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数据融合 - - 一个有效的工具,用于开发食品认证认证模型的有效工具

Ariana Raluca Magdas¹, Maria David¹, Camelia Berghian-Grosan²

¹National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania; Doctoral School in Physics, Faculty of Physics, Babeș-Bolyai University, M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|November 29, 2025

概括

这项研究通过融合来自减弱总反射里埃变换红外 (ATR-FT-IR) 和里埃变换拉曼 (FT-Raman) 光谱的数据来增强蜂蜜识别. 结合方法提高了模型的可靠性,通过植物来源和收获年来区分蜂蜜.

科学领域:

分析化学分析化学
食品科学食品科学食品科学
频谱学是一种光谱学.

背景情况:

可靠的食品和饮料识别模型具有重要的研究兴趣.
数据处理对于提取相关信息至关重要,特别是从复杂的样本中提取相关信息.
数据融合技术通过结合多种分析方法的输出来提高预测模型的可靠性.

研究的目的:

通过数据融合开发改进的蜂蜜识别模型.
根据植物来源和收获年份来区分蜂蜜.
将来自互补振动光谱技术的信息关联起来.

主要方法:

使用了减弱总反射里埃变换红外光谱 (ATR-FT-IR).
雇佣的富里埃变换拉曼 (FT-拉曼) 光谱.
应用低级和中级数据融合策略.

主要成果:

开发了用于蜂蜜识别的增强差异化模型.
成功地根据植物来源对蜂蜜样本进行了区分.
通过收获年份成功区分蜂蜜样本.

结论:

通过ATR-FT-IR和FT-Raman光谱的数据融合,提高了蜂蜜识别模型的可靠性.
综合光谱方法对于按来源和年份分类蜂蜜是有效的.
这种方法为食品认证和质量控制提供了一个强大的战略.

关键词:

数据融合数据融合蜂蜜蜂蜜蜂蜜蜂蜜红外光谱法红外光谱法拉曼·拉曼,拉曼·拉曼,拉曼·拉曼.识别模型的识别模型

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