Ferreira Ana P, Alves Teresa P, Menezes José C
Centre for Biological and Chemical Engineering, IST, Technical University of Lisbon, Av. Rovisco Pais, P-1049-001 Lisbon, Portugal.
Biotechnol Bioeng. 2005 Aug 20;91(4):474-81. doi: 10.1002/bit.20526.
Near-infrared spectroscopy (NIRS) is known to be a suitable technique for rapid fermentation monitoring. Industrial fermentation media are complex, both chemically (ill-defined composition) and physically (multiphase sample matrix), which poses an additional challenge to the development of robust NIRS calibration models. We investigated the use of NIRS for at-line monitoring of the concentration of clavulanic acid during an industrial fermentation. An industrial strain of Streptomyces clavuligerus was cultivated at 200-L scale for the production of clavulanic acid. Partial least squares (PLS) regression was used to develop calibration models between spectral and analytical data. In this work, two different variable selection methods, genetic algorithms (GA) and PLS-bootstrap, were studied and compared with models built using all the spectral variables. Calibration models for clavulanic acid concentration performed well both on internal and external validation. The two variable selection methods improved the predictive ability of the models up to 20%, relative to the calibration model built using the whole spectra.
近红外光谱法（NIRS）是一种适用于快速发酵监测的技术。工业发酵培养基在化学（成分不明确）和物理（多相样品基质）方面都很复杂，这给稳健的近红外光谱校准模型的开发带来了额外挑战。我们研究了近红外光谱法在工业发酵过程中在线监测克拉维酸浓度的应用。培养了一株产克拉维酸的棒状链霉菌工业菌株，规模为200升。采用偏最小二乘法（PLS）回归建立光谱数据与分析数据之间的校准模型。在这项工作中，研究了两种不同的变量选择方法，即遗传算法（GA）和PLS自展法，并与使用所有光谱变量建立的模型进行了比较。克拉维酸浓度的校准模型在内部和外部验证中均表现良好。相对于使用全光谱建立的校准模型，这两种变量选择方法将模型的预测能力提高了20%。
