Protein production in the biopharmaceutical industry necessitates the utilization of multiple analytical techniques and control methodologies to ensure both safety and consistency. To facilitate real‐time monitoring and control of cell culture processes, Raman spectroscopy has emerged as a versatile analytical technology. This technique, categorized as a Process Analytical Technology, employs chemometric models to establish correlations between Raman signals and key variables of interest. One notable approach for achieving real‐time monitoring is through the application of just‐in‐time learning (JITL), an industrial soft sensor modeling technique that utilizes Raman signals to estimate process variables promptly. The conventional Raman‐based JITL method relies on the K‐nearest neighbor (KNN) algorithm with Euclidean distance as the similarity measure. However, it falls short of addressing the impact of data uncertainties. To rectify this limitation, this study endeavors to integrate JITL with a variational autoencoder (VAE). This integration aims to extract dominant Raman features in a nonlinear fashion, which are expressed as multivariate Gaussian distributions. Three experimental runs using different cell lines were chosen to compare the performance of the proposed algorithm with commonly utilized methods in the literature. The findings indicate that the VAE–JITL approach consistently outperforms partial least squares, convolutional neural network, and JITL with KNN similarity measure in accurately predicting key process variables.

中文翻译：

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将即时学习与变分自动编码器相结合，用于基于拉曼光谱的细胞培养过程监控

生物制药行业的蛋白质生产需要利用多种分析技术和控制方法来确保安全性和一致性。为了促进细胞培养过程的实时监测和控制，拉曼光谱已成为一种多功能分析技术。该技术被归类为过程分析技术，采用化学计量模型来建立拉曼信号和感兴趣的关键变量之间的相关性。实现实时监控的一个值得注意的方法是应用即时学习（JITL），这是一种工业软传感器建模技术，利用拉曼信号快速估计过程变量。传统的基于拉曼的 JITL 方法依赖于 K 最近邻（KNN）算法，以欧几里德距离作为相似性度量。然而，它未能解决数据不确定性的影响。为了纠正这一限制，本研究致力于将 JITL 与变分自动编码器 (VAE) 集成。这种集成旨在以非线性方式提取主要拉曼特征，这些特征表示为多元高斯分布。选择使用不同细胞系的三个实验运行来比较所提出的算法与文献中常用方法的性能。研究结果表明，VAE-JITL 方法在准确预测关键过程变量方面始终优于偏最小二乘法、卷积神经网络和采用 KNN 相似性度量的 JITL。