在这项研究中,我们开发了一种方法来建立没有培养数据的拉曼校准模型,用于细胞培养监测。首先,收集拉曼光谱,然后分析所有提到的分析物的信号:葡萄糖,乳酸,谷氨酰胺,谷氨酸,氨,抗体,活细胞,媒体,和饲料代理。利用这些光谱数据,检测了每个因子的特定峰位置和强度.接下来,根据实验设计方法,通过混合上述因素制备样品。收集这些样品的拉曼光谱并用于建立校准模型。比较了光谱预处理和波数区域的几种组合,以优化没有培养数据的细胞培养监测的校准模型。通过进行实际细胞培养并将在线测量的光谱拟合到开发的校准模型来评估开发的校准模型的准确性。因此,校准模型对三个分量都达到了足够好的精度,葡萄糖,乳酸,和抗体(预测均方根误差(RMSEP)=0.23、0.29和0.20g/L,分别)。这项研究在开发不使用文化数据的文化监测方法方面取得了创新成果,同时使用基本的常规方法研究培养基中每种成分的拉曼光谱,然后利用实验方法的设计。
In this study, we developed a method to build Raman calibration models without culture data for cell culture monitoring. First, Raman spectra were collected and then analyzed for the signals of all the mentioned analytes: glucose, lactate, glutamine, glutamate, ammonia, antibody, viable cells, media, and feed agent. Using these spectral data, the specific peak positions and intensities for each factor were detected. Next, according to the design of the experiment method, samples were prepared by mixing the above-mentioned factors. Raman spectra of these samples were collected and were used to build calibration models. Several combinations of spectral pretreatments and wavenumber regions were compared to optimize the calibration model for cell culture monitoring without culture data. The accuracy of the developed calibration model was evaluated by performing actual cell culture and fitting the in-line measured spectra to the developed calibration model. As a result, the calibration model achieved sufficiently good accuracy for the three components, glucose, lactate, and antibody (root mean square errors of prediction, or RMSEP = 0.23, 0.29, and 0.20 g/L, respectively). This study has presented innovative results in developing a culture monitoring method without using culture data, while using a basic conventional method of investigating the Raman spectra of each component in the culture media and then utilizing a design of experiment approach.