Rapid and Quantitative Analysis of the Pyrolysis Mass Spectra of Complex Binary and Tertiary Mixtures Using Multivariate Calibration and Artificial Neural Networks

Binary mixtures of the protein lysozyme with glycogen, of DNA or RNA in glycogen, and the tertiary mixture of cells of the bacteria Bacillus subtilh, Escherichia coli, and Staphylococcusaurecls were subjected to pyrolysis mass spectrometry. To analyze the pyrolysis mass spectra so as to obtain quantitative information representative of the complex components of the mixtures, partial least-squares regression (PLS), principal components regression (PCR), and fully interconnected feedforward artificial neural networks (ANNs) were studied. In the latter case, the weights were modified using the standard back-propagation algorithm, and the nodes used a sigmoidal squashing function. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the concentrations of determinands in samples on which they had not been trained. Neural networks were found to provide the most accurate predictions. We also report that scaling the individual nodes on the input layer of ANNs significantly decreased the time taken for the ANNs to learn. Removing masses of low intensity, which perhaps mainly contributed noise to the pyrolysis mass spectra, had little effect on the accuracy of the ANN predictions though could dramatically speed up the learning process (by more than 100-fold) and slightly improved the accuracy of PLS calibrations.