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使用电子鼻预测生物反应器中的孢子形成事件。

Predicting sporulation events in a bioreactor using an electronic nose.

作者信息

Clemente J J, Monteiro S M S, Carrondo M J T, Cunha A E

机构信息

Instituto de Biologia Experimental e Tecnológica (IBET), Apartado 12, P-2781-901 Oeiras, Portugal.

出版信息

Biotechnol Bioeng. 2008 Oct 15;101(3):545-52. doi: 10.1002/bit.21920.

Abstract

An electronic nose (EN) based on a non- specific multi-sensor array was used to accurately estimate sporulation events and the spore concentration of Bacillus subtilis cultures. The array included 6 metal oxide sensors (MOS), 10 metal oxide semiconductor field effect transistors (MOSFET), one CO(2) infrared sensor and one humidity sensor. The EN was used to monitor the gas emissions from B. subtilis bioreactions during both batch and fed-batch operation. The signal pattern produced by the sensors was evaluated by principal component analysis (PCA) and training cultivations were used to build a model. The arc length of the PCA trajectories was successfully correlated to the off-line spore count; a strong linear correlation (R(2) = 0.992) between the numerical integration of the curves and the measured spore concentration was established. The fast responses of the sensors in combination with the robust correlation with the off-line determination of spore concentration establish this EN device as a convenient tool for monitoring sporulation events in bioprocesses.

摘要

一种基于非特异性多传感器阵列的电子鼻被用于准确估计枯草芽孢杆菌培养物的芽孢形成事件和孢子浓度。该阵列包括6个金属氧化物传感器(MOS)、10个金属氧化物半导体场效应晶体管(MOSFET)、一个二氧化碳红外传感器和一个湿度传感器。该电子鼻用于监测分批和补料分批操作过程中枯草芽孢杆菌生物反应产生的气体排放。通过主成分分析(PCA)评估传感器产生的信号模式,并使用训练培养物建立模型。PCA轨迹的弧长与离线孢子计数成功相关;曲线的数值积分与测量的孢子浓度之间建立了强线性相关性(R² = 0.992)。传感器的快速响应以及与孢子浓度离线测定的稳健相关性,使该电子鼻装置成为监测生物过程中芽孢形成事件的便捷工具。

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