QUALITY CLASSIFICATION OF GRAIN USING A SENSOR ARRAY AND PATTERN-RECOGNITION

Measurements using arrays of electrochemical gas sensors, combined wit h pattern recognition methods, were used to classify wheat samples by quality grade. The classifications corresponded closely to those made by trained grain inspectors. Volatile compounds evolved from warmed sa mples of grain were passed over a heated noble metal catalyst and then into a series of electrochemical sensors. Signals from four sensors w ere recorded for four different catalyst temperatures in order to gene rate 16 signals for each grain odor sample. The 16 sensor signals were treated as a 16-dimensional vector or pattern of responses that was c haracteristic of the odor sample. The patterns for different grain odo r samples were compared using both nearest-neighbor analysis and a com mercial neural network simulation (NNS) program. These methods classif ied the samples correctly by grade with an accuracy of 68% and 65%, re spectively. After compensation for instrument parameters, the NNS scor e improved to 83%; the nearest-neighbor analysis could not be similarl y compensated. The robustness of the two algorithms was compared by ad ding simulated random and systematic errors to the sensor response pat terns. The original data were used as the training set, and the patter ns with errors added were used as the test set. In these cases, the NN S consistently outperformed the nearest-neighbor method at classificat ion of the grain odor samples.