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

Study on Rapid Quantitative Detection of Soil MPs Based on Terahertz Time-Domain Spectroscopy

The presence of microplastics (MPs) in agricultural soils substantially affects the growth, reproduction, feeding, survival, and immunity levels of soil biota. Therefore, it is crucial to investigate fast, effective, and accurate techniques for the detection of soil MPs. This work explores the integration of terahertz time-domain spectroscopy (THz-TDS) techniques with machine learning algorithms to develop a method for the classification and detection of MPs. First, THz spectral image data were preprocessed using moving average (MA). Subsequently, three classification models were developed, including random forest (RF), linear discriminant analysis, and support vector machine (SVM). Notably, the SVM model had an F1 score of 0.9817, demonstrating its ability to rapidly classify MPs in soil samples. Three regression models, namely, principal component regression (PCR), RF, and least squares support vector machine (LSSVM), were developed for the detection of three MPs polymers in agricultural soils. Six feature extraction methods were used to extract the relevant parts of the data containing key information. The results of the study showed that the regression accuracies of PCR, RF, and LSSVM were greater than 83%. Among them, the RF had the highest overall regression accuracy. Notably, PE-UVE-RF had the best performance with Rc2, Rp2, root mean square error of calibration, and root mean square error of prediction values of 0.9974, 0.9916, 0.1595, and 0.2680, respectively. Furthermore, this model gets a better performance by hypothesis testing and predicting real samples.

Modeling the temporal evolution of plastic film microplastics in soil using a backpropagation neural network

Plastic films are a crucial aspect of agricultural production in China, as well as a key source of microplastics in farmland. However, research into the environmental behavior of microplastics derived from polyethylene (PE) and biodegradable plastic films such as polybutylene adipate-co-terephthalate (PBAT) is limited by inadequate knowledge of their evolution and fate in soil. Therefore, we conducted controlled soil incubation experiments using new and aged microplastics derived from prepared PE and PBAT plastic films to determine their temporal evolution characteristics in soil. The results indicated that PBAT microplastics exhibited more pronounced changes in abundance, size, and shape over time than PE microplastics. Notably, the magnitude and timing of changes in newly introduced PBAT microplastics were consistently delayed relative to those of aged microplastics. Specifically, the abundance of aged PBAT microplastics initially increased then decreased, whereas their size continuously decreased, ultimately reaching 21.9 % and 47.5 % of the initial values, respectively. Furthermore, we constructed a novel backpropagation neural network model based on our morphological and spectral data, which effectively identified the incubation duration of PE and PBAT microplastics, with recognition accuracies of 98.1 % and 84.6 %, respectively. These findings offer a novel perspective for assessing the environmental persistence and fate of plastic film microplastics.

Artificial intelligence in microplastic detection and pollution control

The rising prevalence of microplastics (MPs) in various ecosystems has increased the demand for advanced detection and mitigation strategies. This review examines the integration of artificial intelligence (AI) with environmental science to improve microplastic detection. Focusing on image processing, Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and hyperspectral imaging (HSI), the review highlights how AI enhances the efficiency and accuracy of these techniques. AI-driven image processing automates the identification and quantification of MPs, significantly reducing the need for manual analysis. FTIR and Raman spectroscopy accurately distinguish MP types by analyzing their unique spectral features, while HSI captures extensive spatial and spectral data, facilitating detection in complex environmental matrices. Furthermore, AI algorithms integrate data from these methods, enabling real-time monitoring, traceability prediction, and pollution hotspot identification. The synergy between AI and spectral imaging technologies represents a transformative approach to environmental monitoring and emphasizes the need to adopt innovative tools for protecting ecosystem health.

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.

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.

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.

Lipid metabolism analysis in esophageal cancer and associated drug discovery

Esophageal cancer is an upper gastrointestinal malignancy with a bleak prognosis. It is still being explored in depth due to its complex molecular mechanisms of occurrence and development. Lipids play a crucial role in cells by participating in energy supply, biofilm formation, and signal transduction processes, and lipid metabolic reprogramming also constitutes a significant characteristic of malignant tumors. More and more studies have found esophageal cancer has obvious lipid metabolism abnormalities throughout its beginning, progress, and treatment resistance. The inhibition of tumor growth and the enhancement of antitumor therapy efficacy can be achieved through the regulation of lipid metabolism. Therefore, we reviewed and analyzed the research results and latest findings for lipid metabolism and associated analysis techniques in esophageal cancer, and comprehensively proved the value of lipid metabolic reprogramming in the evolution and treatment resistance of esophageal cancer, as well as its significance in exploring potential therapeutic targets and biomarkers.