A novel soft strategy for combination and partition of mass spectra data recorded at different fragmentor voltages in full scan mode of a mass spectrometer was developed to generate abundant multi-way data. It is the first time that non-linear four-way and combined three-way LC-MS data have been obtained simultaneously in a single chromatographic run. This strategy ensures that each analyte can be ionized and detected at the most appropriate MS conditions (ionization modes, fragmentor voltages) and avoids a hard chromatographic segmentation in subsequent chemometric analysis. Two different experimental datasets were analyzed to validate the feasibility and applicability of this strategy. Some simple pretreatments were carried out before LC-MS analysis to prevent potential matrix effects. Proper chemometric tools were used to resolve three-way (partitioned data) and enhanced three-way LC-MS (combined data) data, respectively. The method was assessed by comparing the analytical results obtained from the same chemometric algorithm with both combined and partitioned datasets: (1) the combined data provided the best global overall resolution, higher sensitivity and more reliable results, (2) the partitioned data provided higher selectivity for some specific analytes. The results showed that the developed method could be a soft and ingenious tool to handle the unordered but information-rich raw LC-MS data. Moreover, the proposed strategy could take extra analytical advantages in terms of higher sensitivity and more reliable quantitative results when compared with LC-MS (with single fragmentor voltage) strategy and showed nearly the same capability in analytical quality as classic LC-MS/MS method.

中文翻译：

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数据组合和分区的探索优势：在四重碎裂电压的全扫描模式下首次对液相色谱-质谱数据进行化学计量分析

开发了一种新的软策略，用于在质谱仪的全扫描模式下对在不同碎裂电压下记录的质谱数据进行组合和分区，以生成丰富的多路数据。这是首次在单次色谱运行中同时获得非线性四路和组合三路 LC-MS 数据。该策略确保每种分析物都可以在最合适的 MS 条件（电离模式、碎裂电压）下电离和检测，并避免在随后的化学计量分析中进行硬色谱分割。分析了两个不同的实验数据集以验证该策略的可行性和适用性。在 LC-MS 分析之前进行了一些简单的预处理，以防止潜在的基质效应。使用适当的化学计量学工具分别解析三向（分区数据）和增强型三向 LC-MS（组合数据）数据。该方法通过将相同化学计量算法获得的分析结果与组合数据集和分区数据集进行比较来评估：(1) 组合数据提供了最佳的全局整体分辨率、更高的灵敏度和更可靠的结果，(2) 分区数据提供了更高的某些特定分析物的选择性。结果表明，所开发的方法可以成为处理无序但信息丰富的原始 LC-MS 数据的软而巧妙的工具。而且，