Pattern recognition of respirable dust particles by a back-propagation artificial neural network

A back-propagation neural network was used as a pattern recognition tool for LAMMA mass spectral data. Standard EPA source profiles were used as training and test data of the net. The elemental patterns (10 elements) of the sum of 100 mass spectra of fine dust particles were presented to the trained nets and satisfactory recognition (> 50%) was obtained.