Artificial neural networks combined multi-wavelength transmission spectrum feature extraction for sensitive identification of waterborne bacteria.

Present research is focused on the rapid and accurate identification of bacterial species based on artificial neural networks combined with spectral data processing technology. The spectra of different bacterial species in the logarithmic growth phase were obtained. Model input features were extracted from the raw spectra using signal processing techniques, including normalization, principal component analysis (PCA) and area-based feature value extraction. The identification models based on artificial neural network of back propagation neural networks (BPNN), generalized regression neural networks (GRNN) and probabilistic neural networks (PNN) were developed using the extracted features in order to ascertain whether the different species of bacteria could be differentiated. The performance of developed models and its corresponding signal processing techniques is tested by the recognition accuracy of validation set and test set, and model error. The maximum recognition accuracy of normalized spectrum combined with BPNN was 95.5% (error: 10%, test accuracy: 100%). The total recognition accuracy of PCA-reduced features (200-400 nm) combined with GRNN resulted in 96.3%96.8% (error: 3.3%6.7%, test accuracy: 97.5%~100%). While the overall recognition accuracy of area-based features combined with GRNN reached 97.3% with test accuracy of 100% (model error: 5.0%). Choosing of model and signal processing techniques has a positive influence on improving classification accuracy, so as to make it possible to realize the rapid detection and online monitoring of waterborne microbial contamination.