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2T2D spectroscopy and deep learning for soil MPs quantification
Summary
Researchers developed an AI-powered tool that can accurately measure how much microplastic is hiding in soil by analyzing light-based scans, making detection faster and more precise than older methods. This matters because microplastics in soil can work their way into the crops we eat and the water we drink, so better tools to track contamination are a key step toward understanding — and eventually reducing — our exposure to these tiny plastic particles.
This dataset is primarily used for quantitative analysis of microplastic content in soil by combining DRSN-SpCA with 2T2D spectra. The code is written in Python, and the data is stored in .h5 format. main.py: This is the main function, which includes data import, model training, and result prediction. model_2T2D.py: The DRSN-SpCA model is encapsulated within it. early_stopping.py: This is the early stopping function. function_Function.py: The functions required by main.py are encapsulated within it. The specific data is available via DOI: 10.17632/zhdxvgzkfk.1 (Date_PE_1), DOI: 10.17632/d2xz4sby82.1 (Date_PE_2), DOI: 10.17632/2jf5gwrrg9.1 (Date_PE_3_4), DOI: 10.17632/ksxndsxdgs.1 (Date_PET_1), DOI: 10.17632/yp5kjgv58s.1 (Date_PET_2), DOI: 10.17632/pybh5kgwz8.1 (Date_PET_3_4), DOI: 10.17632/jdtnfgd5v7.1 (Date_PP_1), DOI: 10.17632/spzk3vstdf.1 (Date_PP_2), DOI: 10.17632/4wy2nrjbgc.1 (Date_PP_3_4), DOI: 10.17632/wv8mn99ccb.1 (Date_PS_1), DOI: 10.17632/4jy8k7j7ph.1 (Date_PS_2), DOI: 10.17632/txrxrpks44.1 (Date_PS_3_4).