Advanced process control in the biopharmaceutical industry often lacks real-time measurements due to resource constraints. Raman spectroscopy and Partial Least Squares (PLS) models are often used to monitor bioprocess cultures in real-time. In spite of the ease of training, the accuracy of the PLS model is impacted if it is not used to predict quality attributes for the cell lines it is trained on. To address this issue, a deep convolutional neural network (CNN) is proposed for offline modeling of metabolites using Raman spectroscopy. By utilizing asymmetric least squares smoothing to adjust Raman spectra baselines, a generic training data set is created by amalgamating spectra from various cell lines and operating conditions. This data set, combined with their derivatives, forms a two-dimensional model input. The CNN model is developed and validated for predicting different quality variables against measurements from various continuous and fed-batch experimental runs. Validation results confirm that the deep CNN model is an accurate generic model of the process to predict real-time quality attributes, even in experimental runs not included in the training data. This model is robust and versatile, requiring no recalibration when deployed at different sites to monitor various cell lines and experimental runs.

中文翻译：

---

使用卷积神经网络和拉曼光谱预测细胞培养产品质量属性

由于资源限制，生物制药行业的先进过程控制通常缺乏实时测量。拉曼光谱和偏最小二乘 (PLS) 模型通常用于实时监测生物过程培养物。尽管训练很容易，但如果不使用 PLS 模型来预测其所训练的细胞系的质量属性，则 PLS 模型的准确性会受到影响。为了解决这个问题，提出了一种深度卷积神经网络（CNN），用于使用拉曼光谱对代谢物进行离线建模。通过利用非对称最小二乘平滑来调整拉曼光谱基线，通过合并来自各种细胞系和操作条件的光谱来创建通用训练数据集。该数据集与其导数相结合，形成二维模型输入。 CNN 模型的开发和验证用于根据各种连续和分批补料实验运行的测量结果来预测不同的质量变量。验证结果证实，深度 CNN 模型是预测实时质量属性过程的准确通用模型，即使在训练数据中未包含的实验运行中也是如此。该模型稳健且通用，部署在不同地点以监测各种细胞系和实验运行时无需重新校准。