Component identification for the SERS spectra of microplastics mixture with convolutional neural network

Plasmonic coffee-ring biosensing for AI-assisted point-of-care diagnostics

A major challenge in addressing global health issues is developing simple, affordable biosensors with high sensitivity and specificity. Significant progress has been made in at-home medical detection kits, especially during the COVID-19 pandemic. Here, we demonstrated a coffee-ring biosensor with ultrahigh sensitivity, utilizing the evaporation of two sessile droplets and the formation of coffee-rings with asymmetric nanoplasmonic patterns to detect disease-relevant proteins as low as 3 pg/ml, under 12 min. Experimentally, a protein-laden droplet dries on a nanofibrous membrane, pre-concentrating biomarkers at the coffee ring. A second plasmonic droplet with functionalized gold nanoshells is then deposited at an overlapping spot and dried, forming a visible asymmetric plasmonic pattern due to distinct aggregation mechanisms. To enhance detection sensitivity, a deep neural model integrating generative and convolutional networks was used to enable quantitative biomarker diagnosis from smartphone photos. We tested four different proteins, Procalcitonin (PCT) for sepsis, SARS-CoV-2 Nucleocapsid (N) protein for COVID-19, Carcinoembryonic antigen (CEA) and Prostate-specific antigen (PSA) for cancer diagnosis, showing a working concentration range over five orders of magnitude. Sensitivities surpass equivalent lateral flow immunoassays by over two orders of magnitude using human saliva samples. The detection principle, along with the device, and materials can be further advanced for early disease diagnostics.

MPs Entering Human Circulation through Infusions: A Significant Pathway and Health Concern

Human uptake of microplastic particles (MPs) is causing increasing health concerns, and there is mounting pressure to evaluate the associated risks. While MPs can be ingested, breathed in, or drank in, a very direct entrance channel is available through ingress into the bloodstream. Intravenous infusion usually proceeds from plastic bottles. Many are made of polypropylene (PP), and filtering is applied to limit particle contamination. In this study, we examined the MPs' content of filtrates using a combination of surface-enhanced Raman spectroscopy and scanning electron microscopy. We find that the number of PP particles is significant (∼7500 particles/L). The MP sizes range from 1 to 62 μm, with a median of ∼8.5 μm. About 90% of particles ranged between 1 and 20 μm in size, with ∼60% in the range 1 to 10 μm. We then discuss the potential number of such particles injected and the consequences of their presence in the bloodstream. We highlight the organs for potential deposition, and we discuss possible clinical effects. Our quantitative data are important to help evaluate the toxicity risks associated with MPs and to accurately balance those risks versus the benefits of using intravenous injections.

SpecRecFormer: Deep Learning-Driven Adaptive Component Identification of PAH Mixtures Based on Single-Component Raman Spectra

The identification of components in mixed spectra is a fundamental challenge in spectral analysis, complicated by factors such as spectral peak overlap due to structural similarities, shifts in characteristic peaks from molecular interactions, and interferences caused by matrix effects. While deep learning offers robust feature extraction capabilities and notable advantages in addressing these challenges, it still faces significant obstacles, including the limited availability of labeled spectral data for effective training and the difficulty of applying fixed-threshold predictive models to spectra containing uncertain components. This paper established a deep learning model, SpecRecFormer, for the rapid identification of individual components in mixed polycyclic aromatic hydrocarbons (PAHs) based on their Raman spectra. The model integrates a dual-channel convolutional neural network (CNN) for local feature extraction with a Transformer module for global representation. It is trained on a reference database composed of single-component spectra, with simulated mixed spectra generated through data augmentation to expand and diversify the training set. This architecture enables the model to evaluate the similarity between unknown mixed spectra and known single-component references. To further enhance recognition accuracy, an adaptive threshold strategy is introduced, dynamically adjusting decision thresholds based on spectral characteristics to retain only components exceeding the threshold as candidate predictions. Experimental results demonstrate that with training data derived from only four single-component reference spectra, the model generalizes effectively to three real-world PAH data sets, achieving accuracies of 93.75%, 89.21%, and 93.63%, respectively, significantly outperforming conventional neural network models. These findings present an innovative and highly effective approach to mixed spectral analysis, with substantial potential for advancing applications in environmental science and chemical analysis.

Plasmon Enhanced Universal SERS Detection of Hierarchical Plastics by 3D Plasmonic Funnel Metastructure

Plasmonic nanostructures have aroused tremendous excitement in extreme light matter interactions because of efficient light harvesting and nanometer field concentration, ideal for solar thermal conversion, photocatalysis, photodetection, etc. Here a 3D self-assembled plasmonic nanostructure is reported for ultrasensitive SERS detection of hierarchical micro-nano plastic pollutants ranging from 30 nm to microns by rationally integrating high density of both surface and volumetric hot spots into one structure, enabled by V-shaped close-packed bi-metallic nanoparticles with massive nanovoids across transverse and longitudinal areas. The unique bi-metallic structure of hollow nanocones can enable an enhancement factor up to 1.1 × 108 as well as self-built enrichment of targeting hierarchical analytes toward the size-matched hot spot areas, resulting in not only race detection of micro-nano plastics with concentration down to 10-8 g L-1 but also universal adaptability to simultaneous detection of a broad range of pollutants beyond micro-nano plastics. The results offer a practical solution for trace detection of hierarchical micro-nano plastics and other mixed aqueous pollutants, demonstrating considerable potential for combating water pollution.

Machine learning-integrated surface-enhanced Raman spectroscopy analysis of multicomponent dye mixtures

Surface-enhanced Raman spectroscopy (SERS) is broadly used in the detection and analysis of materials with its fingerprint-like specificity and high sensitivity. However, resembling signals of analytes highly affect the identification and assignment of spectra, which has become a long-term issue to be solved. In this study, various models of machine learning are utilized and compared to support data analysis of complex SERS spectra. Silver-coated gold core-shell nanocubes (Au@AgNCs) are optimized as SERS substrates for the detection of four common dyes - methylene blue (MB), crystal violet (CV), rhodamine B (RhB) and malachite green (MG). Independent principal component analysis (ICA) was utilized to isolate the signals from the SERS spectra of the dye mixtures, and the isolated signals were further classified by commonly used classification models including K Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN). The results show that the CNN model achieved an accuracy of 98% in the classification of single dyes and an accuracy of 97% in the classification of dye mixtures, which is significantly better than other models. Based on these findings, we propose ICA combined with CNN-assisted SERS spectroscopy as an effective analytical tool for analyzing dye mixtures.

Discovery and solution for microplastics: New risk carriers in food

Microplastics (MPs), as a kind of plastic particles with an equal volume size of less than 5 mm, similar to PM2.5 in the air, are causing severe contamination issues in food. Along with the food chain accumulation, they have been confirmed to appear in daily foods and cause serious health risks to the organisms. However, there were no unifying national and local policies on separating, extracting, and detecting MPs in food, which is an essential and imperative early-warning strategy. This review carefully and comprehensively summarized the validated contaminated food, physical and chemical characteristics, extraction methods, traditional and rapid detection techniques, as well as degradation methods of MPs. We thoroughly analyzed the differences among these traditional strategies, and innovatively generalized the existing rapid detection techniques for MPs. Finally, the shortcomings of existing research were discussed, and the possibility of novel rapid and intelligent detection techniques for MPs in food was proposed.

Automatic microplastic classification using dual-modality spectral and image data for enhanced accuracy

The development of an automatic microplastic (MPs) classification system using spectra is crucial due to the time-consuming and error-prone nature of analyzing individual spectra, especially with a large quantity of MPs. This study presents a classification system using a dual-modality dataset from micro-Fourier Transform Infrared Spectroscopy (μFTIR) for five common polymer types: polypropylene, polystyrene, polyethylene terephthalate, polyethylene, and polyamide. A comparison of machine learning models, including Decision Tree (DT), Extremely Randomized Trees (ET), Support Vector Classifier (SVC), and Multiclass Logistic Regression (LR), is conducted using features extracted by AlexNet, ResNet18, and Vision Transformer (ViT). Notably, the AlexNet with Logistic Regression (AlexNet-LR) model demonstrated exceptional performance, achieving a validation accuracy of 99.03 % and nearly perfect test scores of 99.99 %. However, ResNet18-LR was selected for web deployment due to its shorter training and inference times compared to AlexNet-LR, while still achieving 99 % validation and test accuracy. This highlights the effectiveness of using a dual-modality dataset for precise microplastic classification. MPsSpecClassify, a web-based application, was developed to enable users to efficiently identify MPs and improve microplastic pollution management.

Exploring Innovative Approaches for the Analysis of Micro- and Nanoplastics: Breakthroughs in (Bio)Sensing Techniques

Plastic pollution, particularly from microplastics (MPs) and nanoplastics (NPs), has become a critical environmental and health concern due to their widespread distribution, persistence, and potential toxicity. MPs and NPs originate from primary sources, such as cosmetic microspheres or synthetic fibers, and secondary fragmentation of larger plastics through environmental degradation. These particles, typically less than 5 mm, are found globally, from deep seabeds to human tissues, and are known to adsorb and release harmful pollutants, exacerbating ecological and health risks. Effective detection and quantification of MPs and NPs are essential for understanding and mitigating their impacts. Current analytical methods include physical and chemical techniques. Physical methods, such as optical and electron microscopy, provide morphological details but often lack specificity and are time-intensive. Chemical analyses, such as Fourier transform infrared (FTIR) and Raman spectroscopy, offer molecular specificity but face challenges with smaller particle sizes and complex matrices. Thermal analytical methods, including pyrolysis gas chromatography-mass spectrometry (Py-GC-MS), provide compositional insights but are destructive and limited in morphological analysis. Emerging (bio)sensing technologies show promise in addressing these challenges. Electrochemical biosensors offer cost-effective, portable, and sensitive platforms, leveraging principles such as voltammetry and impedance to detect MPs and their adsorbed pollutants. Plasmonic techniques, including surface plasmon resonance (SPR) and surface-enhanced Raman spectroscopy (SERS), provide high sensitivity and specificity through nanostructure-enhanced detection. Fluorescent biosensors utilizing microbial or enzymatic elements enable the real-time monitoring of plastic degradation products, such as terephthalic acid from polyethylene terephthalate (PET). Advancements in these innovative approaches pave the way for more accurate, scalable, and environmentally compatible detection solutions, contributing to improved monitoring and remediation strategies. This review highlights the potential of biosensors as advanced analytical methods, including a section on prospects that address the challenges that could lead to significant advancements in environmental monitoring, highlighting the necessity of testing the new sensing developments under real conditions (composition/matrix of the samples), which are often overlooked, as well as the study of peptides as a novel recognition element in microplastic sensing.

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.

Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review

Surface-enhanced Raman spectroscopy (SERS) has gained significant attention for its ability to detect environmental contaminants with high sensitivity and specificity. The cost-effectiveness and potential portability of the technique further enhance its appeal for widespread application. However, challenges such as the management of voluminous quantities of high-dimensional data, its capacity to detect low-concentration targets in the presence of environmental interferents, and the navigation of the complex relationships arising from overlapping spectral peaks have emerged. In response, there is a growing trend toward the use of machine learning (ML) approaches that encompass multivariate tools for effective SERS data analysis. This comprehensive review delves into the detailed steps needed to be considered when applying ML techniques for SERS analysis. Additionally, we explored a range of environmental applications where different ML tools were integrated with SERS for the detection of pathogens and (in)organic pollutants in environmental samples. We sought to comprehend the intricate considerations and benefits associated with ML in these contexts. Additionally, the review explores the future potential of synergizing SERS with ML for real-world applications.

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.

A Study of High-Frequency Noise for Microplastics Classification Using Raman Spectroscopy and Machine Learning

Given the growing urge for plastic management and regulation in the world, recent studies have investigated the problem of plastic material identification for correct classification and disposal. Recent works have shown the potential of machine learning techniques for successful microplastics classification using Raman signals. Classification techniques from the machine learning area allow the identification of the type of microplastic from optical signals based on Raman spectroscopy. In this paper, we investigate the impact of high-frequency noise on the performance of related classification tasks. It is well-known that classification based on Raman is highly dependent on peak visibility, but it is also known that signal smoothing is a common step in the pre-processing of the measured signals. This raises a potential trade-off between high-frequency noise and peak preservation that depends on user-defined parameters. The results obtained in this work suggest that a linear discriminant analysis model cannot generalize properly in the presence of noisy signals, whereas an error-correcting output codes model is better suited to account for inherent noise. Moreover, principal components analysis (PCA) can become a must-do step for robust classification models, given its simplicity and natural smoothing capabilities. Our study on the high-frequency noise, the possible trade-off between pre-processing the high-frequency noise and the peak visibility, and the use of PCA as a noise reduction technique in addition to its dimensionality reduction functionality are the fundamental aspects of this work.

Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network

With the increasing interest in microplastics (MPs) pollutants, quantitative analysis of MPs in water environment is an important issue. Vibrational spectroscopy, represented by Raman spectroscopy, is widely used in MP detection because they can provide unique fingerprint characteristics of chemical components of MPs, but it is difficult to provide quantitative information. In this paper, an ingenious method for quantitative analysis of MPs in water environment by combining Raman spectroscopy and convolutional neural network (CNN) is proposed. It is innovatively proposed to collect the average mapping spectra (AMS) of the samples to improve the uniformity of Raman spectroscopy detection, and to increase the effective detection range of concentration by filtering different volumes of the same MP solutions. In order to verify the universality and effectiveness of the proposed method, 6 different sizes of Polyethylene (PE) MPs were used as detection objects and mixed into 5 different actual water environments. The R2 and RMSE of CNN for identifying the concentration of PE solutions could reach 0.9972 and 0.033, respectively. Meanwhile, by comparing machine learning models such as Random Forest (RF) and Support Vector Machine (SVM) were compared, and CNN combined with Raman spectroscopy has significant advantages in identifying the concentration of MPs.

Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy

In light of the growing awareness regarding the ubiquitous presence of microplastics (MPs) in our environment, recent efforts have been made to integrate Artificial Intelligence (AI) technology into MP detection. Among spectroscopic techniques, Raman spectroscopy is preferred for the detection of MP particles measuring less than 10 μm, as it overcomes the diffraction limitations encountered in Fourier transform infrared (FTIR). However, Raman spectroscopy's inherent limitation is its low scattering cross section, which often results in prolonged data collection times during practical sample measurements. In this study, we implemented a convolutional neural network (CNN) model alongside a tailored data interpolation strategy to expedite data collection for MP particles within the 1-10 μm range. Remarkably, we achieved the classification of plastic types for individual particles with a mere 0.4 s of exposure time, reaching an approximate confidence level of 85.47(±5.00)%. We postulate that the result significantly accelerates the aggregation of microplastic distribution data in diverse scenarios, contributing to the development of a comprehensive global microplastic map.

Supervised classification combined with genetic algorithm variable selection for a fast identification of polymeric microdebris using infrared reflectance

Pollution caused by plastics and, in particular, microplastics has become a source of environmental concern for Society. Their ubiquity, with millions of tons of plastic debris spilled in both land and sea, requires efficient technological improvements in the ways residues are collected, handled, characterized and recycled. For reliable decision-making, dependable chemical information is essential to assess both the nature of the plastics found in the environment and their fate. In this work an efficient method to identify the polymeric composition of microplastic fragments is proposed. It combines infrared reflectance spectra and chemometric methods. A breakthrough result is that the models include polymers weathered under both dry (shoreline) and submerged (in sea water) conditions and, hence, they are very promising as a starting point for eventual practical applications. In addition, no spectral processing is required after the initial measurement. SYNOPSIS: This approach to identify microplastics in aquatic environments combines infrared measurements and multivariate data analysis to fight against (micro)plastic pollution.