Training Set For Neural Network Research Articles

Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains.

Sixteen representatives of three morphologically distinct groups of streptomycetes were recovered from soil using selective isolation procedures. Duplicated batches of the test strains were examined by Curie-point pyrolysis mass spectrometry and the first data set used for conventional multivariate statistical analyses and as a training set for an artificial neural network. The second set of data was used for 'operational fingerprinting' and for testing the artificial neural network. All of the test strains were correctly identified using the artificial neural network whereas only fifteen of the sixteen strains were assigned to the correct group using the conventional operational fingerprinting procedure. Artificial neural network analysis of pyrolysis mass spectrometric data provides a rapid, cost-effective and reproducible way of identifying and typing large numbers of microorganisms.

Structure-odor relationships using neural networks

Models of relationships between structure and musk odor of 79 nitrobenzenic compounds were constructed by means of a multilayer neural network using the backpropagation algorithm. Substituents on the 5 free sites on the benzenic ring (one position is always substituted by a terbutyl group) were described using 3 steric hindrance descriptors and 3 electronegativity descriptors. Odor was coded by a binary variable. Our neural network gives a better classification (94%) than that obtained by discriminant analysis (81%). The activity of the 79 molecules was then predicted (77% correct prediction) for 8 subsets of 10 (or 9) compounds using 69 (or 70) other molecules as a neural network training set.