基于多光谱数据融合策略的专用的来源的快速分辨分析
Xin Gao1, Wenliang Dong2, Zehua Ying2
1College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, PR China.
Food chemistry
|August 8, 2024
概括
一个使用近红外,中红外和拉曼光谱的新模型准确地识别了河北的起源. 这种非破坏性方法为食品和制药品质量控制和品牌保护提供了快速可行的工具.
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:
- 准确识别河北等农产品对于质量控制和防止欺诈至关重要.
- 传统的原产地识别方法可能耗时且具有破坏性.
- 光谱技术为快速,非破坏性分析提供了潜力.
研究的目的:
- 开发一个快速有效的定性歧视模型,用于河北山的识别.
- 评估不同频谱数据融合策略的性能,以进行原产地分类.
- 建立一个非破坏性的方法,用于地理原产地分类和品牌保护的河北.
主要方法:
- 获得了近红外 (NIR),中红外 (MIR) 和微观拉曼光谱.
- 使用了单个光谱和多光谱数据融合策略.
- 一个灰狼优化器支持向量机 (GWO-SVM) 模型是使用三个光谱的特征级融合构建的.
主要成果:
- 使用NIR,MIR和拉曼光谱的中级融合的GWO-SVM模型在训练和测试集上实现了100.00%的预测准确度.
- 获得了1.00的F1得分,表明了完美的分类表现.
- 证明了NIR,MIR和Raman之间的光谱互补性.
结论:
- 结合NIR,MIR和拉曼光谱与特征级融合提供了一个强大的,非破坏性的,快速的,可行的工具,用于河北原产地分类.
- 这种方法可以帮助地理来源分类和品牌保护.
- 该方法显示出在食品和制药行业的原产地识别和质量监测方面的潜力.
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