Rapid and direct detection of small microplastics in aquatic samples by a new near infrared hyperspectral imaging (NIR-HSI) method

Customized digestion protocols for copepods, euphausiids, chaetognaths and fish larvae facilitate the isolation of ingested microplastics

Degradation of oceanic plastic waste leads to the formation of microplastics that are ingested by a wide range of animals. Yet, the amounts that are taken up, especially by small zooplankton, are largely unknown. This is mostly due to the complex methodology that is required for isolating ingested microplastics from organisms. We developed customised, effective and benign digestion protocols for four important zooplankton taxa (copepods, euphausiids, chaetognaths and fish larvae), and assessed their digestion efficacy and their potential to cause particle loss or to alter microplastics using six polymers (HDPE, LDPE, PS, PET, PVC, PMMA). All protocols are based on an incubation of the organic matrix with 10% KOH at 38 °C, which is optionally combined with digestive enzymes (chitinase, proteinase K). This yielded digestion efficacies of > 98.2%, recovery rates of > 91.8%, < 2.4% change in microplastics' size, while no visual alteration of the microplastics and no changes in their spectra were observed when analysing them with a hyperspectral imaging camera. The proposed protocols are inexpensive (< 2.15 € per sample), but require several days when enzymatic digestion is included. They will facilitate research on microplastic ingestion by small marine organisms and thus enable well-founded conclusions about the threat that microplastics pose to these animals as well as about the role of biota in determining the vertical distribution of microplastics in oceanic environments.

Use of an uncrewed surface vehicle and near infrared hyperspectral imaging for sampling and analysis of aquatic microplastics

Data on MP in aquatic environments have low resolution in space and time. Scaling up sampling and increasing analysis throughput are the main bottlenecks. We combined two approaches: an uncrewed surface vehicle (USV) and near infrared hyperspectral imaging (NIR-HSI) for sampling and analysis of MP > 300 μm. We collected 35 water samples over 4 d in a coastal area. Samples were analyzed using NIR-HSI and Fourier transform infrared spectroscopy (FTIR). Spiked samples were used to determine recovery. We conclude that using a USV can mitigate issues of traditional trawls like scalability, repeatability, and contamination. NIR-HSI detects more polyethylene but less polypropylene than FTIR analysis and reduces analysis time significantly. Highly variable concentrations were found at both sampling locations, with mean MP concentration of 0.28 and 0.01 MP m-3 for location A and B respectively. USV sampling in tandem with NIR-HSI is an effective analytical pipeline for MP monitoring.

Detection of paralytic shellfish toxins by near-infrared spectroscopy based on a near-Bayesian SVM classifier with unequal misclassification costs

BACKGROUND

Paralytic shellfish poisoning caused by human consumption of shellfish fed on toxic algae is a public health hazard. It is essential to implement shellfish monitoring programs to minimize the possibility of shellfish contaminated by paralytic shellfish toxins (PST) reaching the marketplace.

RESULTS

This paper proposes a rapid detection method for PST in mussels using near-infrared spectroscopy (NIRS) technology. Spectral data in the wavelength range of 950-1700 nm for PST-contaminated and non-contaminated mussel samples were used to build the detection model. Near-Bayesian support vector machines (NBSVM) with unequal misclassification costs (u-NBSVM) were applied to solve a classification problem arising from the fact that the quantity of non-contaminated mussels was far less than that of PST-contaminated mussels in practice. The u-NBSVM model performed adequately on imbalanced datasets by combining unequal misclassification costs and decision boundary shifts. The detection performance of the u-NBSVM did not decline as the number of PST samples decreased due to adjustments to the misclassification costs. When the number of PST samples was 20, the G-mean and accuracy reached 0.9898 and 0.9944, respectively.

CONCLUSION

Compared with the traditional support vector machines (SVMs) and the NBSVM, the u-NBSVM model achieved better detection performance. The results of this study indicate that NIRS technology combined with the u-NBSVM model can be used for rapid and non-destructive PST detection in mussels. © 2023 Society of Chemical Industry.

Underappreciated microplastic galaxy biases the filter-based quantification

Long-term environmental loading of microplastics (MPs) causes alarming exposure risks for a variety of species worldwide, considered a planetary threat to the well-being of ecosystems. Robust quantitative estimates of MP extents and featured diversity are the basis for comprehending their environmental implications precisely, and of these methods, membrane-based characterizations predominate with respect to MP inspections. However, though crucial to filter-based MP quantification, aggregation statuses of retained MPs on these substrates remain poorly understood, leaving us a "blind box" that exaggerates uncertainty in quantitive strategies of preselected areas without knowing overview loading structure. To clarify this uncertainty and estimate their impacts on MP counting, using MP imaging data assembled from peer-reviewed studies through a systematic review, here we analyze the particle-specific profiles of MPs retained on various substrates according to their centre of mass with a fast-random forests algorithm. We visualize the formation of distinct galaxy-like MP aggregation-similar to the solar system and Milky Way System comprised of countless stars-across the pristine and environmental samples by leveraging two spatial parameters developed in this study. This unique pattern greatly challenges the homogeneously or randomly distributed MP presumption adopted extensively for simplified membrane-based quantification purposes and selective ROI (region of interest) estimates for smaller-sized plastics down to the nano-range, as well as the compatibility theory using pristine MPs as the standard to quantify the presence of environmental MPs. Furthermore, our evaluation with exemplified numeration cases confirms these location-specific and area-dependent biases in many imaging analyses of a selective filter area, ascribed to the minimum possibility of reaching an ideal turnover point for the selective quantitive strategies. Consequently, disproportionate MP schemes on loading substrates yield great uncertainty in their quantification processing, highlighting the prompt need to include pattern-resolved calibration prior to quantification. Our findings substantially advance our understanding of the structure, behavior, and formation of these MP aggregating statuses on filtering substrates, addressing a fundamental question puzzling scientists as to why reproducible MP quantification is barely achievable even for subsamples. This study inspires the following studies to reconsider the impacts of aggregating patterns on the effective counting protocols and target-specific removal of retained MP aggregates through membrane separation techniques.

Developing and testing a workflow to identify microplastics using near infrared hyperspectral imaging

The analysis of microplastics (MP) is time-consuming which limits our capacity to monitor and mitigate plastic pollution. Here, near infrared (1000-2500 nm) hyperspectral imaging (NIR-HSI) offers an advantage over other spectroscopic techniques because it can rapidly image large areas relative to other systems. While NIR-HSI can successfully detect MP, accuracy and limitations of the method have not been fully explored. In addition, lack of open databases and analysis pipelines increases the barrier to use. In this work, we developed a spectral database containing preproduction pellets, consumer products and marine plastic debris, imaged using a Hyspex SWIR-320me imager. A SIMCA model identified four polymer types: polypropylene, polyethylene, polyethylene terephthalate and polystyrene (PP, PE, PET, PS) to identify MP in hyperspectral images. We determined the accuracy of size estimates for PS MP > 1000 μm using fluorescence microscopy and tested the impact of photooxidation on detection of plastics by NIR-HSI (PE, PP, PS, PET) and subsequent prediction by the SIMCA model. The model performed well across all polymers as shown by high specificity, sensitivity, and accuracy for internal cross validation (>88%), and sensitivity >80% for external validation. PS MP < 500 μm Feret diameter were not consistently detected by NIR-HSI when compared with fluorescence microscopy. However, estimates for Feret diameter were consistent for PS MP > 1000 μm. Analysis by NIR-HSI showed no spectral changes and SIMCA showed no decreased precision with increased photooxidation across polymer types. Recall varied across polymer type and photooxidation stage with no clear trends. This study shows that NIR-HSI is a rapid method which can accurately identify MP of the four most relevant polymer types, precluding the need to analyze particles one at a time. NIR-HSI can be a key technology for environmental monitoring of plastic debris where rapid analysis of multiple samples is required.

Developing a biogas centralised circular bioeconomy using agricultural residues - Challenges and opportunities

Anaerobic digestion (AD) can be used as a stand-alone process or integrated as part of a larger biorefining process to produce biofuels, biochemicals and fertiliser, and has the potential to play a central role in the emerging circular bioeconomy (CBE). Agricultural residues, such as animal slurry, straw, and grass silage, represent an important resource and have a huge potential to boost biogas and methane yields. Under the CBE concept, there is a need to assess the long-term impact and investigate the potential accumulation of specific unwanted substances. Thus, a comprehensive literature review to summarise the benefits and environmental impacts of using agricultural residues for AD is needed. This review analyses the benefits and potential adverse effects related to developing biogas-centred CBE. The identified potential risks/challenges for developing biogas CBE include GHG emission, nutrient management, pollutants, etc. In general, the environmental risks are highly dependent on the input feedstocks and resulting digestate. Integrated treatment processes should be developed as these could both minimise risks and improve the economic perspective.

Recent approaches and advanced wastewater treatment technologies for mitigating emerging microplastics contamination - A critical review

Microplastics have been identified as an emerging pollutant due to their irrefutable prevalence in air, soil, and particularly, the aquatic ecosystem. Wastewater treatment plants (WWTPs) are seen as the last line of defense which creates a barrier between microplastics and the environment. These microplastics are discharged in large quantities into aquatic bodies due to their insufficient containment during water treatment. As a result, WWTPs are regarded as point sources of microplastics release into the environment. Assessing the prevalence and behavior of microplastics in WWTPs is therefore critical for their control. The removal efficiency of microplastics was 65 %, 0.2-14 %, and 0.2-2 % after the successful primary, secondary and tertiary treatment phases in WWTPs. In this review, other than conventional treatment methods, advanced treatment methods have also been discussed. For the removal of microplastics in the size range 20-190 μm, advanced treatment methods like membrane bioreactors, rapid sand filtration, electrocoagulation and photocatalytic degradation was found to be effective and these methods helps in increasing the removal efficiency to >99 %. Bioremediation based approaches has found that sea grasses, lugworm and blue mussels has the ability to mitigate microplastics by acting as a natural trap to the microplastics pollutants and could act as candidate species for possible incorporation in WWTPs. Also, there is a need for controlling the use and unchecked release of microplastics into the environment through laws and regulations.

Probing nanoplastics derived from polypropylene face masks with hyperspectral dark-field microscopy

The high worldwide consumption of cheap plastic goods has already resulted in a serious environmental plastic pollution, exacerbated by piling of disposed personal protective equipment because of the recent outbreak of COVID-19. The aim of this study was to assess the feasibility of dark-field hyperspectral microscopy in the 400-1000 wavelength range for detection of nanoplastics derived from weathered polypropylene masks. A surgical mask was separated to layers and exposed to UV radiation (254 nm) for 192 h. Oxidative degradation of the polypropylene was evidenced by ATR FT-IR analysis. UV treatment for 192 h resulted in generation of differently shaped micro- and nano-sized particles, visualized by dark-field microscopy. The presence of nanoparticles was confirmed by AFM studies. The hyperspectral profiles (400-1000 nm) were collected after every 48 h of the UV treatment. The distinct hyperspectral features faded after prolonged UV exposure, but the assignment of some particles to either blue or white layers of mask could still be made based on spectral characteristics.

Label-Free Live-Cell Imaging of Internalized Microplastics and Cytoplasmic Organelles with Multicolor CARS Microscopy

As the bioaccumulation of microplastics (MPs) is considered as a potential health risk, many efforts have been made to understand the cellular dynamics and cytotoxicity of MPs. Here, we demonstrate that label-free multicolor coherent anti-Stokes Raman scattering (CARS) microscopy enables separate vibrational imaging of internalized MPs and lipid droplets (LDs) with indistinguishable shapes and sizes in live cells. By simultaneously obtaining polystyrene (PS)- and lipid-specific CARS images at two very different frequencies, 1000 and 2850 cm^-1, respectively, we successfully identify the local distribution of ingested PS beads and native LDs in Caenorhabditis elegans. We further show that the movements of PS beads and LDs in live cells can be separately tracked in real time, which allows us to characterize their individual intracellular dynamics. We thus anticipate that our multicolor CARS imaging method could be of great use to investigate the cellular transport and cytotoxicity of MPs without additional efforts for pre-labeling to MPs.

Nanomechanical Atomic Force Microscopy to Probe Cellular Microplastics Uptake and Distribution

The concerns regarding microplastics and nanoplastics pollution stimulate studies on the uptake and biodistribution of these emerging pollutants in vitro. Atomic force microscopy in nanomechanical PeakForce Tapping mode was used here to visualise the uptake and distribution of polystyrene spherical microplastics in human skin fibroblast. Particles down to 500 nm were imaged in whole fixed cells, the nanomechanical characterization allowed for differentiation between internalized and surface attached plastics. This study opens new avenues in microplastics toxicity research.

An effective strategy for the monitoring of microplastics in complex aquatic matrices: Exploiting the potential of near infrared hyperspectral imaging (NIR-HSI)

Contamination by microplastics (MP) represents a critical environmental challenge with potential consequences at ecosystem, economic and societal levels. As the marine system is the final sink for MP, there is an urgent need to develop methods for the monitoring of synthetic particles in different marine compartments and sample matrices. Extensive evaluations are hindered by time and costs associated with to conventional MP spectroscopic analyses. The potential of near infrared hyperspectral imaging (NIR-HSI) has been recently evaluated. However, NIR-HSI has been poorly studied so far, limitedly to the detection of large particles (>300 μm), and its capability for direct characterization of MP in real marine matrices has not been considered yet. In the present study, a rapid near infrared hyperspectral imaging (NIR-HSI) method, coupled with a customised normalised difference image (NDI) strategy for data processing, is presented and used to detect MP down to 50 μm in environmental matrices. The proposed method is largely automated, without the need for extensive data processing, and enabled a successful identification of different polymers, both in surface water and mussel soft tissue samples, as well as on real field samples with environmentally occurring MP. NIR-HSI is applied directly on filters, without the need for particles pre-sorting or multiple sample purifications, avoiding time consuming procedures, airborne contaminations, particle degradation and loss. Thanks to the time and cost effectiveness, a large-scale implementation of this method would enable to extensively monitor the MP presence in natural environments for assessing the ecological risk related to MP contamination.

Label-free identification of microplastics in human cells: dark-field microscopy and deep learning study

The development of an automatic method of identifying microplastic particles within live cells and organisms is crucial for high-throughput analysis of their biodistribution in toxicity studies. State-of-the-art technique in the data analysis tasks is the application of deep learning algorithms. Here, we propose the approach of polystyrene microparticle classification differing only in pigmentation using enhanced dark-field microscopy and a residual neural network (ResNet). The dataset consisting of 11,528 particle images has been collected to train and evaluate the neural network model. Human skin fibroblasts treated with microplastics were used as a model to study the ability of ResNet for classifying particles in a realistic biological experiment. As a result, the accuracy of the obtained classification algorithm achieved up to 93% in cell samples, indicating that the technique proposed will be a potent alternative to time-consuming spectral-based methods in microplastic toxicity research.

Application of spectral features for separating homochromatic foreign matter from mixed congee

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A method that can separate homochromatic FM in mixed congee was proposed.

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Spectral features of FM and mixed congee were extracted to build recognition model.

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The SVM model achieved high identification rates (99.17%) for homochromatic FM.

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The proposed method is better than computer vision in separating homochromatic FM.

Foreign matter (FM) in mixed congee not only reduces the quality of the congee but may also harm consumers. However, the common computer vision methods with poor recognition ability for the homochromatic FM. This study used hyperspectral reflectance images with the pattern recognition model to detect homochromatic FM on the mixed congee surface. First, spectral features corresponding to homochromatic FM and background were extracted from hyperspectral images. Then, based on the optimal spectral preprocessing method, LDA, K-nearest neighbor, backpropagation artificial neural network, and support vector machine (SVM) were used to classify the spectral features. The results revealed that the SVM model input with raw spectra principal components exhibited optimal identification rates of 99.17%. Finally, most of the pixels for homochromatic FM were classified correctly by using the SVM model. To summarized, hyperspectral images combined with pattern recognition are an effective method for recognizing homochromatic FM in mixed congee.

A comprehensive and fast microplastics identification based on near-infrared hyperspectral imaging (HSI-NIR) and chemometrics

Microplastic pollution is a global concern theme, and there is still the need for less laborious and faster analytical methods aiming at microplastics detection. This article describes a high throughput screening method based on near-infrared hyperspectral imaging (HSI-NIR) to identify microplastics in beach sand automatically with minimum sample preparation. The method operates directly in the entire sample or on its retained fraction (150 μm-5 mm) after sieving. Small colorless microplastics (<600 μm) that would probably be imperceptible as a microplastic by visual inspection, or missed during manual pick up, can be easily detected. No spectroscopic subsampling was performed due to the high-speed analysis of line-scan instrumentation, allowing multiple microplastics to be assessed simultaneously (video available). This characteristic is an advantage over conventional infrared (IR) spectrometers. A 75 cm² scan area was probed in less than 1 min at a pixel size of 156 × 156 μm. An in-house comprehensive spectral dataset, including weathered microplastics, was used to build multivariate supervised soft independent modelling of class analogy (SIMCA) classification models. The chemometric models were validated for hundreds of microplastics (primary and secondary) collected in the environment. The effect of particle size, color and weathering are discussed. Models' sensitivity and specificity for polyethylene (PE), polypropylene (PP), polyamide-6 (PA), polyethylene terephthalate (PET) and polystyrene (PS) were over 99% at the defined statistical threshold. The method was applied to a sand sample, identifying 803 particles without prior visual sorting, showing automatic identification was robust and reliable even for weathered microplastics analyzed together with other matrix constituents. The HSI-NIR-SIMCA described is also applicable for microplastics extracted from other matrices after sample preparation. The HSI-NIR principals were compared to other common techniques used to microplastic chemical characterization. The results show the potential to use HSI-NIR combined with classification models as a comprehensive microplastic-type characterization screening.

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides unprecedented opportunities for the detection and monitoring of floating riverine and marine plastic debris. However, a major challenge in the application of RS techniques is the lack of a fundamental understanding of spectral signatures of water-borne plastic debris. Recent work has emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present and analyse a high-resolution hyperspectral image database of a unique mix of 40 virgin macroplastic items and vegetation. Our double camera setup covered the visible to shortwave infrared (VIS-SWIR) range from 400 to 1700 nm in a darkroom experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. Using the resulting reflectance spectra of 1.89 million pixels in linear discriminant analyses (LDA), we determined the importance of each spectral band for discriminating between water and mixed floating debris, and vegetation and plastics. The absorption peaks of plastics (1215 nm, 1410 nm) and vegetation (710 nm, 1450 nm) are associated with high LDA weights. We then compared Sentinel-2 and Worldview-3 satellite bands with these outcomes and identified 12 satellite bands to overlap with important wavelengths for discrimination between the classes. Lastly, the Normalised Vegetation Difference Index (NDVI) and Floating Debris Index (FDI) were calculated to determine why they work, and how they could potentially be improved. These findings could be used to enhance existing efforts in monitoring macroplastic pollution, as well as form a baseline for the design of future multispectral RS systems.

Dark-field hyperspectral microscopy for label-free microplastics and nanoplastics detection and identification in vivo: A Caenorhabditis elegans study

Microplastics pollution is a serious ecological threat, severely affecting environments and human health. Tackling microplastics pollution requires an effective methodology to detect minute polymer particles in environmental samples and organisms. Here were report a novel methodology to visualise and identify nanoscale (down to 100 nm) and microscale synthetic commercially-available uniform spherical polymer particles using dark-field hyperspectral microscopy in visible-near infrared (400-1000 nm) wavelength range. Polystyrene particles with diameters between 100 nm-1 μm, polymethacrylate 1 μm and melamine formaldehyde 2 μm microspheres suspended in pure water samples were effectively imaged and chemically identified based on spectral signatures and image-assisted analysis. We succeeded in visualisation and spectral identification of pure and mixed nano- and microplastics in vivo employing optically-transparent Caenorhabditis elegans nematodes as a model to demonstrate the ingestion and tissue distribution of microplastics. As we demonstrate here, dark-field hyperspectral microscopy is capable for differentiating between chemically-different microplastics confined within live invertebrate intestines. Moreover, this optical technology allows for quantitative identification of microplastics ingested by nematodes. We believe that this label-free non-destructive methodology will find numerous applications in environmental nano- and microplastics detection and quantification, investigation of their biodistribution in tissues and organs and nanotoxicology.

Near-infrared hyperspectral imaging (NIR-HSI) and normalized difference image (NDI) data processing: An advanced method to map collagen in archaeological bones

In the present study, an innovative and highly efficient near-infrared hyperspectral imaging (NIR-HSI) method is proposed to provide spectral maps able to reveal collagen distribution in large-size bones, also offering semi-quantitative estimations. A recently introduced method for the construction of chemical maps, based on Normalized Difference Images (NDI), is declined in an innovative approach, through the exploitation of the NDI values computed for each pixel of the hyperspectral image to localize collagen and to extract information on its content by a direct comparison with known reference samples. The developed approach addresses an urgent issue of the analytical chemistry applied to bioarcheology researches, which rely on well-preserved collagen in bones to obtain key information on chronology, paleoecology and taxonomy. Indeed, the high demand for large-sample datasets and the consequent application of a wide variety of destructive analytical methods led to the considerable destruction of precious bone samples. NIR-HSI pre-screening allows researchers to properly select the sampling points for subsequent specific analyses, to minimize costs and time and to preserve integrity of archaeological bones (which are available in a very limited amount), providing further opportunities to understand our past.