Raman spectroscopy has found widespread usage in monitoring cell culture processes both in research and practical applications. However, commonly, preprocessing methods, spectral regions, and modeling parameters have been chosen based on experience or trial‐and‐error strategies. These choices can significantly impact the performance of the models. There is an urgent need for a simple, effective, and automated approach to determine a suitable procedure for constructing accurate models. This paper introduces the adoption of a design of experiment (DoE) method to optimize partial least squares models for measuring the concentration of different components in cell culture bioreactors. The experimental implementation utilized the orthogonal test table L25(56). Within this framework, five factors were identified as control variables for the DoE method: the window width of Savitzky–Golay smoothing, the baseline correction method, the order of preprocessing steps, spectral regions, and the number of latent variables. The evaluation method for the model was considered as a factor subject to noise. The optimal combination of levels was determined through the signal‐to‐noise ratio response table employing Taguchi analysis. The effectiveness of this approach was validated through two cases, involving different cultivation scales, different Raman spectrometers, and different analytical components. The results consistently demonstrated that the proposed approach closely approximated the global optimum, regardless of data set size, predictive components, or the brand of Raman spectrometer. The performance of models recommended by the DoE strategy consistently surpassed those built using raw data, underscoring the reliability of models generated through this approach. When compared to exhaustive all‐combination experiments, the DoE approach significantly reduces calculation times, making it highly practical for the implementation of Raman spectroscopy in bioprocess monitoring.

中文翻译：

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增强实时细胞培养监测：使用田口方法自动优化拉曼模型

拉曼光谱在研究和实际应用中广泛用于监测细胞培养过程。然而，通常，预处理方法、光谱区域和建模参数是根据经验或试错策略来选择的。这些选择可以显着影响模型的性能。迫切需要一种简单、有效和自动化的方法来确定构建准确模型的合适程序。本文介绍了采用实验设计（DoE）方法来优化偏最小二乘模型，用于测量细胞培养生物反应器中不同成分的浓度。实验实现利用正交试验表L25(56）。在此框架内，确定了五个因素作为 DoE 方法的控制变量：Savitzky-Golay 平滑的窗口宽度、基线校正方法、预处理步骤的顺序、光谱区域和潜在变量的数量。模型的评估方法被认为是受噪声影响的因素。通过采用田口分析的信噪比响应表确定最佳水平组合。通过两个不同种植规模、不同拉曼光谱仪和不同分析组件的案例验证了该方法的有效性。结果一致表明，无论数据集大小、预测组件或拉曼光谱仪的品牌如何，所提出的方法都非常接近全局最优值。美国能源部策略推荐的模型的性能始终超过使用原始数据构建的模型，强调了通过这种方法生成的模型的可靠性。与详尽的全组合实验相比，DoE 方法显着减少了计算时间，使其对于在生物过程监测中实施拉曼光谱非常实用。