,系统揭示冷藏早期牛肉纹理演变规律。通过标准化的图像处理获得有效肌肉区域图像,并在多距离(1、3、5像素)、多方向(0°、45°、90°、135°)条件下提取对比度、不相似性等纹理参数。结果表明,随贮藏时间延长,表面对比度由37.82显著升高至54.31、熵由6.65增加至7.52(P<0.05),而同质性由0.486降低至0.434,能量由0.314降至0.245,角二阶矩由0.132显著下降至0.050,反映出肉表面由平滑均匀向复杂无序状态转变。主成分分析结果显示,第一主成分可解释59.76%的纹理信息,其得分随贮藏时间呈显著单向变化,可有效区分不同冷藏阶段样品,表征牛肉表面纹理整体演变程度。研究表明,基于灰度共生矩阵的纹理特征能够敏感反映冷藏过程中牛肉表面结构变化,为未来建立快速、无损的牛肉品质评价方法提供了新的思路和理论依据。
The surface structure of beef during refrigerated storage undergoes dynamic changes driven by moisture migration and tissue degradation;however, current evaluation approaches largely rely on destructive physicochemical measurements, with limited systematic quantification of surface texture information.This study investigated the evolution of surface texture characteristics of beef Longissimus thoracis (LT) during early refrigerated storage using a machine vision approach combined with gray-level co-occurrence Matrix analysis, based on 30 independent beef samples collected at 10 storage time points (0-30 h), with three samples analyzed at each time point.Standardized image processing procedures were applied to obtain surface images of beef samples stored at 4 ℃ for different durations.After image segmentation to extract effective muscle regions, multiple GLCM texture features, including contrast, dissimilarity, homogeneity, energy, angular second moment, and entropy, were calculated under multi-distance (1, 3, and 5 pixels) and directions (0°, 45°, 90° and 135°).The results showed that contrast increased significantly from 37.82 to 54.31 and entropy from 6.65 to 7.52 (P<0.05), whereas homogeneity decreased from 0.486 to 0.434, energy from 0.314 to 0.245, and angular second moment from 0.132 to 0.050 (P<0.05), indicating a transition of beef surface texture from uniform and smooth to heterogeneous and complex.Principal component analysis revealed that the first principal component explained 59.76% of the total variance and displayed a clear time-dependent shift, effectively discriminating samples at different storage stages.These findings demonstrate that GLCM-based texture features are sensitive to refrigerated storage-induced surface structural changes in beef and provide a non-destructive and quantitative approach for evaluating beef quality evolution during cold storage.
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