Departamento de Engenharia Química, Universidade Federal de São Carlos, São Carlos, SP, Brazil.
Bioprocess Biosyst Eng. 2010 Jun;33(5):557-64. doi: 10.1007/s00449-009-0377-y. Epub 2009 Sep 2.
The complexity of biological processes often makes impractical the development of detailed, structured phenomenological models of the cultivation of microorganisms in bioreactors. In this context, data pre-treatment techniques are useful for bioprocess control and fault detection. Among them, principal component analysis (PCA) plays an important role. This work presents a case study of the application of this technique during real experiments, where the enzyme penicillin G acylase (PGA) was produced by Bacillus megaterium ATCC 14945. PGA hydrolyzes penicillin G to yield 6-aminopenicilanic acid (6-APA) and phenyl acetic acid. 6-APA is used to produce semi-synthetic beta-lactam antibiotics. A static PCA algorithm was implemented for on-line detection of deviations from the desired process behavior. The experiments were carried out in a 2-L bioreactor. Hotteling's T(2) was the discrimination criterion employed in this multivariable problem and the method showed a high sensibility for fault detection in all real cases that were studied.
生物过程的复杂性常常使得难以开发出详细的、结构化的微生物培养生物反应器的现象学模型。在这种情况下，数据预处理技术对于生物过程控制和故障检测很有用。其中，主成分分析（PCA）起着重要的作用。本工作介绍了在实际实验中应用该技术的案例研究，其中青霉素 G 酰化酶（PGA）由巨大芽孢杆菌 ATCC 14945 产生。PGA 水解青霉素 G 生成 6-氨基青霉素酸（6-APA）和苯乙酸。6-APA 用于生产半合成β-内酰胺抗生素。实现了静态 PCA 算法，用于在线检测偏离期望过程行为的情况。实验在 2-L 生物反应器中进行。Hotteling 的 T(2) 是该多变量问题中使用的判别准则，该方法在研究的所有实际情况下均表现出了很高的故障检测灵敏度。
