Analytical Chemistry Laboratory, Research Institute for Chemical and Biological Analyses, University of Santiago de Compostela, Santiago, Spain.
Mestrelab Research S.L. Santiago de Compostela, Santiago, Spain.
Anal Bioanal Chem. 2022 Jan;414(1):587-600. doi: 10.1007/s00216-021-03538-7. Epub 2021 Aug 18.
A new strategy for the computer-assisted methods development in the reversed-phase liquid chromatographic separations of unknown sample mixtures has been developed using the latent spectral information in chromatogram raw data files of appropriately designed experiments, rather than resorting to elemental information functions (e.g., the number of peaks in chromatograms or similar criteria). The strategy developed allows unification of the approach for samples of both known and unknown composition and, thus, provide a general strategy for computer-aided tools in the chromatography laboratory. The operation principle of this strategy departs from extracting the spectra of components in the mixture chromatograms by resorting to multivariate curve resolution-alternating least squares (MCR-ALS). This technique allows the estimation of the true spectra for the individual components except when they have identical spectra or are fully overlapped. Thus, a convenient experimental design will try to perform separations of the sample mixture having at least partial resolution of components in some runs. This will allow estimating the spectra of components and, then, assign these components to the peaks in each run chromatogram. In this way, a retention model can be built for each component so computerized optimization process can be developed to provide the chromatographer with the best possible separation programs. Following this approach, strategies for sample mixtures of known and unknown composition are only different in the need of an initial spectrum discovery process for unknown mixtures and therefore a real general approach for the computer-assisted LC methods development is now available for the first time.
已经开发出一种新的策略，用于在反相液相色谱分离未知样品混合物的计算机辅助方法开发中，该策略利用适当设计的实验的色谱原始数据文件中的潜在光谱信息，而不是诉诸于元素信息函数（例如，色谱图中的峰数或类似标准）。所开发的策略允许对已知和未知组成的样品进行统一处理，从而为色谱实验室中的计算机辅助工具提供了一种通用策略。该策略的操作原理是通过多元曲线分辨交替最小二乘法（MCR-ALS）来提取混合物色谱中各组分的光谱。该技术允许估计各组分的真实光谱，除非它们的光谱相同或完全重叠。因此，方便的实验设计将尝试在某些运行中至少部分分离样品混合物中的组分。这将允许估计组分的光谱，然后将这些组分分配到每个运行的色谱图中的峰上。通过这种方式，可以为每个组分建立保留模型，从而可以开发计算机化的优化过程，为色谱师提供最佳的分离程序。按照这种方法，对于已知和未知组成的样品混合物，策略仅在未知混合物需要初始光谱发现过程方面有所不同，因此，现在首次为计算机辅助 LC 方法开发提供了真正的通用方法。
