Pattern recognition analysis of optical sensor array data to detect nitroaromatic compound vapors

A fiber optic-based sensor array has been employed to determine the presence or absence of nitroaromatic compound (NAC) vapors in variable backgrounds of volatile organic compound (VOC) vapors. The system is based on previously developed cross-reactive array technology and employs a sensor array attached to the distal tips of an optical fiber bundle. Four different sensors, with 50 replicates of each type, were used to computationally train the system to detect and recognize the presence of explosives-like NAC vapors. Two of the NACs were employed because they are commonly detected on the soil surface above buried 2,4,6-trinitrotoluene plastic land mines. Based on fluorescent responses, samples in an external prediction set were classified with 100% accuracy using models trained to determine if NAC vapors were present. Additionally, models were developed with one of the three NAC vapors held out of the training process, but included in the prediction set. In all three models, over 92% of samples in an external prediction set were classified correctly.