RationaleMass spectrometry imaging (MSI) elevates the power of conventional mass spectrometry (MS) to multidimensional space, elucidating both chemical composition and localization. However, the field lacks any robust quality control (QC) and/or system suitability testing (SST) protocols to monitor inconsistencies during data acquisition, both of which are integral to ensure the validity of experimental results. To satisfy this demand in the community, we propose an adaptable QC/SST approach with five analyte options amendable to various ionization MSI platforms (e.g., desorption electrospray ionization, matrix‐assisted laser desorption/ionization [MALDI], MALDI‐2, and infrared matrix‐assisted laser desorption electrospray ionization [IR‐MALDESI]).MethodsA novel QC mix was sprayed across glass slides to collect QC/SST regions‐of‐interest (ROIs). Data were collected under optimal conditions and on a compromised instrument to construct and refine the principal component analysis (PCA) model in R. Metrics, including mass measurement accuracy and spectral accuracy, were evaluated, yielding an individual suitability score for each compound. The average of these scores is utilized to inform if troubleshooting is necessary.ResultsThe PCA‐based SST model was applied to data collected when the instrument was compromised. The resultant SST scores were used to determine a statistically significant threshold, which was defined as 0.93 for IR‐MALDESI‐MSI analyses. This minimizes the type‐I error rate, where the QC/SST would report the platform to be in working condition when cleaning is actually necessary. Further, data scored after a partial cleaning demonstrate the importance of QC and frequent full instrument cleaning.ConclusionsThis study is the starting point for addressing an important issue and will undergo future development to improve the efficiency of the protocol. Ultimately, this work is the first of its kind and proposes this approach as a proof of concept to develop and implement universal QC/SST protocols for a variety of MSI platforms.

中文翻译：

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质谱成像系统适用性测试的统计方法

基本原理质谱成像 (MSI) 将传统质谱 (MS) 的能力提升到多维空间，阐明化学成分和定位。然而，该领域缺乏任何强大的质量控制（QC）和/或系统适用性测试（SST）协议来监控数据采集过程中的不一致性，这两者对于确保实验结果的有效性都是不可或缺的。为了满足社区的这一需求，我们提出了一种适应性强的 QC/SST 方法，该方法具有五种分析物选项，可修改为各种电离 MSI 平台（例如解吸电喷雾电离、基质辅助激光解吸/电离 [MALDI]、MALDI-2 和红外基质辅助激光解吸电喷雾电离 [IR-MALDESI])。方法将一种新型 QC 混合物喷洒在载玻片上以收集 QC/SST 感兴趣区域 (ROI)。在最佳条件下，在受损的仪器上收集数据，以构建和完善 R 中的主成分分析 (PCA) 模型。对指标（包括质量测量精度和光谱精度）进行了评估，从而得出每种化合物的单独适用性评分。这些分数的平均值用于告知是否需要进行故障排除。结果基于 PCA 的 SST 模型应用于仪器受到损害时收集的数据。由此产生的 SST 评分用于确定统计学上显着的阈值，IR-MALDESI-MSI 分析的阈值定义为 0.93。这最大限度地减少了 I 类错误率，当实际需要清洁时，QC/SST 会报告平台处于工作状态。此外，部分清洁后评分的数据证明了质量控制和频繁全面仪器清洁的重要性。结论这项研究是解决一个重要问题的起点，并将在未来进行发展以提高方案的效率。最终，这项工作是此类工作中的首例，并提出这种方法作为概念验证，为各种 MSI 平台开发和实施通用 QC/SST 协议。