Gordon S H, Wheeler B C, Schudy R B, Wicklow D T, Greene R V
Biopolymer Research Unit, National Center for Agricultural Utilization Research, Peoria, Illinois 61604, USA.
J Food Prot. 1998 Feb;61(2):221-30. doi: 10.4315/0362-028x-61.2.221.
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.
傅里叶变换红外光声光谱法（FTIR-PAS）是一种用于探测固体基质表面的高灵敏度方法，被用于检测玉米中的产毒真菌污染。感染了产毒真菌（如黄曲霉）的玉米粒所显示的FTIR-PAS光谱与未感染玉米粒的光谱有显著差异。识别出了光声红外光谱特征，并训练了一个人工神经网络，通过模式识别来区分受污染和未受污染的玉米。目前正在开展工作，将有关谷物作物真菌病害的流行病学信息整合到模式识别程序中，以产生一种基于更多知识、因而更可靠和更具特异性的技术。除了分析数据外，还提出了一个层次化组织的专家系统模型，利用玉米品种、植物胁迫和易感性、地理位置、天气、昆虫媒介以及处理和储存条件等流行病学因素，来预测粮食中可能存在的黄曲霉和其他种类的产毒真菌污染。
