Protein therapeutics have numerous critical quality attributes (CQA) that must be evaluated to ensure safety and efficacy, including the requirement to adopt and retain the correct three-dimensional fold without forming unintended aggregates. Therefore, the ability to monitor protein higher order structure (HOS) can be valuable throughout the lifecycle of a protein therapeutic, from development to manufacture. 2D NMR has been introduced as a robust and precise tool to assess the HOS of a protein biotherapeutic. A common use case is to decide whether two groups of spectra are substantially different, as an indicator of difference in HOS. We demonstrate a quantitative use of principal component analysis (PCA) scores to perform this decision-making, and demonstrate the effect of acquisition and processing details on class separation using samples of NISTmAb monoclonal antibody Reference Material subjected to two different oxidative stress protocols. The work introduces an approach to computing similarity from PCA scores based upon the technique of histogram intersection, a method originally developed for retrieval of images from large databases. Results show that class separation can be robust with respect to random noise, reconstruction method, and analysis region selection. By contrast, details such as baseline distortion can have a pronounced effect, and so must be controlled carefully. Since the classification approach can be performed without the need to identify peaks, results suggest that it is possible to use even more efficient measurement strategies that do not produce spectra that can be analyzed visually, but nevertheless allow useful decision-making that is objective and automated.

蛋白质治疗剂具有众多关键质量属性（CQA），必须对其进行评估以确保安全性和有效性，包括要求采用和保留正确的三维折叠而不形成意外的聚集体。因此，从开发到生产的整个过程中，监测蛋白质高阶结构（HOS）的能力都非常有价值。已引入2D NMR作为一种强大而精确的工具来评估蛋白质生物治疗剂的HOS。一个常见的用例是确定两组光谱是否基本不同，以作为HOS差异的指标。我们演示了定量使用主成分分析（PCA）分数来执行此决策的过程，并展示了使用经过两种不同氧化应激实验的NISTmAb单克隆抗体参考材料样品采集和处理细节对类分离的影响。这项工作介绍了一种基于直方图相交技术，根据PCA分数计算相似度的方法，该方法最初是为从大型数据库中检索图像而开发的。结果表明，在随机噪声，重构方法和分析区域选择方面，类分离可能很健壮。相反，诸如基线失真之类的细节可能会产生明显的影响，因此必须谨慎控制。由于无需识别峰即可执行分类方法，