验证拉曼光谱模型,以验证不同生产系统下种植的澳大利亚牛肉的原产地
Stephanie M Fowler1, Matthew Kerr2, David L Hopkins3
1Centre for Red Meat and Sheep Development, NSW Department of Primary Industries, Cowra, Australia; Gulbali Institute, Charles Sturt University, Wagga Wagga, NSW, Australia.
Meat science
|May 1, 2024
概括
拉曼光谱学准确地分类了草牛肉与谷物牛肉 (高达95.7%),有助于原产地验证. 然而,南澳大利亚牛的模型显示精度较低 (73.2%),表明需要进一步研究.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
背景情况:
- 由于健康和环境问题,消费者对经过验证的牛肉生产系统的需求正在增加.
- 拉曼光谱已经显示出区分牛养系统 (草与谷物) 的潜力.
- 认证牛肉原产地对于消费者信任和市场透明度至关重要.
研究的目的:
- 验证现有的部分最小平方差异分析 (PLS-DA) 模型用于牛肉分类.
- 评估这些模型的稳定性,使用来自不同澳大利亚生产系统的独立尸体样本.
- 为了确定拉曼光谱在区分草和谷物养牛肉方面的准确性.
主要方法:
- 利用了使用手持拉曼装置收集的585具尸体脂肪样本的光谱数据.
- 采用部分最小平方差分分析 (PLS-DA) 来构建和验证两类分类模型 (草和谷物养).
- 相关的光谱数据与脂肪酸分析进行全面评估.
主要成果:
- 实现了高的预测准确度,高达95.7%的牛肉被分类为草或谷物养.
- 观察到预测能力的显著变化,南澳大利亚牛的模型只达到73.2%的准确性.
- 确定了潜在的变异来源,包括遗传学,管理,饮食和未知因素,如食时间.
结论:
- 拉曼光谱与PLS-DA相结合,为验证牛肉生产系统和来源提供了一个有前途的工具.
- 模型性能可能在地理位置上有所不同,因此需要对特定区域进行校准和验证.
- 需要进一步的研究来了解养时间对光谱数据和模型准确性的影响.
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