Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil

Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors

With the massive release of microplastics (MPs) into the environment, research related to MPs is advancing rapidly. Effective research methods are necessary to identify the chemical composition, shape, distribution, and environmental impacts of MPs. In recent years, artificial intelligence (AI)-driven machine learning methods have demonstrated excellent performance in analyzing MPs in soil and water. This review provides a comprehensive overview of machine learning methods for the prediction of MPs for various tasks, and discusses in detail the data source, data preprocessing, algorithm principle, and algorithm limitation of applied machine learning. In addition, this review discusses the limitation of current machine learning methods for various task analysis in MPs along with future prospect. Finally, this review finds research potential in future work in building large generalized MPs datasets, designing high-performance but low-computational-complexity algorithms, and evaluating model interpretability.

Navigating the nexus: climate dynamics and microplastics pollution in coastal ecosystems

Microplastics (MPs) pollution is an emerging environmental health concern, impacting soil, plants, animals, and humans through their entry into the food chain via bioaccumulation. Human activities such as improper solid waste dumping are significant sources that ultimately transport MPs into the water bodies of the coastal areas. Moreover, there is a complex interplay between the coastal climate dynamics, environmental factors, the burgeoning issue of MPs pollution and the complex web of coastal pollution. We embark on a comprehensive journey, synthesizing the latest research across multiple disciplines to provide a holistic understanding of how these inter-connected factors shape and reshape the coastal ecosystems. The comprehensive review also explores the impact of the current climatic patterns on coastal regions, the intricate pathways through which MPs can infiltrate marine environments, and the cascading effects of coastal pollution on ecosystems and human societies in terms of health and socio-economic impacts in coastal regions. The novelty of this review concludes the changes in climate patterns have crucial effects on coastal regions, proceeding MPs as more prevalent, deteriorating coastal ecosystems, and hastening the transfer of MPs. The continuous rising sea levels, ocean acidification, and strong storms result in habitat loss, decline in biodiversity, and economic repercussion. Feedback mechanisms intensify pollution effects, underlying the urgent demand for environmental conservation contribution. In addition, the complex interaction between human, industry, and biodiversity demanding cutting edge strategies, innovative approaches such as remote sensing with artificial intelligence for monitoring, biobased remediation techniques, global cooperation in governance, policies to lessen the negative socioeconomic and environmental effects of coastal pollution.

Critical evaluation of hyperspectral imaging technology for detection and quantification of microplastics in soil

In this study, we critically evaluated the performance of an emerging technology, hyperspectral imaging (HSI), for detecting microplastics (MPs) in soil. We examined the technology's robustness against varying environmental conditions in five groups of experiments. Our findings show that near-infrared (NIR) hyperspectral imaging (HSI) effectively detects microplastics (MPs) in soil, though detection efficacy is influenced by factors such as MP concentration, color, and soil moisture. We found a generally linear relationship between the levels of MPs in various soils and their spectral responses in the NIR HSI imaging spectrum. However, effectiveness is reduced for certain MPs, like polyethylene, in kaolinite clay. Furthermore, we showed that soil moisture considerably influenced the detection of MPs, leading to nonlinearities in quantification and adding complexities to spectral analysis. The varied responses of MPs of different sizes and colors to NIR HSI present further challenges in detection and quantification. The research suggests pre-grouping of MPs based on size before analysis and proposes further investigation into the interaction between soil moisture and MP detectability to enhance HSI's application in MP monitoring and quantification. To our knowledge, this study is the first to comprehensively evaluate this technology for detecting and quantifying microplastics.

Artificial intelligence-empowered collection and characterization of microplastics: A review

Microplastics have been detected from water and soil systems extensively, with increasing evidence indicating their detrimental impacts on human and animal health. Concerns surrounding microplastic pollution have spurred the development of advanced collection and characterization methods for studying the size, abundance, distribution, chemical composition, and environmental impacts. This paper offers a comprehensive review of artificial intelligence (AI)-empowered technologies for the collection and characterization of microplastics. A framework is presented to streamline efforts in utilizing emerging robotics and machine learning technologies for collecting, processing, and characterizing microplastics. The review encompasses a range of AI technologies, delineating their principles, strengths, limitations, representative applications, and technology readiness levels, facilitating the selection of suitable AI technologies for mitigating microplastic pollution. New opportunities for future research and development on integrating robots and machine learning technologies are discussed to facilitate future efforts for mitigating microplastic pollution and advancing AI technologies.

Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils

Soil, particularly in agricultural regions, has been recognized as one of the significant reservoirs for the emerging contaminant of MPs. Therefore, developing a rapid and efficient method is critical for their identification in soil. Here, we coupled HSI systems [i.e., VNIR (400-1000 nm), InGaAs (800-1600 nm), and MCT (1000-2500 nm)] with machine learning algorithms to distinguish soils spiked with white PE and PA (average size of 50 and 300 µm, respectively). The soil-normalized SWIR spectra unveiled significant spectral differences not only between control soil and pure MPs (i.e., PE 100% and PA 100%) but also among five soil-MPs mixtures (i.e., PE 1.6%, PE 6.9%, PA 5.0%, and PA 11.3%). This was primarily attributable to the 1st-3rd overtones and combination bands of C-H groups in MPs. Feature reductions visually demonstrated the separability of seven sample types by SWIR and the inseparability of five soil-MPs mixtures by VNIR. The detection models achieved higher accuracies using InGaAs (92-100%) and MCT (97-100%) compared to VNIR (44-87%), classifying 7 sample types. Our study indicated the feasibility of InGaAs and MCT HSI systems in detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soil. SYNOPSIS: One of two SWIR HSI systems (i.e., InGaAs and MCT) with a sample imaging surface area of 3.6 mm² per grid cell was sufficient for detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soils without the digestion and separation procedures.

Protein content prediction of rice grains based on hyperspectral imaging

This study utilized hyperspectral imaging technology combined with mathematical modeling methods to predict the protein content of rice grains. Firstly, the Kjeldahl method was used to determine the protein content of rice grains, and different preprocessing techniques were applied to the spectral information. Then, a prediction model for rice grain protein content was developed by combining the spectral data with the protein content. After performing multiplicative scatter correction (MSC) preprocessing and selecting feature wavelengths based on successive projections algorithm (SPA), the multivariate linear regression (MLR) model showed the best prediction performance, with a calibration set R2C of 0.9393, a validation set R2V of 0.8998, an RMSEV of 0.1725, and an RPD of 3.16. Finally, the quantitative protein content model was mapped pixel by pixel to visualize the distribution of rice protein, providing possibilities for non-destructive protein content detection.

Analysis of micro(nano)plastics based on automated data interpretation and modeling: A review

The widespread presence of micro(nano)plastics (MNPs) in the environment threatens ecosystem integrity, and thus, it is necessary to determine and assess the occurrence, characteristics, and transport of MNPs between ecological components. However, most analytical approaches are cost- and time-inefficient in providing quantitative information with sufficient detail, and interpreting results can be difficult. Alternative analyses integrating novel measurements by imaging or proximal sensing with signal processing and machine learning may supplement these approaches. In this review, we examined published research on methods used for the automated data interpretation of MNPs found in the environment or those artificially prepared by fragmenting bulk plastics. We critically reviewed the primary areas of the integrated analytical process, which include sampling, data acquisition, processing, and modeling, applied in identifying, classifying, and quantifying MNPs in soil, sediment, water, and biological samples. We also provide a comprehensive discussion regarding model uncertainties related to estimating MNPs in the environment. In the future, the development of routinely applicable and efficient methods is expected to significantly contribute to the successful establishment of automated MNP monitoring systems.

Recognition and detection technology for microplastic, its source and health effects

Microplastic (MP) is ubiquitous in the environment which appeared as an immense intimidation to human and animal health. The plastic fragments significantly polluted the ocean, fresh water, food chain, and other food items. Inadequate maintenance, less knowledge of adverse influence along with inappropriate usage in addition throwing away of plastics items revolves present planet in to plastics planet. The present study aims to focus on the recognition and advance detection technologies for MPs and the adverse effects of micro- and nanoplastics on human health. MPs have rigorous adverse effect on human health that leads to condensed growth rates, lessened reproductive capability, ulcer, scrape, and oxidative nervous anxiety, in addition, also disturb circulatory and respiratory mechanism. The detection of MP particles has also placed emphasis on identification technologies such as scanning electron microscopy, Raman spectroscopy, optical detection, Fourier transform infrared spectroscopy, thermo-analytical techniques, flow cytometry, holography, and hyperspectral imaging. It suggests that further research should be explored to understand the source, distribution, and health impacts and evaluate numerous detection methodologies for the MPs along with purification techniques.

Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering

The maturity of kiwifruit is widely gauged by its soluble solids content (SSC), with accurate assessment being essential to guarantee the fruit's quality. Hyperspectral imaging offers a non-destructive alternative to traditional destructive methods for SSC evaluation, though its efficacy is often hindered by the redundancy and external disturbances of spectral images. This study aims to enhance the accuracy of SSC predictions by employing feature engineering to meticulously select optimal spectral features and mitigate disturbance effects. We conducted a comprehensive investigation of four spectral pre-processing and nine spectral feature selection methods, as components of feature engineering, to determine their influence on the performance of a linear regression model based on ordinary least squares (OLS). Additionally, the stacking generalization technique was employed to amalgamate the strengths of the two most effective models derived from feature engineering. Our findings demonstrate a considerable improvement in SSC prediction accuracy post feature engineering. The most effective model, when considering both feature engineering and stacking generalization, achieved an R M S E p of 0.721, a M A P E p of 0.046, and an R P D p of 1.394 in the prediction set. The study confirms that feature engineering, especially the careful selection of spectral features, and the stacking generalization technique are instrumental in bolstering SSC prediction in kiwifruit. This advancement enhances the application of hyperspectral imaging for quality assessment, offering benefits that extend across the agricultural industry.

Satellite hyperspectral imaging technology as a potential rapid pollution assessment tool for urban landfill sites: case study of Ghazipur and Okhla landfill sites in Delhi, India

Hyperspectral imaging technology has been used for biochemical analysis of Earth's surface exploiting the spectral reflectance signatures of various materials. The new-generation Italian PRISMA (PRecursore IperSpettrale dellaMissione Applicativa) hyperspectral satellite launched by the Italian space agency (ASI) provides a unique opportunity to map various materials through spectral signature analysis for recourse management and sustainable development. In this study PRISMA hyperspectral satellite imagery-based multiple spectral indices were generated for rapid pollution assessment at Ghazipur and Okhla landfill sites in Delhi, India. It was found that the combined risk score for Okhla landfill site was higher than the Ghazipur landfill site. Various manmade materials identified, exploiting the hyperspectral imagery and spectral signature libraries, indicated presence of highly saline water, plastic (black, ABS, pipe, netting, etc.), asphalt tar, black tar paper, kerogen BK-Cornell, black paint and graphite, chalcocite minerals, etc. in large quantities in both the landfill sites. The methodology provides a rapid pollution assessment tool for municipal landfill sites.

Machine learning: Next promising trend for microplastics study

Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.

Formation, behavior, properties and impact of micro- and nanoplastics on agricultural soil ecosystems (A Review)

Micro and nanoplastics (MPs and NPs, respectively) in agricultural soil ecosystems represent a pervasive global environmental concern, posing risks to soil biota, hence soil health and food security. This review provides a comprehensive and current summary of the literature on sources and properties of MNPs in agricultural ecosystems, methodology for the isolation and characterization of MNPs recovered from soil, MNP surrogate materials that mimic the size and properties of soil-borne MNPs, and transport of MNPs through the soil matrix. Furthermore, this review elucidates the impacts and risks of agricultural MNPs on crops and soil microorganisms and fauna. A significant source of MPs in soil is plasticulture, involving the use of mulch films and other plastic-based implements to provide several agronomic benefits for specialty crop production, while other sources of MPs include irrigation water and fertilizer. Long-term studies are needed to address current knowledge gaps of formation, soil surface and subsurface transport, and environmental impacts of MNPs, including for MNPs derived from biodegradable mulch films, which, although ultimately undergoing complete mineralization, will reside in soil for several months. Because of the complexity and variability of agricultural soil ecosystems and the difficulty in recovering MNPs from soil, a deeper understanding is needed for the fundamental relationships between MPs, NPs, soil biota and microbiota, including ecotoxicological effects of MNPs on earthworms, soil-dwelling invertebrates, and beneficial soil microorganisms, and soil geochemical attributes. In addition, the geometry, size distribution, fundamental and chemical properties, and concentration of MNPs contained in soils are required to develop surrogate MNP reference materials that can be used across laboratories for conducting fundamental laboratory studies.

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.

Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology

Microplastics (MPs) in farming soils can have a substantial impact on soil ecology and agricultural productivity, as well as affecting human health and the food chain cycle. As a result, it is vital to study MPs detection technologies that are rapid, efficient, and accurate in agriculture soils. This study investigated the classification and detection of MPs using hyperspectral imaging (HSI) technology and a machine learning methodology. To begin, the hyperspectral data was preprocessed using SG convolution smoothing and Z-score normalization. Second, the feature variables were extracted from the preprocessed spectral data using bootstrapping soft shrinkage, model adaptive space shrinkage, principal component analysis, isometric mapping (Isomap), genetic algorithm, successive projections algorithm (SPA), and uninformative variable elimination. Finally, three support vector machine (SVM), back propagation neural network (BPNN), and one-dimensional convolutional neural network (1D-CNN) models were developed to classify and detect three microplastic polymers: polyethylene, polypropylene, and polyvinyl chloride, as well as their combinations. According to the experimental results, the best approaches based on three models were Isomap-SVM, Isomap-BPNN, and SPA-1D-CNN. Among them, the accuracy, precision, recall and F1_score of Isomap-SVM were 0.9385, 0.9433, 0.9385 and 0.9388, respectively. The accuracy, precision, recall and F1_score of Isomap-BPNN were 0.9414, 0.9427, 0.9414 and 0.9414, respectively, while the accuracy, precision, recall and F1_score of SPA-1D-CNN were 0.9500, 0.9515, 0.9500 and 0.9500, respectively. When their classification accuracy was compared, SPA-1D-CNN had the best classification performance, with a classification accuracy of 0.9500. The findings of this study shown that the SPA-1D-CNN based on HSI technology can efficiently and accurately identify MPs in farmland soils, providing theoretical backing as well as technical means for real-time detection of MPs in farmland soils.

Robust near-infrared-based plastic classification with relative spectral similarity pattern

Sensor-based material flow characterization techniques, particularly hyperspectral imaging in the near-infrared (NIR) range, can recognize materials quickly, accurately, and economically. When identifying materials using NIR hyperspectral imaging, extracting influential features from high-dimensional wavelength information is essential for effective recognition. However, spectral noise from the rough and contaminated surfaces of objects (especially un-shredded waste) degrades the feature-extraction performance, which in turn deteriorates the material classification performance. In this study, we propose a real-time feature-extraction method, named relative spectral similarity pattern color mapping (RSSPCM), to robustly classify materials in noisy environments, such as plastic waste sorting facilities. RSSPCM compares relative intra- and inter-class spectral similarity patterns, instead of individual similarity, to class-representative spectra alone. Recognition targets have similar chemical makeups that are applied to feature extraction as an intra-class similarity ratio. The proposed model is robust owing to the remaining relative similarity trends found in a contaminated spectrum. We evaluated the effectiveness of the proposed method using noisy samples obtained from a waste-management facility. The results were compared with two spectral groups obtained at different noise levels. Both results showed high accuracy as there was an increased number of true positives for low-reflectance regions. The average F1-score values were 0.99 and 0.96 for low- and high-noise sets, respectively. Furthermore, the proposed method showed minimal F1-score variations between classes (standard deviation of 0.026 for the high-noise set).

Terahertz Spectroscopy Characterization and Prediction of the Aging Degree of Polyethylene Pipes Based on PLS

Polyethylene (PE) is widely used in pipeline transportation owing to its excellent corrosion resistance, good stability, and ease of processing. As organic polymer materials, PE pipes inevitably undergo different degrees of aging during long-term use. In this study, terahertz time-domain spectroscopy was used to study the spectral characteristics of PE pipes with different degrees of photothermal aging, and the variation in the absorption coefficient with aging time was obtained. The absorption coefficient spectrum was extracted using uninformative variable elimination (UVE), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and random frog RF spectral screening algorithms, and the spectral slope characteristics of the aging-sensitive band were selected as the evaluation indices of the degree of PE aging. Based on this, a partial least squares aging characterization model was established to predict white PE80, white PE100 and black PE100 pipes with different aging degrees. The results showed that the prediction accuracy of the absorption coefficient spectral slope feature prediction model for the aging degree of different types of pipes was greater than 93.16% and the verification set error was within 13.5 h.

Microplastics occurrence, detection and removal with emphasis on insect larvae gut microbiota

Microplastics have been identified in all living forms including human beings, the present need is to restrain its spread and devise measures to remediate microplastics from polluted ecosystems. In this regard, the present review emphasizes on the occurrence, sources detection and toxic effects of microplastics in various ecosystems. The removal of microplastics is prevalent by various physico-chemical and biological methods, although the removal efficiency by biological methods is low. It has been noted that the degradation of plastics by insect gut larvae is a well-known aspect, however, the underlying mechanism has not been completely identified. Studies conducted have shown the magnificent contribution of gut microbiota, which have been isolated and exploited for microplastic remediation. This review also focuses on this avenue, as it highlights the contribution of insect gut microbiota in microplastic degradation along with challenges faced and future prospects in this area.

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the R p 2 , R c 2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the R p 2 , R c 2 , and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality.

Identification of soil heavy metal pollution by constructing 2D plane using hyperspectral index

This study proposes a method for the rapid identification of elements in soil heavy metal pollution using spectral indices. Set up a simulation experiment of planting crops in soil polluted by multi-gradient Cu and Pb. The obtained polluted soil spectral data was initially pre-processed to obtain the original spectrum (OR), the continuum removed spectrum (CR), and the first-order differential spectrum (FOD). Then the preliminary model of soil heavy metal pollution index (SHMPI) was constructed. Using the correlation optimal algorithm, the maximum median distance algorithm, and the maximum average distance algorithm to select the optimal bands corresponding to the OR, CR, and FOD. The optimal bands selected by each algorithm were substituted into the SHMPI. Each algorithm obtains three indices, and two of them were selected as the x-axis and y-axis to form a two-dimensional pollution identification plane. Nine two-dimensional planes can be obtained by three algorithms and three combinations of OR, CR, and FOD. Support vector machine classifier was used to classify the Cu and Pb polluted samples in the planes, and nine classification models to distinguish Cu and Pb pollution in soil were constructed. The results show that using the correlation optimal algorithm to extract the optimal bands, and using OR and CR to construct SHMPI, the accuracy of the classification line model of Cu and Pb pollution obtained was 93% in the training group and 86% in the validation group. This method can stably and effectively identify the types of heavy metal pollution in soil, and can also effectively identify whether the soil is polluted by heavy metals, which is expected to guide the rapid and non-destructive identification of heavy metal pollution in polluted areas, and provide new ideas for the identification of other types of heavy metals in soil.

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

A review of microplastics in soil: Occurrence, analytical methods, combined contamination and risks

Microplastics (MPs) pollution is becoming a serious environmental issue of global concern. Currently, the effects of MPs on aquatic ecosystems have been studied in detail and in depth from species to communities. However, soils, the largest reservoir of MPs, have been less studied, and little is known about the occurrence, environmental fate and ecological impacts of MPs. Therefore, based on the existing knowledge, this paper firstly focused specifically on the main sources of soil MPs pollution and explored the main reasons for their strong heterogeneity in spatial distribution. Secondly, as a primary prerequisite for evaluating MPs contamination, we systematically summarized the analytical methods for soil MPs and critically compared the advantages and disadvantages of the different methods in the various operational steps. Furthermore, this review highlighted the combined contamination of MPs with complex chemical contaminants, the sorption mechanisms and the associated factors in the soil. Finally, the risks posed by MPs to soil, plants, the food chain and even humans were outlined, and future directions for soil MPs research were proposed, while the urgent need for a unified approach to MPs extraction and identification was emphasized. This study provides a theoretical reference for a comprehensive understanding of the separation of soil MPs and their ecological risk as carriers of pollution.

Visible-NIR hyperspectral classification of grass based on multivariate smooth mapping and extreme active learning approach

Grass community classification is the basis for the development of animal husbandry and dynamic monitoring of environment, which has become a critical problem to further strengthen the intelligent management of grassland. Compared with grass survey based on satellite remote sensing, the visible near infrared (NIR) hyperspectral not only monitor dynamically in a short distance, but also have high dimensions and detailed spectral information in each pixel. However, the hyperspectral labeled sample for classification is expensive and manual selection is more subjective. In order to solve above limitations, we proposed a visible-NIR hyperspectral classification model for grass based on multivariate smooth mapping and extreme active learning (MSM–EAL). Firstly, MSM is used to preprocess and reconstruct the spectrum. Secondly, by jointing XGBoost and active learning (AL), the advanced samples with the largest amount of information are actively selected to improve the performance of target classification. Innovation lies in: (1) MSM global enhanced preprocessing spectral reconstruction algorithm is proposed, in which isometric feature mapping is effectively applied to the grass hyperspectral for the first time. (2) EAL framework is constructed to solve the issue of high cost and small number for hyperspectral labeled samples, at the same time, enhance the physical essence behind spectral classification more intuitively. A field hyperspectral collection platform is assembled to establish nm resolution visible-NIR hyperspectral dataset of grass, Grass1, containing 750 samples, which to verify the effectiveness of the model. Experiments on the Grass1 dataset confirmed that compared with the full spectrum, the time consumption of MSM was reduced by 9.471 s with guaranteed overall accuracy (OA). Comparing EAL with AL, and other classification algorithms, EAL improves OA 22.2% over AL, and XAL has the best performance value on Kappa, Macro, Recall and F1-score, respectively. Altogether, the lightweight MSM–EAL model realizes intelligent and real-time classification, providing a new method for obtaining high-precision inter group classification of grass.

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.