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在使用优化算法对各种牲畜肉样本的超光谱图像中增强预测总纯素含量

Sijia Liu¹, Jiarui Cui², Yu Lv²

¹School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; School of Food Science and Engineering, Northwest University, Xi'an 710000, China.

Food research international (Ottawa, Ont.)

|March 3, 2025

概括

这项研究开发了快速检测牲畜肉中使用高光谱成像 (HSI) 检测总纯素的方法. 优化的算法实现了准确的预测,有助于创建低蛋白饮食和在线肉质监测.

科学领域:

食品科学食品科学食品科学
分析化学分析化学
生物技术是生物技术.

背景情况:

牲畜肉中的纯素含量是制定低纯素饮食的关键因素.
为了饮食管理和肉类质量控制,需要准确和快速检测总纯素的方法.

研究的目的:

开发快速检测和算法优化动物肉的纯素总含量.
通过精确的肉类分析,支持创建低清素饮食.
通过使用光谱数据,探索混合牲畜肉中的纯素变异.

主要方法:

使用化学和高光谱数据 (Vis-NIR HSI和NIR-HSI) 构建总的预测模型.
使用豪斯多夫距离和皮尔森相关系数,分析物种之间的光谱曲线相关性.
优化预测模型使用先进的算法,如SSA-Bi-LSTM-MHA和代变量选择.

主要成果:

在混合牲畜肉中,总纯素的最佳预测模型是R²值为0.7820 (Vis-NIR HSI) 和0.7766 (NIR-HSI).
由于计算复杂度增加,混合样本的模型性能被验证为低于单个样本.
超光谱成像 (HSI) 显示了快速检测纯素含量的显著潜力.

结论:

HSI技术有效地快速检测牲畜肉中的纯素含量.
优化的算法,特别是SSA-Bi-LSTM-MHA,对精确的纯素量化有很大的希望.
这项研究促进了在线监测牲畜肉质量的工业化发展,并支持饮食干预措施.

关键词:

算法优化的算法优化相关性分析是一项相关性分析.数据融合数据融合超光谱成像技术的技术.这就是SSA-Bi-LSTM-MHA.

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