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首页> 美国卫生研究院文献>Engineering in Life Sciences >Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods
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Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods

机译：利用拉曼光谱和多元校正方法对巴斯德毕赤酵母进行制药生产过程的高级监控

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摘要

This contribution includes an investigation of the applicability of Raman spectroscopy as a PAT analyzer in cyclic production processes of a potential Malaria vaccine with . In a feasibility study, Partial Least Squares Regression (PLSR) models were created off‐line for cell density and concentrations of glycerol, methanol, ammonia and total secreted protein. Relative cross validation errors RMSE range from 2.87% (glycerol) to 11.0% (ammonia). In the following, on‐line bioprocess monitoring was tested for cell density and glycerol concentration. By using the nonlinear Support Vector Regression (SVR) method instead of PLSR, the error RMSE for cell density was reduced from 5.01 to 2.94%. The high potential of Raman spectroscopy in combination with multivariate calibration methods was demonstrated by the implementation of a closed loop control for glycerol concentration using PLSR. The strong nonlinear behavior of exponentially increasing control disturbances was met with a feed‐forward control and adaptive correction of control parameters. In general the control procedure works very well for low cell densities. Unfortunately, PLSR models for glycerol concentration are strongly influenced by a correlation with the cell density. This leads to a failure in substrate prediction, which in turn prevents substrate control at cell densities above 16 g/L.
机译：这项贡献包括研究拉曼光谱作为PAT分析仪在潜在的疟疾疫苗的循环生产过程中的应用。在一项可行性研究中，离线建立了偏最小二乘回归（PLSR）模型，用于细胞密度以及甘油，甲醇，氨和总分泌蛋白的浓度。相对交叉验证误差RMSE范围从2.87％（甘油）到11.0％（氨）。接下来，测试了在线生物过程监测的细胞密度和甘油浓度。通过使用非线性支持向量回归（SVR）方法代替PLSR，单元密度的误差RMSE从5.01降低到2.94％。通过使用PLSR对甘油浓度进行闭环控制，证明了拉曼光谱法与多元校准方法相结合的高潜力。前馈控制和控制参数的自适应校正满足了指数级增加的控制扰动的强烈非线性行为。通常，对于低细胞密度，控制程序效果很好。不幸的是，甘油浓度的PLSR模型受到细胞密度相关性的强烈影响。这导致底物预测的失败，继而阻止了以高于16 g / L的细胞密度进行底物控制。

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