关键词:
Fourier transform infrared spectroscopy
摘要:
Objective As an important component of the atmospheric environment, bioaerosols have a profound effect on environmental quality, climate change, and human health. As environmental and public health problems intensify, the monitoring and identification of bioaerosols have attracted widespread attention. However, traditional bioaerosol identification methods, such as microbial culture and molecular biology techniques, are slow and complex. We combine attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy with one-dimensional convolutional neural network (1D-CNN) to leverage the high sensitivity, non-invasive and real-time advantages of spectroscopic technology, as well as deep learning powerful capabilities in feature extraction and classification of complex spectral data, and build an efficient and accurate bioaerosol identification model. Methods Bioaerosol samples, including three types of bacteria and three types of fungi, are used as the research object, and high-quality infrared absorption spectrum data are collected using a Fourier transform infrared spectrometer with an attenuated total reflection (ATR) accessory. To improve data quality, preprocessing techniques such as wavelet packet transform and Savitzky-Golay filtering are used for baseline correction and noise filtering. On this basis, a 1D-CNN model, including a convolution layer, a pooling layer, a dropout layer, and a fully connected layer, is constructed to utilize its powerful feature extraction and classification capabilities for the fast and accurate identification of bioaerosols. The effectiveness and superiority of the model are fully verified through reasonable data set division, multi-angle performance evaluation, and comparison with traditional machine learning methods. A mixed sample test plan of different concentrations is designed to further evaluate the model′s generalization ability in complex environments. Results and Discussions Through comparative analysis of t