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Classification of Skeletal Muscle Fibers01:48

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Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
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Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
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开发一种替代的分类方法,用从横截面上线性测量来预测火腿成分.

X Wei1, B Bohrer2, B Uttaro3

  • 1Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, Lacombe, AB T4L 1W1, Canada; University of Guelph, Guelph, ON N1G 2W1, Canada.

Meat science
|June 10, 2023
PubMed
概括
此摘要是机器生成的。

猪肉火腿的数字图像分析使用线性测量准确预测瘦肉和脂肪百分比. 这种方法可以对用于商业加工的火腿进行分类,为猪肉质量评估提供了实际应用.

关键词:
分类 分类 分类 分类.组成 构成 构成DXA DXA 是一个哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈姆哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈线性测量方法 线性测量方法

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Author Spotlight: Improving Beef Cattle Nutrition and Production with a Focus on Feed Efficiency and Meat Quality Traits Through Advanced Biochemical and Molecular Assays
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科学领域:

  • 动物科学动物科学
  • 农业工程 农业工程
  • 食品科学 食品科学 食品科学

背景情况:

  • 准确评估猪肉中的瘦肉和脂肪含量对于分类和加工至关重要.
  • 确定肉类成分的传统方法可能耗时或需要专门的设备.

研究的目的:

  • 开发和验证一种数字图像分析方法,用于预测猪肉骨头中的瘦肉和脂肪百分比.
  • 建立一个基于图像分析预测的分类系统来识别极度瘦或脂肪的火腿.

主要方法:

  • 采用火腿截面的数字图像分析来测量特定的瘦肌和皮下脂肪位置.
  • 从两个脂肪位置的线性测量被用到一步回归来预测双能量X射线 (DXA) 脂肪和瘦肉的百分比.
  • 使用预测方程开发了一个分类系统,以识别DXA脂肪和瘦肉百分比的第10百分点值的火腿.

主要成果:

  • 线性测量预测了DXA脂肪或瘦肉百分比,预测准确度 (R2) 为0.7.7.
  • 该分类系统成功地在第10百分点值确定了极度瘦 (< 60.2%) 和脂肪 (> 32.0%) 的火腿.
  • 将分类值调整到第30百分位数后,脂肪火腿预测的准确性提高了60%,而瘦肉的准确性则降低了18%.

结论:

  • 对火腿截面的数字图像分析提供了一种可靠的方法来预测猪肉成分.
  • 开发的分类系统有可能转化为商业猪肉行业的实际工具.
  • 这种方法提供了评估猪肉质量的非破坏性和有效手段,并促进了商业加工决策.