[1] Huffman J A, Perring A E, Savage N J et al. Real-time sensing of bioaerosols: review and current perspectives[J]. Aerosol Science and Technology, 54, 465-495(2020).

[2] Liang X F, Yang Z H, Wang S Y et al. Progres in remote sensing of toxic and harmful gas based on differential absorption lidar[J]. Laser Technology, 45, 53-60(2021).

[3] Yang R, Dong J H, Yang Z H et al. Progress in bioaerosol laser remote detection technology[J]. Laser Journal, 44, 1-7(2023).

[4] Chen Y B, Wang X P, Bu Z C et al. Calibration and result analysis of aerosol LiDAR in megacity experiment[J]. Laser Technology, 46, 435-443(2022).

[5] Carestia M, Pizzoferrato R, Gelfusa M et al. Development of a rapid method for the automatic classification of biological agents’ fluorescence spectral signatures[J]. Optical Engineering, 54, 114105(2015).

[6] Duschek F, Fellner L, Gebert F et al. Standoff detection and classification of bacteria by multispectral laser-induced fluorescence[J]. Advanced Optical Technologies, 6, 75-83(2017).

[7] Ruske S, Topping D O, Foot V E et al. Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer[J]. Atmospheric Measurement Techniques, 10, 695-708(2017).

[8] Leśkiewicz M, Kaliszewski M, Włodarski M et al. Improved real-time bio-aerosol classification using artificial neural networks[J]. Atmospheric Measurement Techniques, 11, 6259-6270(2018).

[9] Liu J X, Du B, Deng Y Q et al. Terahertz-spectral identification of organic compounds based on differential PCA-SVM method[J]. Chinese Journal of Lasers, 46, 0614039(2019).

[10] Gebert F, Kraus M, Fellner L et al. Novel standoff detection system for the classification of chemical and biological hazardous substances combining temporal and spectral laser-induced fluorescence techniques[J]. The European Physical Journal Plus, 133, 269(2018).

[11] Narlagiri L M, Bharati M S S, Beeram R et al. Recent trends in laser-based standoff detection of hazardous molecules[J]. TrAC Trends in Analytical Chemistry, 153, 116645(2022).

[12] Pan Y L. Detection and characterization of biological and other organic-carbon aerosol particles in atmosphere using fluorescence[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 150, 12-35(2015).

[13] Wang Q, Zeng W D, Xia Z P et al. Recognition of food-borne pathogenic bacteria by Raman spectroscopy based on random forest algorithm[J]. Chinese Journal of Lasers, 48, 0311002(2021).

[14] Yu X Y, Zhang Y J, Yin G F et al. Feature wavelength selection of phytoplankton fluorescence spectra based on partial least squares[J]. Acta Optica Sinica, 34, 0930002(2014).

[15] Ji G S, Chen P L, Song H. Study the survey into the decision tree classification algorithms rule[J]. Science Mosaic, 9-12(2007).

[16] Lai C T. Research on decision tree classification algorithm[J]. China Computer & Communication, 32, 59-62(2020).

[17] Wang J, Zhou Z H, Zhou A Y[M]. Machine learning and its application(2006).

[18] Ding H B, Wang Z Z, Liu D. Comparison of de-noising methods of LiDAR signal[J]. Acta Optica Sinica, 41, 2401001(2021).

[19] Hu M H, Mao J D, Li J et al. A novel lidar signal denoising method based on convolutional autoencoding deep learning neural network[J]. Atmosphere, 12, 1403(2021).

[20] Fellner L, Kraus M, Gebert F et al. Multispectral LIF-based standoff detection system for the classification of CBE hazards by spectral and temporal features[J]. Sensors, 20, 2524(2020).

[21] Rafael C G[M]. Digital image processing(2011).

[22] Cheng P Q[M]. Digital signal processing course: MATLAB version(2017).