Université Paris-Saclay, CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), 3 rue des Rouges Terres 51110 Pomacle, France; CentraleSupélec, Chaire Photonique, Laboratoire Matériaux Optiques, Photonique et Systèmes (LMOPS), Metz F-57070, France; Université de Lorraine, Laboratoire Matériaux Optiques, Photonique et Systèmes (LMOPS), Metz F-57070, France.
Université Paris-Saclay, CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), 3 rue des Rouges Terres 51110 Pomacle, France.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Jan 5;304:123343. doi: 10.1016/j.saa.2023.123343. Epub 2023 Sep 3.
An in-situ monitoring model of alcoholic fermentation based on Raman spectroscopy was developed in this study. The optimized acquisition parameters were an 80 s exposure time with three accumulations. Standard solutions were prepared and used to populate a learning database. Two groups of mixed solutions were prepared for a validation database to simulate fermentation at different conditions. First, all spectra of the standards were evaluated by principal component analysis (PCA) to identify the spectral features of the target substances and observe their distribution and outliers. Second, three multivariate calibration models for prediction were developed using the partial least squares (PLS) method, either on the whole learning database or subsets. The limit of detection (LOD) of each model was estimated by using the root mean square error of cross validation (RMSECV), and the prediction ability was further tested with both validation datasets. As a result, improved LODs were obtained: 0.42 and 1.55 g·L for ethanol and glucose using a sub-learning dataset with a concentration range of 0.5 to 10 g·L. An interesting prediction result was obtained from a cross-mixed validation set, which had a root mean square error of prediction (RMSEP) for ethanol and glucose of only 3.21 and 1.69, even with large differences in mixture concentrations. This result not only indicates that a model based on standard solutions can predict the concentration of a mixed solution in a complex matrix but also offers good prospects for applying the model in real bioreactors.
本研究开发了一种基于拉曼光谱的酒精发酵原位监测模型。优化的采集参数为 80s 曝光时间，累积三次。制备标准溶液并用于填充学习数据库。制备两组混合溶液作为验证数据库，以模拟不同条件下的发酵。首先，通过主成分分析（PCA）评估所有标准光谱，以识别目标物质的光谱特征，并观察它们的分布和异常值。其次，使用偏最小二乘（PLS）方法，基于整个学习数据库或子集，开发了三个用于预测的多元校正模型。通过交叉验证均方根误差（RMSECV）估算每个模型的检测限（LOD），并使用两个验证数据集进一步测试预测能力。结果，使用浓度范围为 0.5 至 10g·L 的子学习数据集，分别获得了 0.42 和 1.55g·L 的乙醇和葡萄糖的改进 LOD。从交叉混合验证集中获得了一个有趣的预测结果，其乙醇和葡萄糖的预测均方根误差（RMSEP）仅为 3.21 和 1.69，即使混合物浓度差异很大。这一结果不仅表明基于标准溶液的模型可以预测复杂基质中混合溶液的浓度，而且为该模型在实际生物反应器中的应用提供了良好的前景。
