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61,005 resultsShowing papers similar to Vis-NIR spectroscopy based rapid and non-destructive method to quantitate microplastics: An emerging contaminant in farm soil
ClearPredicting 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.
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.
Development of a low-cost method for quantifying microplastics in soils and compost using near-infrared spectroscopy
Near-infrared spectroscopy was developed as a low-cost, non-destructive method to quantify microplastic mass in soil and compost samples, with sensitivity improved through spectral preprocessing techniques. The method offers a practical alternative to time-consuming visual counting or expensive chemical analysis for high-throughput soil monitoring.
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.
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.
A novel and simple method for measuring nano/microplastic concentrations in soil using UV-Vis spectroscopy with optimal wavelength selection
Researchers developed a simple UV-Vis spectroscopy method for measuring nano- and microplastic concentrations in soil, using optimized wavelength combinations to account for interference from soil particles. The study demonstrated a linear relationship between spectroscopic measurements and actual plastic concentrations, offering a potentially practical tool for monitoring plastic contamination across different soil types.
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.
Automated identification and quantification of invisible microplastics in agricultural soils
Researchers developed an automated method combining laser direct infrared and FTIR spectroscopy to identify microplastics in agricultural soils, revealing that particles smaller than 500 micrometers account for over 96% of soil microplastics that are invisible to traditional visual inspection.
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.
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.
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.
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.
Microplastics in Agricultural Soil: Detection Techniques, Challenges, Limitation and Future Research Direction - a Review
Researchers review the current methods for detecting microplastics in agricultural soil, comparing techniques like spectroscopy and microscopy while highlighting the limitations of each approach, including false negatives and size-detection constraints. Developing faster, more accurate detection tools is critical because microplastics are accumulating in farmland worldwide and their full impact on soil health and food safety remains poorly understood.
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.
What is the most effective analytical method for quantification and identification of microplastics in contaminated soils?
Researchers compared four analytical methods for detecting microplastics in agricultural soils: near-infrared spectroscopy, scanning electron microscopy, multispectral analysis, and X-ray diffraction. They found that near-infrared spectroscopy and multispectral analysis were the most sensitive and accurate, while X-ray diffraction could only detect microplastics at very high concentrations. The study provides practical guidance for selecting the most effective technique for monitoring microplastic contamination in farmland soils.
Towards a fast and generalized microplastic quantification method in soil using terahertz spectroscopy
Researchers compared terahertz and near-infrared spectroscopy for quantifying microplastics in soil, finding that terahertz spectroscopy offered a faster and more accurate approach than NIR for distinguishing household microplastics from standard reference polymers in soil matrices.
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.
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.
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.
A Review of Analytical Methods for Microplastics in Soils
This review systematically examines analytical methods for detecting and quantifying microplastics in soil, including visual analysis, chemical analysis, spectroscopic techniques, microscopy, and mass spectrometry. The authors evaluate the advantages, limitations, and scope of each method, noting that no single technique covers all particle sizes and polymer types. The review calls for continued innovation in analytical methods to provide more effective tools for addressing soil microplastic pollution.
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.
Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil
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.
A Preliminary Study on the Utilization of Hyperspectral Imaging for the On-Soil Recognition of Plastic Waste Resulting from Agricultural Activities
Researchers explored the use of near-infrared hyperspectral imaging to detect and identify plastic waste in agricultural soils. They developed a classification model that could distinguish different types of plastic from soil and assess the degradation state of the material. The study demonstrates that hyperspectral imaging combined with chemometric analysis offers a rapid, non-destructive approach for monitoring plastic contamination in agricultural environments.
Quantification of Microplastics in Soils Using Accelerated Solvent Extraction: Comparison with a Visual Sorting Method
Researchers evaluated accelerated solvent extraction as an alternative to visual sorting for quantifying microplastics in soil, finding it recovered similar total amounts but with some differences by polymer type. Improving the accuracy and efficiency of soil microplastic measurement is essential for understanding agricultural and terrestrial plastic contamination.