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61,005 resultsShowing papers similar to Spectral data of PE and PP microplastics in soil (FT-NIR & ATR-FTIR)
ClearSpectral data of PE and PP microplastics in soil (FT-NIR & ATR-FTIR)
Researchers developed a dataset of FT-NIR and ATR-FTIR spectral data for polyethylene and polypropylene microplastics in soil, designed to support training and validation of a support vector regression model for rapid quantitative detection of microplastics using spectral fusion and machine learning.
Rapid detection of microplastics in plastic-covered soils using FT-NIR and ATR-FTIR spectral data fusion
Researchers developed a rapid, non-destructive method to detect microplastics in agricultural soils by combining two infrared spectroscopy techniques (FT-NIR and ATR-FTIR) with machine-learning models. The fused spectral approach substantially outperformed either technique alone, detecting microplastics down to around 7 parts per million. Fast, accurate soil screening tools are critical for understanding and managing the growing microplastic contamination in farmland.
Rapid Detection of Microplastics in Plastic-covered Soil Using FT-NIR and ATR-FTIR Spectral Data Fusion
Scientists developed a new method to quickly detect tiny plastic particles in farm soil by combining two different light-based detection techniques. This method can accurately measure microplastic pollution in agricultural fields where plastic covers are used for growing crops. This matters because microplastics in farm soil can potentially enter our food chain through the fruits and vegetables we eat.
Predicting soil microplastic concentration using vis-NIR spectroscopy
Researchers used visible and near-infrared (vis-NIR) spectroscopy to predict microplastic concentrations in soil samples, developing calibration models that could estimate contamination levels directly from spectral measurements without extensive sample preparation. The approach offers potential for faster and more scalable monitoring of microplastic pollution in agricultural and natural soils.
Microplastic Analysis in Soil Using Ultra-High-Resolution UV–Vis–NIR Spectroscopy and Chemometric Modeling
Researchers tested a new method using UV-visible-near infrared spectroscopy combined with machine learning to identify microplastics in soil samples. They found the technique could rapidly and accurately distinguish between different plastic polymers and natural soil particles. The study offers a promising alternative to current labor-intensive identification methods, potentially making large-scale microplastic soil monitoring more practical.
Soil Microplastics Spectrum Based on Visible Near-Infrared Spectroscopy
Researchers developed a visible near-infrared spectroscopy method for quantifying microplastics in soil, finding that spectral reflectance decreases with increasing microplastic content and that a regression model combining normalisation with first-derivative transformation achieved the best predictive accuracy with R-squared values of 0.75 and 0.77 for calibration and validation sets.
Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging
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.
VNIR and SWIR Hyperspectral Imaging for Microplastic detection on Soil
Researchers applied VNIR (400-1000 nm) and SWIR (1000-2000 nm) hyperspectral imaging to detect and identify seven types of cryo-milled microplastic polymers mixed into soil surfaces. Partial least squares regression models successfully distinguished polymer types, offering a non-destructive, rapid screening approach for identifying microplastics directly in soil environments.
VNIR and SWIR Hyperspectral Imaging for Microplastic detection on Soil
Researchers used non-destructive hyperspectral imaging in visible-near infrared and short-wave infrared ranges to detect microplastics on soil surfaces. Using seven different cryo-milled microplastic polymers and partial least squares analysis, the study demonstrates that hyperspectral imaging can identify microplastics in soil without the complicated, time-consuming steps required by conventional detection methods.
Application of Near-infrared Spectroscopy and Multiple Spectral Algorithms to Explore the Effect of Soil Particle Sizes on Soil Nitrogen Detection
Researchers applied near-infrared spectroscopy with machine learning algorithms to rapidly measure soil nitrogen content. While focused on agricultural management rather than microplastics, spectroscopic methods like near-infrared are also used for detecting microplastics in soil samples.
Rapid Detection of Microplastics in Plastic-covered Soil Using FT-NIR and ATR-FTIR Spectral Data Fusion
Scientists developed a faster way to detect tiny plastic particles in farm soil by combining two different scanning methods. This new technique can accurately measure microplastic pollution in agricultural fields where plastic covers are used to help crops grow. This matters because microplastics in farm soil can potentially enter our food supply, so having better detection methods helps us monitor and control this type of pollution.
Efficient screening of microplastics in soils using hyperspectral imaging in the short-wave infrared range coupled with machine learning – A laboratory-based experiment
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.
Research on Soil Microplastics Detection Algorithm based on Hyperspectral Imaging Technology
Researchers developed a soil microplastic detection algorithm using hyperspectral imaging (400-1000 nm wavelength range) combined with three supervised classification approaches -- Support Vector Machine (SVM), Mahalanobis Distance (MD), and a third algorithm -- to enable convenient and efficient identification and classification of microplastic pollutants in soil.
Innovative approach for determining polypropylene microplastics pollution in calcareous soils: Vis-NIR spectroscopy
Researchers demonstrated that visible and near-infrared (Vis-NIR) spectroscopy combined with statistical modeling can accurately detect and quantify polypropylene microplastics in agricultural calcareous soils, with a model accuracy of R² = 0.91. This is promising because it could enable rapid, low-cost field screening of soil microplastic contamination without expensive laboratory analysis.
A novel way to rapidly monitor microplastics in soil by hyperspectral imaging technology and chemometrics
Hyperspectral imaging combined with chemometrics was demonstrated as a novel way to rapidly detect and map multiple types of microplastics in soil samples, identifying particles of different polymer types based on their spectral signatures. The approach could enable faster and more spatially detailed monitoring of microplastic contamination in agricultural and environmental soils.
High-throughput NIR spectroscopic (NIRS) detection of microplastics in soil
High-throughput near-infrared spectroscopy (NIRS) was evaluated for detecting and quantifying microplastics in soil samples, finding that it could rapidly identify multiple polymer types without time-consuming sample preparation. The method offers potential for scaling up microplastic monitoring in terrestrial environments where conventional analytical methods are too slow for large sample numbers.
Quantitative Analysis of Microplastics in Soil Using Near-Infrared Spectroscopy
This master's thesis examines the use of near-infrared spectroscopy as a quantitative analytical method for detecting and measuring microplastic concentrations in soil samples, assessing its potential as a faster alternative to conventional microplastic quantification techniques.
Application of FTIR-ATR spectroscopy in the detection of microplastics in Croatian agricultural soils
Researchers applied FTIR-ATR spectroscopy to detect and characterize microplastics in agricultural soil samples from Croatia, identifying polymer types, particle shapes, and size distributions. Multiple polymer types were detected across all sampled fields, with polyethylene and polypropylene most common, and higher contamination levels found near areas with intensive plastic mulch film use.
Comparison of learning models to predict LDPE, PET, and ABS concentrations in beach sediment based on spectral reflectance
Researchers compared machine learning models to predict concentrations of LDPE, PET, and ABS microplastics in beach sediments using visible-near-infrared spectral reflectance, demonstrating that spectroscopic methods can efficiently estimate microplastic pollution in understudied terrestrial and coastal environments.
μATR-FTIR Spectral Libraries of Plastic Particles (FLOPP and FLOPP-e) for the Analysis of Microplastics
Researchers developed two novel FTIR spectral libraries (FLOPP and FLOPP-e) specific to microplastic particles, including weathered samples, demonstrating improved spectral matching accuracy for identifying environmental microplastics compared to conventional polymer databases.
Detection of microplastic pollution in top soils using optical reflectance spectroscopy from the ultraviolet to shortwave infrared: a review
This review examined the potential of optical reflectance spectroscopy across the ultraviolet to shortwave infrared range as a detection method for microplastic pollution in soils. Researchers assessed the current state of spectroscopic approaches for soil microplastic identification, highlighting both the promise of this non-destructive technique and the key challenges that must be overcome for reliable field and laboratory application.
Toward high-precision analysis of soil micro-and nanoplastics: A review of spectroscopy and machine learning approaches
Researchers reviewed multiple spectroscopy techniques — including infrared, Raman, and hyperspectral imaging — combined with machine learning as faster, cheaper alternatives to traditional methods for detecting microplastics and nanoplastics in soil. While promising, key challenges remain including poor detection of nanoplastics, limited real-world validation, and detection limits that often miss environmentally relevant concentrations.
Vis-NIR spectroscopy based rapid and non-destructive method to quantitate microplastics: An emerging contaminant in farm soil
Researchers developed a rapid, non-destructive method using visible and near-infrared spectroscopy to quantify microplastics in farm soil. The study suggests this approach could overcome the limitations of current extraction-based methods, which are time-consuming and prone to errors and biases.
Degradation degree analysis of environmental microplastics by micro FT-IR imaging technology
Researchers used micro-FTIR spectral-image fusion to classify the degradation degree of polyethylene microplastics collected from coastal environments, achieving 97.1% classification accuracy and enabling estimation of environmental persistence time from spectral data.