บทคัดย่องานวิจัย

หมวดหมู่ : ฐานข้อมูลงานวิจัย

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

GORDON, S.H., WHEELER, B.C., SCHUDY, R.B., WICKLOW, D.T. and GREENE, R.V.

Journal of Food Protection 61 : 221-230.

1998

บทคัดย่อ

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

Kernels of corn infected with mycotoxigenic fungi, such as Aspergillus flavus, display FTIR-PAS spectra that differ significantly from spectra of uninfected kernels.  Photoacoustic infrared spectral features were identified and an artificial neural network was trained to distinguish contaminated from 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 reliable and specific technique.  A model of a hierachically organised expert system is proposed, using epidemiological factors such as corn variety, plant stress and susceptibility to infection, geographic location, weather, insect vactors and handling and storage conditions, in addition to the analytical data, to predict A. flavus and other kinds of toxigenic fungal contamination that might be present in food grains.