应用K-means群优化波长选择方法,结合大豆粗脂肪含量检测相似度的相似度
Xi-Yao Feng1, Zheng-Guang Chen1, Jin-Ming Liu1
1College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
Food chemistry
|December 19, 2025
概括
这项研究引入了用于近红外光谱的新波长选择方法 (simKACO),以准确预测大豆脂肪含量. 它通过考虑光谱变量相关性,优于传统方法.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 波长选择中的传统欧几里德距离忽略了光谱变量相关性.
- 准确预测食品质量参数,如脂肪含量至关重要.
研究的目的:
- 为近红外光谱学 (NIRS) 数据开发一个改进的波长选择策略.
- 通过整合K-means,群优化 (ACO) 和相似度测量 (simKACO) 来提高大豆脂肪含量预测的准确性.
主要方法:
- 拟议的simKACO策略集成K-means集群和ACO与相似度量 (共因相似度,相关系数).
- 在NIRS中使用共弦相似性来指导与大豆脂肪含量相关的波长的特征选.
- 开发了KcosACOcos-PLS模型用于预测分析.
主要成果:
- 在simKACO框架内,共弦相似性被证明是最有效的相似性测量方法.
- 在KcosACOcos-PLS模型中,测试组R2达到0.8665.5.
- 与其他特征选择方法 (UVE,IRF,GA,ACO) 相比,表现出优异的预测性能和概括性.
结论:
- simKACO方法,特别是与共弦相似性,为NIRS.中的波长选择提供了一种有效的方法.
- 这一策略提高了预测大豆脂肪含量的准确性.
- 提供了使用光谱分析检测食品质量的宝贵参考.
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