Abstract：
BACKGROUND: Enhancing the quality control of medicinal plants is a complex challenge due to their rich variety of chemical compounds present at varying and extreme concentrations. Chromatographic fingerprints, which have become essential for characterising these complex natural materials, require achieving optimal separation conditions to effectively maximise the number of detected peaks. The challenges in optimising fingerprints and other complex multi-analyte samples include the unavailability of standards, the presence of unknown constituents and the substantial workload that would require conventional optimisation methods based on models.
RESULTS: This work introduces an interpretive optimisation approach which operates on the premise of predicting chromatograms using global models. Initially, a multi-linear gradient experimental design is sequentially executed to accommodate all peaks in the chromatogram in an adequate time window. Following this, a small set of sample peaks (reference peaks) is selected based on their consistent traceability across all chromatograms in the design. Using this reference dataset, a global model is constructed, initially focused solely on the reference peaks and later extended to encompass all detected peaks in the sample. The aim is to find gradients that maximise resolution while minimising analysis time. These optimised gradients are applied successfully to enhance the separation of medicinal plant extracts, with particular emphasis on peppermint and pennyroyal extracts.
CONCLUSIONS: The proposed optimisation relying on global models can be applied to highly complex samples even in the absence of standards, or in cases where standards are available but their use is impractical due to workload constraints. Moreover, in discerning the most promising gradients for highly complex samples, peak purity has demonstrated superior reliability and competitiveness compared to peak capacity as chromatographic objective function.
RESULTS: This work introduces an interpretive optimisation approach which operates on the premise of predicting chromatograms using global models. Initially, a multi-linear gradient experimental design is sequentially executed to accommodate all peaks in the chromatogram in an adequate time window. Following this, a small set of sample peaks (reference peaks) is selected based on their consistent traceability across all chromatograms in the design. Using this reference dataset, a global model is constructed, initially focused solely on the reference peaks and later extended to encompass all detected peaks in the sample. The aim is to find gradients that maximise resolution while minimising analysis time. These optimised gradients are applied successfully to enhance the separation of medicinal plant extracts, with particular emphasis on peppermint and pennyroyal extracts.
CONCLUSIONS: The proposed optimisation relying on global models can be applied to highly complex samples even in the absence of standards, or in cases where standards are available but their use is impractical due to workload constraints. Moreover, in discerning the most promising gradients for highly complex samples, peak purity has demonstrated superior reliability and competitiveness compared to peak capacity as chromatographic objective function.
摘要：
背景：增强药用植物的质量控制是一个复杂的挑战，因为它们以不同和极端的浓度存在丰富的各种化合物。色谱指纹图谱,这对于表征这些复杂的天然材料至关重要，需要实现最佳分离条件以有效地最大化检测到的峰的数量。优化指纹和其他复杂的多分析物样品的挑战包括标准品的不可用性，未知成分的存在和需要基于模型的常规优化方法的大量工作量。
结果：这项工作引入了一种解释性优化方法，该方法在使用全局模型预测色谱图的前提下运行。最初，顺序执行多线性梯度实验设计以在适当的时间窗口内适应色谱图中的所有峰。在此之后,一小组样品峰(参考峰)是基于它们在设计中的所有色谱图中的一致可追溯性而选择的。使用此参考数据集，构建了一个全局模型，最初仅关注参考峰，后来扩展到涵盖样品中所有检测到的峰。目的是找到最大化分辨率同时最小化分析时间的梯度。这些优化的梯度被成功地应用于增强药用植物提取物的分离，特别强调薄荷和pennyroyal提取物。
结论：建议的基于全局模型的优化可应用于高度复杂的样品，即使没有标准，或在标准可用但由于工作量限制而使用不切实际的情况下。此外，在辨别高度复杂样品最有希望的梯度时，与作为色谱目标函数的峰容量相比，峰纯度已显示出优越的可靠性和竞争力。
结果：这项工作引入了一种解释性优化方法，该方法在使用全局模型预测色谱图的前提下运行。最初，顺序执行多线性梯度实验设计以在适当的时间窗口内适应色谱图中的所有峰。在此之后,一小组样品峰(参考峰)是基于它们在设计中的所有色谱图中的一致可追溯性而选择的。使用此参考数据集，构建了一个全局模型，最初仅关注参考峰，后来扩展到涵盖样品中所有检测到的峰。目的是找到最大化分辨率同时最小化分析时间的梯度。这些优化的梯度被成功地应用于增强药用植物提取物的分离，特别强调薄荷和pennyroyal提取物。
结论：建议的基于全局模型的优化可应用于高度复杂的样品，即使没有标准，或在标准可用但由于工作量限制而使用不切实际的情况下。此外，在辨别高度复杂样品最有希望的梯度时，与作为色谱目标函数的峰容量相比，峰纯度已显示出优越的可靠性和竞争力。
