RAPID AND QUANTITATIVE ANALYSIS OF METABOLITES IN FERMENTER BROTHS USING PYROLYSIS MASS SPECTROMETRY WITH SUPERVISED LEARNING - APPLICATION TO THE SCREENING OF PENICILLIUM CHRYSOGENUM FERMENTATIONS FOR THE OVERPRODUCTION OF PENICILLINS [Review]

摘要

The combination of pyrolysis mass spectrometry (PyMS) and artificial neural networks (ANNs) can be used to quantify levels of penicillins in strains of Penicillium chrysogenum and ampicillin in spiked samples of Escherichia coli. Four P. chrysogenum strains (NRRL 1951, Wis Q176, P1, and P2) were grown in submerged culture to produce penicillins, and fermentation samples were taken aseptically and subjected to PyMS. To deconvolute the pyrolysis mass spectra so as to obtain quantitative information on the titre of penicillins, fully-interconnected feedforward artificial neural networks (ANNs) were studied; the weights were modified using the standard back-propagation algorithm, and the nodes used a sigmoidal squashing function. In addition the multivariate linear regression techniques of partial least squares regression (PLS), principal components regression (PCR) and multiple linear regression (MLR) were applied. The ANNs could be trained to give excellent estimates for the penicillin titre, not only from the spectra that had been used to train the ANN but more importantly from previously unseen pyrolysis mass spectra. All the linear regression methods failed to give accurate predictions, because of the very variable biological backgrounds (the four different strains) in which penicillin was produced and also of the inability of models using linear regression accurately to map non-linearities. Comparisons of squashing functions on the output nodes of identical 150-8-1 neural networks revealed that networks employing linear functions gave more accurate estimates of ampicillin in E. coli near the edges of the concentration range than did those using sigmoidal functions. It was also shown that these neural networks could be successfully used to extrapolate beyond the concentration range on which they had been trained. PyMS with the multivariate clustering technique of principal components analysis was able to differentiate between four strains of P. chrysogenum studied, and was also able to detect phenotypic differences at five, seven, nine or 11 days growth. A crude sampling procedure consisting of homogenised agar plugs proved applicable for rapid analysis of a large number of samples. [References: 122]