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Detection of microplastics stress on rice seedling by visible/near-infrared hyperspectral imaging and synchrotron radiation Fourier transform infrared microspectroscopy
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Researchers used visible/near-infrared hyperspectral imaging combined with synchrotron radiation analysis and deep learning to detect physiological and biochemical stress responses in rice seedlings exposed to microplastics, developing a rapid and interpretable early-detection method for microplastic stress in crops.
In conclusion, the combination of spectral technology and deep learning to capture the physiological and biochemical reactions of leaves could provide a rapid and interpretable method for detecting rice seedlings under MPs stress. This method could provide a solution for the early detection of external stress on other crops.
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Researchers developed a non-destructive, interpretable deep learning framework using excitation-emission matrix fluorescence spectra of root exudates to detect microplastic stress in rice seedlings at early stages. The approach enables early-warning detection of plastic contamination effects on crop health without damaging the plants.
Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging
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Researchers combined short-wave infrared hyperspectral imaging with machine learning algorithms to detect low concentrations of polyamide and polyethylene microplastics in soil samples, achieving accurate classification with implications for fast, non-destructive screening of agricultural land for plastic contamination.
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Researchers applied hyperspectral imaging technology combined with machine learning to rapidly screen and classify microplastics in farmland soil samples, demonstrating an efficient non-destructive identification method for soil microplastic contamination.
Efficient screening of microplastics in soils using hyperspectral imaging in the short-wave infrared range coupled with machine learning – A laboratory-based experiment
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Researchers tested short-wave infrared hyperspectral imaging combined with machine learning to detect three types of microplastics in soil, finding it could identify elevated contamination but was not sensitive enough for typical environmental background levels. The technique shows most promise for screening heavily polluted sites like landfills and industrial areas.
Screening the phytotoxicity of micro/nanoplastics through non-targeted metallomics with synchrotron radiation X-ray fluorescence and deep learning: Taking micro/nano polyethylene terephthalate as an example
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Researchers developed a new screening method combining synchrotron radiation X-ray fluorescence imaging with deep learning to detect the toxic effects of micro- and nanoplastics on plants. By analyzing how the tiny plastic particles disrupt the distribution of essential elements in plant tissues, the method can predict toxicity without traditional biological assays. The approach offers a faster, non-targeted way to assess environmental risks posed by plastic pollution to plants.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.