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通过LC×LC-光和数据驱动的化学测量建模进行全面的化学指纹识别,用于无监督分类

Mirta R Alcaraz¹, Elisabet Martín-Tornero¹, Héctor C Goicoechea²

¹Departamento de Química Analítica, Facultad de Ciencias, Universidad de Extremadura, Badajoz, 06006, Spain.

|December 25, 2025

概括

这项研究引入了一种新的二维液态色谱 (LC × LC) 方法用于食品分析. 这种先进的技术,结合化学测量,创建可靠的化学指纹区分食品,如西班牙cavas.

科学领域:

分析化学分析化学
食品科学食品科学食品科学
化学测量化学测量化学测量

背景情况:

全面的二维液态色谱 (LC × LC) 为复杂样品提供了高分辨率的分离.
食品分析传统上依赖于有针对性的分析,这可能会错过关键的化学信息.
化学测量方法对于从复杂的色谱数据中提取有意义的模式至关重要.

研究的目的:

开发和应用一个优化的LC×LC方法,以吸收和光检测为食品分析.
评估LC × LC配置文件作为样品特征化化学指纹的能力.
整合化学测量策略以进行数据驱动的分析和增强化学差异化.

主要方法:

开发和优化一个全面的二维液态色谱 (LC × LC) 协议.
使用吸光 (UV) 和光检测模式.
化学测量技术的应用,包括PARAFAC,主要成分分析 (PCA) 和等级集群分析 (HCA).
通过整合紫外线和光数据来探索中级数据融合.

主要成果:

优化的LC × LC方法证明了食品概况的增强分辨率和分析性能.
与单独的紫外线检测相比,光检测提供了更深入的化学洞察力.
使用PARAFAC分数进行的无监督分析显示了与地理来源,组成和生产者相关的独特集群模式.
紫外线和光信号的数据融合进一步提高了分辨样本的能力.

结论:

LC × LC与化学测量相结合,为食品分析提供了一种强大而非有针对性的方法.
开发的方法提供了有意义的化学差异化,作为传统技术的可靠替代品.
结合互补的检测模式 (紫外线和光) 显著提高复杂食品矩阵的分析能力.

关键词:

吸收和光检测的检测.综合的二维液态染色学数据驱动的分析数据驱动的分析双模式检测检测可以实现.没有监督的分析.

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