Cell Culture Media and Raman Spectra Preprocessing Procedures Impact Glucose Chemometrics

IF 2.1 4区 化学 Q1 SOCIAL WORK
Naresh Pavurala, Chikkathur N. Madhavarao, Jaeweon Lee, Jayanti Das, Muhammad Ashraf, Thomas O'Connor
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Abstract

Deployment of process analytical technology tools such as Raman or IR spectroscopy and associated multivariate calibration models for process monitoring and control plays an important role in process automation and advanced manufacturing of pharmaceuticals. Preprocessing or preparation of the spectroscopic data is an important step in developing a multivariate calibration model. There are several ways available to preprocess the data and each may influence the calibration model performance differently. Here we investigated the influence of preprocessing procedures on the development and performance of the chemometric models to predict the glucose concentration in a bioreactor. Box–Behnken design of experiment (DOE) was used to generate the Raman spectroscopy data. Four factors were considered critical in the DOE—glucose, glutamine, glutamic acid, and antifoam concentration. Raman spectroscopy data were collected both with and without aeration conditions, independently from three cell culture media. For each medium, data consisted of calibration set (27 conditions) and model validation set (9 conditions) separately. Additionally, Raman data was also collected for certain DOE runs with increasing concentration of cell densities ranging from 0.5 × 10 E06/mL to 30 × 10 E06/mL under aerating conditions. Data from the three cell culture media were used separately to develop calibration models that used four different preprocessing procedures, namely, baseline correction (BLC), Savitzky–Golay smoothing (SGS), Savitzky–Golay derivative (SGD) and orthogonal signal correction (OSC). The preprocessing procedures were applied individually and in combinations to evaluate the calibration model parameters and the performance metrics. We further developed glucose calibration models based on partial least squares (PLS) regression with 1–3 principal components. The models developed with OSC procedure gave superior performance metrics with just one principal component across all three media. Models developed with other preprocessing procedures required two or more principal components to give comparable performance. Overall, the choice of preprocessing procedures affected the model performance.

细胞培养基和拉曼光谱预处理程序影响葡萄糖化学计量学
过程分析技术工具的部署,如拉曼或红外光谱以及相关的多变量校准模型,用于过程监测和控制,在过程自动化和先进制药制造中发挥着重要作用。光谱数据的预处理或准备是建立多元校准模型的重要步骤。有几种方法可用于预处理数据,每种方法对校准模型性能的影响不同。在这里,我们研究了预处理程序对化学计量模型的发展和性能的影响,以预测生物反应器中的葡萄糖浓度。采用Box-Behnken实验设计(DOE)生成拉曼光谱数据。四个因素被认为是影响doe的关键因素——葡萄糖、谷氨酰胺、谷氨酸和消泡剂浓度。拉曼光谱数据在有和没有曝气条件下收集,独立于三种细胞培养基。每种培养基的数据分别由校准集(27个条件)和模型验证集(9个条件)组成。此外,在曝气条件下,当细胞密度从0.5 × 10 E06/mL增加到30 × 10 E06/mL时,还收集了某些DOE运行的拉曼数据。分别使用三种细胞培养基的数据建立校准模型,使用四种不同的预处理程序,即基线校正(BLC)、Savitzky-Golay平滑(SGS)、Savitzky-Golay导数(SGD)和正交信号校正(OSC)。预处理程序分别和组合应用,以评估校准模型参数和性能指标。我们进一步建立了基于1-3个主成分的偏最小二乘(PLS)回归的葡萄糖校准模型。使用OSC程序开发的模型在所有三种媒体中仅使用一个主成分就提供了优越的性能指标。使用其他预处理程序开发的模型需要两个或更多的主要组件才能提供可比的性能。总的来说,预处理程序的选择影响了模型的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Chemometrics
Journal of Chemometrics 化学-分析化学
CiteScore
5.20
自引率
8.30%
发文量
78
审稿时长
2 months
期刊介绍: The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.
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