基于计算机视觉测量牛肉颜色和预测储存时间的方法的研究
Yixuan Chen1, Jinghao Zhou2, Fatih Oz3
1State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; School of Food Science and Technology, Jiangnan University, Wuxi 214122, China.
Meat science
|January 29, 2026
概括
这项研究引入了一种计算机视觉和机器学习方法,通过测量表面颜色来评估牛肉的新鲜度. 该技术准确地预测了储存时间和氧米球蛋白水平,提供了一种非破坏性的方法来监测牛肉质量.
科学领域:
- 食品科学 食品科学 食品科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 牛肉质量评估传统上依赖于主观方法或破坏性测试.
- 需要客观,非破坏性的方法来准确确定牛肉的新鲜度和保质期.
研究的目的:
- 开发一种使用计算机视觉和机器学习测量牛肉表面颜色的非破坏性方法.
- 准确预测牛肉的储存时间和氧基球蛋白含量.
- 为了将计算机视觉衍生色度数据与传统色度数据进行比较.
主要方法:
- 获得长胸肌 (LT) 肌肉的图像.
- 使用GrabCut和Otsu二元化对红肌区域进行细分.
- 提取RGB值并将其转换为CIE L*,a*,b*色彩空间.
- 开发和培训一个卷积神经网络 (CNN) 模型.
主要成果:
- 与传统色度计相比,计算机视觉对牛肉颜色的时间变化具有更高的灵敏度.
- 该CNN模型实现了高精度 (R2=0.926用于存储时间,R2=0.893用于氧化球蛋白).
- 开发的方法为评估牛肉新鲜度提供了一个强大的框架.
结论:
- 计算机视觉与机器学习相结合,为牛肉质量评估提供了准确而非破坏性的方法.
- 这种方法可以更好地反映牛肉的外观和新鲜度.
- 这项研究为预测牛肉储存时间和氧化球蛋白水平建立了一个科学稳健的框架.
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