The ethanol concentration in batch cultivation with the yeast Saccharomyces cerevisiae was predicted on-line using a gas sensor array. Headspace samples were pumped past the gas sensors array for 10 s every five minutes and the voltage changes of the sensors were measured. For the calibration procedure no off-line sampling was used. Instead, a theoretical model of the process has been applied to simulate the ethanol production at any given time. However, the kinetic parameters of the simulation model are unknown at the beginning of the calibration. It is demonstrated that these kinetic parameters of the theoretical process model can be acquired from the response of the gas sensor array alone. The calculated parameters result in a simulation model that is at least as accurate as a model whose parameters have been acquired by least squares fitting to off-line measurements. The root mean square error of calibration as well as the percentage error for validation sets were below 0.2 g L−1 and 7%, respectively. The obtained results indicate that the model-based calibrated gas sensor array can be a cheap alternative to other tools, such as spectroscopy-based methods, that are used for monitoring yeast cultivations.

中文翻译：

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用于在线监测酿酒酵母分批培养中乙醇浓度的气体传感器阵列的基于模型的校准

使用气体传感器阵列在线预测酵母酿酒酵母分批培养中的乙醇浓度。每五分钟将顶空样品泵送通过气体传感器阵列 10 秒，并测量传感器的电压变化。对于校准程序，没有使用离线采样。相反，该过程的理论模型已应用于模拟任何给定时间的乙醇生产。然而，在校准开始时，模拟模型的动力学参数是未知的。结果表明，理论过程模型的这些动力学参数可以单独从气体传感器阵列的响应中获得。计算出的参数产生的仿真模型至少与其参数已通过最小二乘拟合离线测量获得的模型一样准确。校准的均方根误差以及验证集的百分比误差分别低于 0.2 g L-1 和 7%。获得的结果表明，基于模型的校准气体传感器阵列可以作为其他工具的廉价替代品，例如用于监测酵母培养的基于光谱的方法。