Isobe, A. et al. A multilevel dataset of microplastic abundance worldwide’s upper ocean and the Laurentian Great Lakes. Microplast. Nanoplast. 1, 16 (2021).
Chatterjee, S. & Sharma, S. Microplastics in our oceans and marine health. J. Field Actions 19(2019), 54–61 (2019).
Cutroneo, L. et al. Microplastics in seawater: tasting techniques, lab methods, and recognition methods used to port environment. Environ. Sci. Pollut. Res. 27, 8938–8952 (2020).
Nguyen, B. et al. Separation and analysis of microplastics and nanoplastics in complicated ecological samples. Acc. Chem. Res. 52, 858–866 (2019).
Miller, M. E., Motti, C. A., Menendez, P. & Kroon, F. J. Efficacy of microplastic separation methods on seawater samples: Testing precision utilizing high-density polyethylene. Biol. Bull. 240, 52–66 (2021).
Prata, J. C., da Costa, J. P., Duarte, A. C. & Rocha-Santos, T. Methods for tasting and detection of microplastics in water and sediment: A critique. TrAC Trends Anal. Chem. 110, 150–159 (2019).
Schymanski, D. et al. Analysis of microplastics in drinking water and other tidy water samples with micro-Raman and micro-infrared spectroscopy: Minimum requirements and finest practice standards. Anal. Bioanal. Chem. 413, 5969–5994 (2021).
Hidalgo-Ruz, V., Gutow, L., Thompson, R. C. & Thiel, M. Microplastics in the marine environment: An evaluation of the approaches utilized for recognition and metrology. Environ. Sci. Technol. 46, 3060–3075 (2012).
Sajeesh, P. & Sen, A. K. Particle separation and sorting in microfluidic gadgets: An evaluation. Microfluid Nanofluid. 17, 1–52 (2014).
Zhang, S., Wang, Y., Onck, P. & den Toonder, J. A succinct evaluation of microfluidic particle adjustment approaches. Microfluid Nanofluid. 24, 24 (2020).
Blevins, M. G. et al. Field-portable microplastic picking up in liquid environments: A viewpoint on emerging methods. Sensors 21, 3532 (2021).
Elsayed, A. A. et al. A microfluidic chip makes it possible for quick analysis of water microplastics by optical spectroscopy. Sci. Rep. 11, 10533 (2021).
Mesquita, P., Gong, L. & Lin, Y. A low-cost microfluidic approach for microplastics recognition: Towards constant acknowledgment. Micromachines (Basel) 13, 499 (2022).
Chen, C. K. et al. A portable filtration system for the quick elimination of microplastics from ecological samples. Chem. Eng. J. 428, 132614 (2022).
Pollard, M., Hunsicker, E. & Platt, M. A tunable three-dimensional printed microfluidic resistive pulse sensing unit for the characterization of algae and microplastics. AIR CONDITIONING Sens. 5, 2578–2586 (2020).
Elsayed, A. A. et al. A microfluidic chip makes it possible for quick analysis of water microplastics by optical spectroscopy. Sci. Rep. 11, 10533 (2021).
Silva, A. B. et al. Microplastics in the environment: Challenges in analytical chemistry—An evaluation. Anal. Chim. Acta 1017, 1–19 (2018).
Crawford, C. B. & Quinn, B. 10-Microplastic recognition methods. In Microplastic Pollutants (eds Quinn, B. & Crawford, C. B.) 219–267 (Elsevier, 2017). https://doi.org/10.1016/B978-0-12-809406-8.00010-4.
Ribeiro-Claro, P., Nolasco, M. M. & Araújo, C. Chapter 5-Characterization of microplastics by Raman spectroscopy. In Characterization and Analysis of Microplastics Vol. 75 (eds Rocha-Santos, T. A. P. & Duarte, A. C.) 119–151 (Elsevier, 2017).
Yang, S.-J. et al. Rapid recognition of microplastic utilizing portable Raman system and additional trees algorithm. In Real-Time Photonic Measurements, Data Management, and Processing V Vol. 11555 (eds Li, M. et al.) 70–77 (SPIE, 2020).
Samuel, A. Z. et al. On picking an appropriate spectral matching approach for automated analytical applications of Raman spectroscopy. AIR CONDITIONING Omega 6, 2060–2065 (2021).
Araujo, C. F., Nolasco, M. M., Ribeiro, A. M. P. & Ribeiro-Claro, P. J. A. Identification of microplastics utilizing Raman spectroscopy: Latest advancements and future potential customers. Water Res. 142, 426–440 (2018).
Sathya, R. & Abraham, A. Comparison of monitored and not being watched knowing algorithms for pattern category. Int. J. Adv. Res. Artif. Intell. 2, 34–38 (2013).
Ramanna, S., Morozovskii, D., Swanson, S. & Bruneau, J. Machine knowing of polymer types from the spectral signature of Raman spectroscopy microplastics information. (2022).
Yu, S. et al. Analysis of Raman spectra by utilizing deep knowing approaches in the recognition of marine pathogens. Anal. Chem. 93, 11089–11098 (2021).
Sun, J. et al. Rapid recognition of salmonella serovars by utilizing Raman spectroscopy and artificial intelligence algorithm. Talanta 253, 123807 (2023).
Brownlee, J. Sensitivity analysis of dataset size vs. design efficiency. In Python Machine Learning (2021).
Nalepa, J. & Kawulok, M. Selecting training sets for assistance vector makers: An evaluation. Artif Intell Rev 52, 857–900 (2019).
Ramanna, S., Morozovskii, D., Swanson, S. & Bruneau, J. Machine knowing of polymer types from the spectral signature of Raman spectroscopy microplastics information. arXiv preprint arXiv:2201.05445 (2022).
Statnikov, A., Wang, L. & Aliferis, C. F. An extensive contrast of random forests and assistance vector makers for microarray-based cancer category. BMC Bioinform. 9, 319 (2008).
Dong, M. et al. Raman spectra and surface area modifications of microplastics weathered under natural surroundings. Sci. Tot. Environ. 739, 139990 (2020).
Rashidi, H. H., Albahra, S., Robertson, S., Tran, N. K. & Hu, B. Common analytical principles in the monitored maker finding out arena. Front. Oncol. 13, 1130229 (2023).
Lavoy, M. & Crossman, J. An unique approach for raw material elimination from samples consisting of microplastics. Environ. Pollut. 286, 117357 (2021).
Cowger, W. et al. Microplastic spectral category requires an open source neighborhood: Open specy to the rescue!. Anal. Chem. 93, 7543–7548 (2021).
Gillibert, R. et al. Raman tweezers for little microplastics and nanoplastics recognition in seawater. Environ. Sci. Technol. 53, 9003–9013 (2019).
Yuan, F. et al. A high-efficiency mini-hydrocyclone for microplastic separation from water through air flotation. J. Water Process Eng. 49, 103084 (2022).
Lv, D. et al. Trapping and launching of single microparticles and cells in a microfluidic chip. Electrophoresis 43, 2165 (2022).
Li, D. et al. Alcohol pretreatment to get rid of the disturbance of Micro additive particles in the recognition of microplastics utilizing Raman spectroscopy. Environ. Sci. Technol. 56, 12158–12168 (2022).
Yang, S.-J. et al. Rapid recognition of microplastic utilizing portable Raman system and additional trees algorithm. In Real-time photonic measurements, information management, and processing V Vol. 11555 (eds Li, M. et al.) 115550T (SPIE, 2020).
Gonzalez, G., Roppolo, I., Pirri, C. F. & Chiappone, A. Current and emerging patterns in polymeric 3D printed microfluidic gadgets. Addit. Manuf. 55, 102867 (2022).
Urso, M., Ussia, M., Novotný, F. & Pumera, M. Trapping and finding nanoplastics by MXene-derived oxide microrobots. Nat. Commun. 13, 3573 (2022).
Cai, H. et al. Analysis of ecological nanoplastics: Progress and obstacles. Chem. Eng. J. 410, 128208 (2021).
Xie, L., Gong, K., Liu, Y. & Zhang, L. Strategies and obstacles of recognizing nanoplastics in environment by surface-enhanced Raman spectroscopy. Environ. Sci. Technol. 57, 25–43 (2023).
Long, R. Fairness in artificial intelligence: Against incorrect positive rate equality as a step of fairness. J. Moral Philos. 19, 49–78 (2021).
Fahrenfeld, N. L., Arbuckle-Keil, G., Beni, N. N. & Bartelt-Hunt, S. L. Source tracking microplastics in the freshwater environment. TrAC Trends Anal. Chem. 112, 248–254 (2019).
Dey, T. Microplastic toxin detection by surface area boosted Raman spectroscopy (SERS): A mini-review. Nanotechnol. Environ. Eng. 8, 41–48 (2023).
Yang, S. High-wavenumber Raman analysis. In Recent Developments in Atomic Force Microscopy and Raman Spectroscopy for Materials Characterization (eds Pathak, C. S. & Kumar, S.) (IntechOpen, 2021). https://doi.org/10.5772/intechopen.100474.
Tuschel, D. Selecting an excitation wavelength for Raman spectroscopy. Spectroscopy 31, 14–23 (2016).
Munno, K., De Frond, H., O’Donnell, B. & Rochman, C. M. Increasing the availability for defining microplastics: Introducing brand-new application-based and spectral libraries of plastic particles (SLoPP and SLoPP-E). Anal. Chem. 92, 2443–2451 (2020).
Dong, M. et al. A Raman database of microplastics weathered under natural surroundings. Mendeley Data V2 739, 139990 (2020).
di Frischia, S., Chiuri, A., Angelini, F. & Colao, F. Optimization of signal-to-noise ratio in a CCD for spectroscopic applications. (2019).
di Frischia, S. et al. Enhanced information enhancement utilizing GANs for Raman spectra category. In 2020 IEEE International Conference on Big Data (Big Data) 2891–2898 (2020). https://doi.org/10.1109/BigData50022.2020.9377977.
Pedregosa, F. et al. Scikit-learn: Machine knowing in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Maruthamuthu, M. K., Raffiee, A. H., de Oliveira, D. M., Ardekani, A. M. & Verma, M. S. Raman spectra-based deep knowing: A tool to recognize microbial contamination. Microbiologyopen 9, e1122 (2020).
Ho, C.-S. et al. Rapid recognition of pathogenic germs utilizing Raman spectroscopy and deep knowing. Nat Commun 10, 4927 (2019).
Yu, S. et al. Analysis of Raman spectra by utilizing deep knowing approaches in the recognition of marine pathogens. Anal. Chem. 93, 11089–11098 (2021).
Huang, S. et al. Blood types recognition based upon deep knowing analysis of Raman spectra. Biomed. Opt. Express 10, 6129–6144 (2019).
Kukula, K. et al. Rapid detection of germs utilizing Raman spectroscopy and deep knowing. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), 796–799 (2021). https://doi.org/10.1109/CCWC51732.2021.9375955.
Huang, J. et al. On-website detection of SARS–CoV-2 antigen by deep learning-based surface-enhanced Raman spectroscopy and its biochemical structures. Anal. Chem. 93, 9174–9182 (2021).
Shao, X. et al. Deep convolutional neural networks integrate Raman spectral signature of serum for prostate cancer bone metastases evaluating. Nanomedicine 29, 102245 (2020).
Ciloglu, F. U. et al. Drug-resistant Staphylococcus aureus germs detection by integrating surface-enhanced Raman spectroscopy (SERS) and deep knowing methods. Sci. Rep. 11, 18444 (2021).
Yan, H. et al. Tongue squamous cell cancer discrimination with Raman spectroscopy and convolutional neural networks. Vib. Spectrosc. 103, 102938 (2019).
Zhang, L. et al. Rapid histology of laryngeal squamous cell cancer with deep-learning based promoted Raman spreading microscopy. Theranostics 9, 2541–2554 (2019).
Shin, H. et al. Early-phase lung cancer medical diagnosis by deep learning-based spectroscopic analysis of flowing exosomes. AIR CONDITIONING Nano 14, 5435–5444 (2020).
Ma, D. et al. Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network. Spectrochim. Acta A Mol. Biomol. Spectrosc. 256, 119732 (2021).
Lu, H., Tian, S., Yu, L., Lv, X. & Chen, S. Diagnosis of liver disease B based upon Raman spectroscopy integrated with a multiscale convolutional neural network. Vib Spectrosc 107, 103038 (2020).
Li, Y. et al. Early medical diagnosis of stomach cancer based upon deep knowing integrated with the spectral-spatial category approach. Biomed. Opt. Express 10, 4999–5014 (2019).
Guselnikova, O. et al. Label-complimentary surface-enhanced Raman spectroscopy with synthetic neural network strategy for acknowledgment photoinduced DNA damage. Biosens Bioelectron 145, 111718 (2019).
Wang, K. et al. Arcobacter recognition and types decision utilizing Raman spectroscopy integrated with neural networks. Appl. Environ. Microbiol. 86, e00924 (2020).
Chollet, F. et al. Keras. GitHub. Preprint at (2015).
He, K., Zhang, X., Ren, S. & Sun, J. Deep recurring knowing for image acknowledgment. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 770–778 (2016).
Brownlee, J. Classification precision is inadequate: more efficiency steps you can utilize. (2014).
Youden, W. J. Index for ranking diagnostic tests. Cancer 3, 32–35 (1950).
Kim, J., Erath, J., Rodriguez, A. & Yang, C. A high-efficiency microfluidic gadget for size-selective trapping and sorting. Lab Chip 14, 2480–2490 (2014).
