LASIR CNRS UMR 8516, Université de Lille, Sciences et Technologies, 59655 Villeneuve d'Ascq Cedex, France.
Sanofi Pasteur, 1541 Avenue Marcel Mérieux, 69280 Marcy-l'Étoile, France.
Anal Chim Acta. 2017 Feb 1;952:9-17. doi: 10.1016/j.aca.2016.11.064. Epub 2016 Dec 2.
Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry.
多元统计过程控制（MSPC）越来越受欢迎，因为分析仪器（如拉曼光谱）提供的大型多元数据集为生物制药行业中复杂细胞培养的监测带来了挑战。然而，拉曼光谱在线监测通常会产生不同步的数据组，导致批次时间变化。此外，由于光谱测量通常以交替方式记录，并且多个光学探头并行连接到同一光谱仪，因此细胞培养监测中常见不同步的数据组。对于多元分析（如多向主成分分析（MPCA））在 MSPC 监测中的应用，同步批次是前提条件。相关优化变形（COW）是一种常用的数据对齐方法，具有令人满意的性能；然而，它以前从未应用于 MSPC 应用中的光谱数据集采集时间的同步。在本文中，我们首次提出使用 COW 方法对具有不同持续时间的批次进行同步分析，该方法使用拉曼光谱进行分析。在第二步中，我们根据 COW 同步的正常操作条件（NOC）批次，在不同的时间间隔开发 MPCA 模型。最后，根据相应的 MPCA 模型对新批次进行预测。我们使用基于 Hotelling 的 T 和 Q 的两个多元控制图监测批次的演变。结果表明，MSPC 模型能够识别异常操作条件，包括污染批次，这在细胞培养监测中非常重要。我们证明了基于拉曼的 MSPC 监测可用于诊断偏离正常条件的批次，比传统诊断更有效，这将为生物制药行业节省时间和金钱。
