| A genomic fingerprint classification system was developed to identify 63 Xanthomonas pathovars. Three sets of genomic fingerprints generated from repetitive DNA sequence-based polymerase chain reaction (rep-PCR), using BOX, ERIC and REP primers, were used in this research. In addition, a fourth set of BER fingerprints was formed by linearly combining the BOX, ERIC and REP fingerprints. Mean and wavelet filter techniques were used to reduce noise on the fingerprints. Several backpropagation neural network (BPN) classifiers were trained using the BOX, ERIC, REP and BER original fingerprints and filtered fingerprints. Both mean and wavelet filtering helped improve the recognition rates. Wavelet filtering was better at reducing misclassification error rates, and mean filtering was better at reducing false rejection error rates. The average top-2 recognition rates of BOX, ERIC, REP and BER BPN classifiers were 95%, 93%, 92% and 98%, respectively. By combining the results of the BOX, ERIC and REP BPN classifiers with the lowest misclassification error rates, a top-1 recognition rate of 95% was achieved together with a misclassification error rate of 0.57% and a false rejection rate of 4.3%. |
