Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics.

色谱系统结合质谱检测技术被广泛用于生物学研究中，研究生物分子的水平如何响应不同的内部和外部刺激。通常预期这种变化幅度很小，因此需要理解和最小化所有可能影响分析的实验因素。通常会观察到运行顺序效应，这对基于色谱-质谱联用的分析研究构成了重大挑战，在对测量数据进行生物学评估之前，需要解决该问题。到目前为止，还没有建立的共识，度量或方法可以快速估计这种影响的程度。在本文中，我们演示了如何将对潜在结构的正交投影（OPLS®）用于分析研究中运行顺序效果的客观量化。量化指标表示为与运行顺序相关的实验数据中的变化量。这种方法的主要优点之一是，它为量化所有检测到的特征（不仅是内部标准）的运行顺序效果提供了一种快速方法。由OPLS提供的对运行顺序影响进行量化得到的结果可用于数据归一化评估，支持分析方案的优化以及鉴定受仪器漂移影响较大的化合物。在三个分析平台（GCMS代谢组学，LCMS代谢组学和LCMS脂质组学）上进行的血浆谱分析实验数据证明了OPLS在定量分析运行顺序中的应用。