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2T2D spectroscopy and deep learning for soil MPs quantification
Summary
Scientists have developed a new tool that combines a special light-scanning technique with artificial intelligence to detect and measure tiny plastic particles (microplastics) hiding in soil. This matters because microplastics in soil can end up in the crops we eat and the water we drink, so having a faster, more accurate way to track them helps researchers understand how much plastic pollution is entering our food supply. This dataset and code release makes the detection method available for other scientists to test and build on, which could speed up future research into microplastics and health risks.
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 (PE_1), DOI: 10.17632/d2xz4sby82.1 (PE_2), DOI: 10.17632/2jf5gwrrg9.1 (PE_3_4), DOI: 10.17632/ksxndsxdgs.1 (PET_1), DOI: 10.17632/yp5kjgv58s.1 (PET_2), DOI: 10.17632/pybh5kgwz8.1 (PET_3_4), DOI: 10.17632/jdtnfgd5v7.1 (PP_1), DOI: 10.17632/spzk3vstdf.1 (PP_2), DOI: 10.17632/4wy2nrjbgc.1 (PP_3_4), DOI: 10.17632/wv8mn99ccb.1 (PS_1), DOI: 10.17632/4jy8k7j7ph.1 (PS_2), DOI: 10.17632/txrxrpks44.1 (PS_3_4).