过程分析技术
偏最小二乘回归
生物过程
生物系统
工艺工程
电容
过程控制
计算机科学
稳健性(进化)
生化工程
过程(计算)
机器学习
工程类
化学
生物
化学工程
生物化学
电极
物理化学
基因
操作系统
作者
Yanjun Sun,Qiongqiong Zhang,Yunfei He,Dongliang Chen,Zheyu Wang,Xiang Zheng,Mingyue Fang,Hang Zhou
摘要
ABSTRACT Serving as a dedicated process analytical technology (PAT) tool for biomass monitoring and control, the capacitance probe, or dielectric spectroscopy, is showing great potential in robust pharmaceutical manufacturing, especially with the growing interest in integrated continuous bioprocessing. Despite its potential, challenges still exist in terms of its accuracy and applicability, particularly when it is used to monitor cells during stationary and decline phases. In this study, data pre‐processing methods were first evaluated through cross‐validation, where the first‐order derivative emerged as the most effective method to diminish variability in prediction accuracy across different training datasets. Subsequently, a segmented adaptive partial least squares (SA‐PLS) model was developed, and its accuracy and universality were demonstrated through several validation studies using different clones and culture processes. Furthermore, a real‐time viable cell density (VCD) auto‐control system in perfusion culture was established, where the VCD was maintained around the target with notable precision and robustness. This model enhanced the monitoring capabilities of capacitance‐based PAT tools throughout the cultivation, expanded their application in cell‐specific automatic control strategies, and contributed vitally to the advancement of continuous manufacturing paradigms.
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