Mazarevica Gunta, Diewok Josef, Baena Josefa R, Rosenberg Erwin, Lendl Bernhard
Institute of Chemical Technologies and Analytics, Vienna University of Technology, Getreidemarkt 9/164-AC, A-1060 Vienna, Austria.
Appl Spectrosc. 2004 Jul;58(7):804-10. doi: 10.1366/0003702041389229.
A new method for on-line monitoring of fermentations using mid-infrared (MIR) spectroscopy has been developed. The method has been used to predict the concentrations of glucose and ethanol during a baker's yeast fermentations. A completely automated flow system was employed as an interface between the bioprocess under study and the Fourier transform infrared (FT-IR) spectrometer, which was equipped with a flow cell housing a diamond attenuated total reflection (ATR) element. By using the automated flow system, experimental problems related to adherence of CO(2) bubbles to the ATR surface, as well as formation of biofilms on the ATR surface, could be efficiently eliminated. Gas bubbles were removed during sampling, and by using rinsing steps any biofilm could be removed from the ATR surface. In this way, constant measuring conditions could be guaranteed throughout prolonged fermentation times (approximately 8 h). As a reference method, high-performance liquid chromatography (HPLC) with refractive index detection was used. The recorded data from different fermentations were modeled by partial least-squares (PLS) regression comparing two different strategies for the calibration. On the one hand, calibration sets were constructed from spectra recorded from either synthetic standards or from samples drawn during fermentation. On the other hand, spectra from fermentation samples and synthetic standards were combined to form a calibration set. Differences in the kinetics of the studied fermentation processes used for calibration and prediction, as well as the precision of the HPLC reference method, were identified as the main chemometric sources of error. The optimal PLS regression method was obtained using the mixed calibration set of samples from fermentations and synthetic standards. The root mean square errors of prediction in this case were 0.267 and 0.336 g/L for glucose and ethanol concentration, respectively.
已开发出一种使用中红外(MIR)光谱在线监测发酵过程的新方法。该方法已用于预测面包酵母发酵过程中葡萄糖和乙醇的浓度。采用了一个完全自动化的流动系统作为所研究的生物过程与傅里叶变换红外(FT-IR)光谱仪之间的接口,该光谱仪配备了一个装有金刚石衰减全反射(ATR)元件的流通池。通过使用自动化流动系统,可以有效消除与CO₂气泡附着在ATR表面以及ATR表面形成生物膜相关的实验问题。在采样过程中去除气泡,并通过冲洗步骤可以从ATR表面去除任何生物膜。通过这种方式,可以在延长的发酵时间(约8小时)内保证恒定的测量条件。作为参考方法,使用了带有折光指数检测的高效液相色谱(HPLC)。通过偏最小二乘法(PLS)回归对不同发酵记录的数据进行建模,比较了两种不同的校准策略。一方面,校准集由从合成标准品记录的光谱或从发酵过程中抽取的样品记录的光谱构建。另一方面,将发酵样品和合成标准品的光谱组合形成校准集。用于校准和预测的所研究发酵过程的动力学差异以及HPLC参考方法的精度被确定为主要的化学计量学误差来源。使用来自发酵样品和合成标准品的混合校准集获得了最佳的PLS回归方法。在这种情况下,葡萄糖和乙醇浓度的预测均方根误差分别为0.267和0.336 g/L。