Real-time monitoring of bioprocesses plays a key-role in modern industries, providing new information on full-scale production, thus enabling control of the process and allowing it to run at optimal conditions while minimizing waste. Monitoring of phosphates and ammonium in fermentation processes has a twofold interest: they are important nutrients for living organisms while at the same time constituting environmental nutrient pollutants, for which unnecessary use and disposal must be avoided. In this report, the possibility of simultaneous analysis of phosphates and ammonium in fermentations was verified using spectroscopy-based methods combined with chemometrics to construct calibration models. To achieve this, the models were based on synthetic samples mimicking real fermentation media, providing a dataset where the analytes were completely uncorrelated. Different at-line techniques (mid- and near- infrared spectroscopy, MIR and NIR) were evaluated for their ability to monitor quickly both analytes, in a wide range of concentrations (10-100 mM), in three media of different complexities. Partial Least Squares (PLS) models on MIR spectroscopy gave very good results, with prediction errors lower than 5% for both analytes in all datasets. In contrast, the results for PLS models on NIR spectroscopy were inferior (prediction errors between 3 and 26%) for both analytes, as, in the case of phosphate, it could be demonstrated that the model was based on based on indirect predictions.

中文翻译：

---

类似发酵培养基中磷酸盐和铵盐的快速测量：可行性研究

生物过程的实时监控在现代工业中发挥着关键作用，提供有关全面生产的新信息，从而能够控制过程并使其在最佳条件下运行，同时最大限度地减少浪费。监测发酵过程中的磷酸盐和铵有双重意义：它们是生物体的重要营养物质，同时构成环境营养污染物，必须避免不必要的使用和处置。在本报告中，使用基于光谱的方法结合化学计量学构建校准模型，验证了同时分析发酵中磷酸盐和铵的可能性。为实现这一目标，模型基于模拟真实发酵培养基的合成样品，提供分析物完全不相关的数据集。评估了不同的在线技术（中红外和近红外光谱、MIR 和 NIR）能够快速监测三种不同复杂性介质中各种浓度（10-100 mM）的分析物。MIR 光谱的偏最小二乘 (PLS) 模型给出了非常好的结果，所有数据集中两种分析物的预测误差均低于 5%。相比之下，两种分析物的 PLS 模型在 NIR 光谱上的结果较差（预测误差在 3% 和 26% 之间），因为在磷酸盐的情况下，可以证明该模型基于间接预测。浓度范围很广（10-100 mM），在三种不同复杂度的介质中。MIR 光谱的偏最小二乘 (PLS) 模型给出了非常好的结果，所有数据集中两种分析物的预测误差均低于 5%。相比之下，两种分析物的 PLS 模型在 NIR 光谱上的结果较差（预测误差在 3% 和 26% 之间），因为在磷酸盐的情况下，可以证明该模型基于间接预测。浓度范围很广（10-100 mM），在三种不同复杂度的介质中。MIR 光谱的偏最小二乘 (PLS) 模型给出了非常好的结果，所有数据集中两种分析物的预测误差均低于 5%。相比之下，两种分析物的 PLS 模型在 NIR 光谱上的结果较差（预测误差在 3% 和 26% 之间），因为在磷酸盐的情况下，可以证明该模型基于间接预测。