论文标题
机器学习和深度学习方法，用于从拉曼光谱中进行预测建模
Machine Learning and Deep Learning methods for predictive modelling from Raman spectra in bioprocessing
论文作者
论文摘要
在化学加工和生物处理中，常规的在线传感器仅限于测量压力和温度，pH，溶解的O和CO $ _2 $以及可行的细胞密度（VCD）等基本过程变量。其他化学物种的浓度更难测量，因为它通常需要在线或离线方法。与在线感测相比，这种方法具有侵入性和缓慢。众所周知，可以通过它们与单色光的相互作用来区分不同的分子，从而根据浓度产生不同的拉曼光谱谱。鉴于目标变量的参考测量值的可用性，回归方法可用于模拟拉曼光谱曲线与分析物浓度之间的关系。这项工作的重点是使用机器学习和深度学习方法来促进回归任务的拉曼光谱预处理方法，以及基于这些方法的新回归模型的开发。在大多数情况下，就预测误差和预测鲁棒性而言，这允许胜过常规的拉曼模型。
In chemical processing and bioprocessing, conventional online sensors are limited to measure only basic process variables like pressure and temperature, pH, dissolved O and CO$_2$ and viable cell density (VCD). The concentration of other chemical species is more difficult to measure, as it usually requires an at-line or off-line approach. Such approaches are invasive and slow compared to on-line sensing. It is known that different molecules can be distinguished by their interaction with monochromatic light, producing different profiles for the resulting Raman spectrum, depending on the concentration. Given the availability of reference measurements for the target variable, regression methods can be used to model the relationship between the profile of the Raman spectra and the concentration of the analyte. This work focused on pretreatment methods of Raman spectra for the facilitation of the regression task using Machine Learning and Deep Learning methods, as well as the development of new regression models based on these methods. In the majority of cases, this allowed to outperform conventional Raman models in terms of prediction error and prediction robustness.
