Abstract：
The development of calibration models using Raman spectra data has long been challenged owing to the substantial time and cost required for robust data acquisition. To reduce the number of experiments involving actual incubation, a calibration model development method was investigated by measuring artificially mixed samples. In this method, calibration datasets were prepared using spectra from artificially mixed samples with adjusted concentrations based on design of experiments. The precision of these calibration models was validated using the actual cell culture sample. The results showed that when the culture conditions were unchanged, the root mean square error of prediction (RMSEP) of glucose, lactate, and antibody concentrations was 0.34, 0.33, and 0.25 g/L, respectively. Even when variables such as cell line or culture media were changed, the RMSEPs of glucose, lactate, and antibody concentrations remained within acceptable limits, demonstrating the robustness of the calibration models with artificially mixed samples. To further improve accuracy, a model training method for small datasets was also investigated. The spectral pretreatment conditions were optimized using error heat maps based on the first batch of each cell culture condition and applied these settings to the second and third batches. The RMSEPs improved for glucose, lactate, and antibody concentration, with values of 0.44, 0.19, and 0.18 g/L under constant culture conditions, 0.37, 0.12, and 0.12 g/L for different cell lines, and 0.26, 0.40, and 0.12 g/L when the culture media was changed. These results indicated the efficacy of calibration modeling with artificially mixed samples for actual incubations under various conditions.
摘要：
由于稳健的数据采集所需的大量时间和成本,使用拉曼光谱数据的校准模型的开发长期以来一直受到挑战。为了减少涉及实际孵化的实验数量，通过测量人工混合样品，研究了一种校准模型开发方法。在这种方法中，校准数据集是使用来自人工混合样品的光谱制备的，基于实验设计调整了浓度。使用实际细胞培养样品验证这些校准模型的精度。结果表明,当培养条件不变时,葡萄糖的预测均方根误差(RMSEP)，乳酸，抗体浓度分别为0.34、0.33和0.25g/L，分别。即使改变了诸如细胞系或培养基之类的变量，葡萄糖的RMSEPs，乳酸，抗体浓度保持在可接受的范围内，证明了人工混合样本校准模型的鲁棒性。为了进一步提高准确性，还研究了小数据集的模型训练方法。基于每个细胞培养条件的第一批次,使用误差热图优化光谱预处理条件,并将这些设置应用于第二批次和第三批次。RMSEP改善了葡萄糖，乳酸，和抗体浓度，在恒定培养条件下，值为0.44、0.19和0.18g/L，不同细胞系的0.37、0.12和0.12g/L，和0.26、0.40和0.12g/L时改变培养基。这些结果表明在各种条件下使用人工混合样品进行实际孵育的校准建模的功效。
