Neural network pattern recognition of photoacoustic FTIR spectra and knowledge-based techniques for detection of mycotoxigenic fungi in food grains.

Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS), a highly sensitive probe of the surfaces of solid substrates, is used to detect toxigenic fungal contamination in corn. Kernels of corn infected with mycotoxigenic fungi, such as Aspergillus flavus, display FTIR-PAS spectra that differ significantly form spectra of uninfected kernels. Photoacoustic infrared spectral features were identified, and an artificial neural network was trained to distinguish contaminated form uncontaminated corn by pattern recognition. Work is in progress to integrate epidemiological information about cereal crop fungal disease into the pattern recognition program to produce a more knowledge-based, and hence more reliable and specific, technique. A model of a hierarchically organized expert system is proposed, using epidemiological factors such as corn variety, plant stress and susceptibility to infection, geographic location, weather, insect vectors, and handling and storage conditions, in addition to the analytical data, to predict Al. flavus and other kinds of toxigenic fungal contamination that might be present in food grains.