Feng Chun, Zhao Nanjing, Yin Gaofang, Gan Tingting, Yang Ruifang, Chen Xiaowei, Chen Min, Duan Jingbo
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China.
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; Key Laboratory of Optical Monitoring Technology for Environment, Anhui Province, Hefei 230031, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Apr 15;251:119423. doi: 10.1016/j.saa.2020.119423. Epub 2021 Jan 5.
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.
目前的研究聚焦于基于人工神经网络结合光谱数据处理技术对细菌种类进行快速准确的识别。获取了处于对数生长期的不同细菌种类的光谱。使用信号处理技术从原始光谱中提取模型输入特征，包括归一化、主成分分析（PCA）和基于面积的特征值提取。利用提取的特征开发了基于反向传播神经网络（BPNN）、广义回归神经网络（GRNN）和概率神经网络（PNN）的人工神经网络识别模型，以确定不同种类的细菌是否能够被区分。通过验证集和测试集的识别准确率以及模型误差来测试所开发模型及其相应信号处理技术的性能。归一化光谱结合BPNN的最大识别准确率为95.5%（误差：10%，测试准确率：100%）。PCA降维特征（200 - 400纳米）结合GRNN的总识别准确率为96.3%96.8%（误差：3.3%6.7%，测试准确率：97.5%~100%）。而基于面积的特征结合GRNN的总体识别准确率达到97.3%，测试准确率为100%（模型误差：5.0%）。模型和信号处理技术的选择对提高分类准确率有积极影响，从而有可能实现对水体微生物污染的快速检测和在线监测。
