Abstract：
This work aimed to set inline Raman spectroscopy models to monitor biochemically (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, and ammonium) all upstream stages of a virus-like particle-making process. Linear (Partial least squares, PLS; Principal components regression, PCR) and nonlinear (Artificial neural networks, ANN; supported vector machine, SVM) modeling approaches were assessed. The nonlinear models, ANN and SVM, were the more suitable models with the lowest absolute errors. The mean absolute error of the best models within the assessed parameter ranges for viable cell density (0.01-8.83 × 106 cells/mL), cell viability (1.3-100.0 %), glucose (5.22-10.93 g/L), lactate (18.6-152.7 mg/L), glutamine (158-1761 mg/L), glutamate (807.6-2159.7 mg/L), and ammonium (62.8-117.8 mg/L) were 1.55 ± 1.37 × 106 cells/mL (ANN), 5.01 ± 4.93 % (ANN), 0.27 ± 0.22 g/L (SVM), 4.7 ± 2.6 mg/L (SVM), 51 ± 49 mg/L (ANN), 57 ± 39 mg/L (SVM) and 2.0 ± 1.8 mg/L (ANN), respectively. The errors achieved, and best-fitted models were like those for the same bioprocess using offline data and others, which utilized inline spectra for mammalian cell lines as a host.
摘要：
这项工作旨在设置内联拉曼光谱模型以监测生化(活细胞密度，细胞活力，葡萄糖，乳酸，谷氨酰胺,谷氨酸,和铵）病毒样颗粒制造过程的所有上游阶段。线性(偏最小二乘，PLS；主成分回归，PCR)和非线性(人工神经网络，ANN；支持向量机，对SVM)建模方法进行了评估。非线性模型，ANN和SVM，是绝对误差最低的更合适的模型。在评估的活细胞密度参数范围内（0.01-8.83×106个细胞/mL）的最佳模型的平均绝对误差，细胞活力（1.3-100.0%），葡萄糖（5.22-10.93g/L），乳酸（18.6-152.7mg/L），谷氨酰胺（158-1761毫克/升），谷氨酸（807.6-2159.7毫克/升），铵（62.8-117.8mg/L）为1.55±1.37×106个细胞/mL（ANN），5.01±4.93%（ANN），0.27±0.22g/L(SVM)，4.7±2.6mg/L（SVM），51±49毫克/升（ANN），57±39mg/L(SVM)和2.0±1.8mg/L(ANN)，分别。所取得的错误,最适合的模型就像使用离线数据和其他数据的相同生物过程的模型一样，它利用哺乳动物细胞系的内联光谱作为宿主。
