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2T2D spectroscopy and deep learning for soil MPs quantification
Summary
Scientists have developed a computer tool that uses light-based scanning and artificial intelligence to accurately measure how much microplastic is hiding in soil samples. This matters because microplastics can enter our food supply through crops grown in contaminated soil, and having a faster, more reliable way to detect them helps researchers track this pollution and understand potential risks to human health. Note that this dataset and code paper focuses on the technical method itself, not on health outcomes from microplastic exposure.
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).