An efficient and precise (micro)plastic identification method: feature infrared spectra extraction based on EIS-VIP-CARS and ANN modeling
Understanding microplastics' (MPs) ecological impact necessitates their precise identification. To address the issue of the competitive adaptive reweighted sampling (CARS) algorithm extracting numerous feature wavenumber points (FWPs) that often miss transmittance peaks (TPs), resulting in high...
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          | Published in | Environmental research Vol. 279; no. Pt 2; p. 121916 | 
|---|---|
| Main Authors | , , , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Netherlands
          Elsevier Inc
    
        15.08.2025
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| Subjects | |
| Online Access | Get full text | 
| ISSN | 0013-9351 1096-0953 1096-0953  | 
| DOI | 10.1016/j.envres.2025.121916 | 
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| Abstract | Understanding microplastics' (MPs) ecological impact necessitates their precise identification. To address the issue of the competitive adaptive reweighted sampling (CARS) algorithm extracting numerous feature wavenumber points (FWPs) that often miss transmittance peaks (TPs), resulting in high computational load and low accuracy in artificial neural network (ANN) models, this study introduces a novel approach. Initially, the equal interval sampling (EIS) method is employed to capture the main information of the full spectra. Subsequently, the variable importance in projection (VIP) is innovatively integrated into the CARS to formulate the EIS-VIP-CARS method for extracting feature spectra (FS). Using 20 typical MPs as the subjects, this study compares the identification performance of ANN models using full-spectra, EIS, CARS, EIS-CARS, VIP-CARS, and EIS-VIP-CARS. The results show that VIP-CARS extracts 128 FWPs, a reduction of 49.41 % compared to CARS. Moreover, the distribution of these FWPs is more concentrated around the TPs and their vicinity. The accuracy of MPs by the ANN model based on VIP-CARS is generally higher than that of CARS. EIS-VIP-CARS extracts 55 FWPs, representing a reduction of 58.65 % and 57.03 % compared to EIS and VIP-CARS, respectively. The overall distribution of these points closely aligns with the distribution of functional groups. The ANN model based on EIS-VIP-CARS can achieve a similar accuracy for MPs as the model based on EIS, both greater than 99 %, demonstrating good generalization ability. The accuracies of the MNN and convolutional neural network (CNN) models are higher than those of the SNN model, but the modeling time is longer. The ANN model established using the EIS-VIP-CARS is an efficient and precise approach for the identification of MPs in infrared spectroscopy. This study provides technical references for the research on the environmental behavior of MPs and is also of significant importance for the classification and management of plastic waste.
[Display omitted]
•Developed an EIS-VIP-CARS-based feature spectral extraction method.•VIP replaces AVRC in CARS to facilitate the capture of transmittance peaks.•Optimized hyperparameters to enhance the generalization ability of the ANN models.•CNN and MNN exhibit higher accuracies for MPs compared to SNN but require longer modeling times. | 
    
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| AbstractList | Understanding microplastics' (MPs) ecological impact necessitates their precise identification. To address the issue of the competitive adaptive reweighted sampling (CARS) algorithm extracting numerous feature wavenumber points (FWPs) that often miss transmittance peaks (TPs), resulting in high computational load and low accuracy in artificial neural network (ANN) models, this study introduces a novel approach. Initially, the equal interval sampling (EIS) method is employed to capture the main information of the full spectra. Subsequently, the variable importance in projection (VIP) is innovatively integrated into the CARS to formulate the EIS-VIP-CARS method for extracting feature spectra (FS). Using 20 typical MPs as the subjects, this study compares the identification performance of ANN models using full-spectra, EIS, CARS, EIS-CARS, VIP-CARS, and EIS-VIP-CARS. The results show that VIP-CARS extracts 128 FWPs, a reduction of 49.41 % compared to CARS. Moreover, the distribution of these FWPs is more concentrated around the TPs and their vicinity. The accuracy of MPs by the ANN model based on VIP-CARS is generally higher than that of CARS. EIS-VIP-CARS extracts 55 FWPs, representing a reduction of 58.65 % and 57.03 % compared to EIS and VIP-CARS, respectively. The overall distribution of these points closely aligns with the distribution of functional groups. The ANN model based on EIS-VIP-CARS can achieve a similar accuracy for MPs as the model based on EIS, both greater than 99 %, demonstrating good generalization ability. The accuracies of the MNN and convolutional neural network (CNN) models are higher than those of the SNN model, but the modeling time is longer. The ANN model established using the EIS-VIP-CARS is an efficient and precise approach for the identification of MPs in infrared spectroscopy. This study provides technical references for the research on the environmental behavior of MPs and is also of significant importance for the classification and management of plastic waste.
[Display omitted]
•Developed an EIS-VIP-CARS-based feature spectral extraction method.•VIP replaces AVRC in CARS to facilitate the capture of transmittance peaks.•Optimized hyperparameters to enhance the generalization ability of the ANN models.•CNN and MNN exhibit higher accuracies for MPs compared to SNN but require longer modeling times. Understanding microplastics' (MPs) ecological impact necessitates their precise identification. To address the issue of the competitive adaptive reweighted sampling (CARS) algorithm extracting numerous feature wavenumber points (FWPs) that often miss transmittance peaks (TPs), resulting in high computational load and low accuracy in artificial neural network (ANN) models, this study introduces a novel approach. Initially, the equal interval sampling (EIS) method is employed to capture the main information of the full spectra. Subsequently, the variable importance in projection (VIP) is innovatively integrated into the CARS to formulate the EIS-VIP-CARS method for extracting feature spectra (FS). Using 20 typical MPs as the subjects, this study compares the identification performance of ANN models using full-spectra, EIS, CARS, EIS-CARS, VIP-CARS, and EIS-VIP-CARS. The results show that VIP-CARS extracts 128 FWPs, a reduction of 49.41 % compared to CARS. Moreover, the distribution of these FWPs is more concentrated around the TPs and their vicinity. The accuracy of MPs by the ANN model based on VIP-CARS is generally higher than that of CARS. EIS-VIP-CARS extracts 55 FWPs, representing a reduction of 58.65 % and 57.03 % compared to EIS and VIP-CARS, respectively. The overall distribution of these points closely aligns with the distribution of functional groups. The ANN model based on EIS-VIP-CARS can achieve a similar accuracy for MPs as the model based on EIS, both greater than 99 %, demonstrating good generalization ability. The accuracies of the MNN and convolutional neural network (CNN) models are higher than those of the SNN model, but the modeling time is longer. The ANN model established using the EIS-VIP-CARS is an efficient and precise approach for the identification of MPs in infrared spectroscopy. This study provides technical references for the research on the environmental behavior of MPs and is also of significant importance for the classification and management of plastic waste. Understanding microplastics' (MPs) ecological impact necessitates their precise identification. To address the issue of the competitive adaptive reweighted sampling (CARS) algorithm extracting numerous feature wavenumber points (FWPs) that often miss transmittance peaks (TPs), resulting in high computational load and low accuracy in artificial neural network (ANN) models, this study introduces a novel approach. Initially, the equal interval sampling (EIS) method is employed to capture the main information of the full spectra. Subsequently, the variable importance in projection (VIP) is innovatively integrated into the CARS to formulate the EIS-VIP-CARS method for extracting feature spectra (FS). Using 20 typical MPs as the subjects, this study compares the identification performance of ANN models using full-spectra, EIS, CARS, EIS-CARS, VIP-CARS, and EIS-VIP-CARS. The results show that VIP-CARS extracts 128 FWPs, a reduction of 49.41% compared to CARS. Moreover, the distribution of these FWPs is more concentrated around the TPs and their vicinity. The accuracy of MPs by the ANN model based on VIP-CARS is generally higher than that of CARS. EIS-VIP-CARS extracts 55 FWPs, representing a reduction of 58.65% and 57.03% compared to EIS and VIP-CARS, respectively. The overall distribution of these points closely aligns with the distribution of functional groups. The ANN model based on EIS-VIP-CARS can achieve a similar accuracy for MPs as the model based on EIS, both greater than 99%, demonstrating good generalization ability. The accuracies of the MNN and convolutional neural network (CNN) models are higher than those of the SNN model, but the modeling time is longer. The ANN model established using the EIS-VIP-CARS is an efficient and precise approach for the identification of MPs in infrared spectroscopy. This study provides technical references for the research on the environmental behavior of MPs and is also of significant importance for the classification and management of plastic waste.Understanding microplastics' (MPs) ecological impact necessitates their precise identification. To address the issue of the competitive adaptive reweighted sampling (CARS) algorithm extracting numerous feature wavenumber points (FWPs) that often miss transmittance peaks (TPs), resulting in high computational load and low accuracy in artificial neural network (ANN) models, this study introduces a novel approach. Initially, the equal interval sampling (EIS) method is employed to capture the main information of the full spectra. Subsequently, the variable importance in projection (VIP) is innovatively integrated into the CARS to formulate the EIS-VIP-CARS method for extracting feature spectra (FS). Using 20 typical MPs as the subjects, this study compares the identification performance of ANN models using full-spectra, EIS, CARS, EIS-CARS, VIP-CARS, and EIS-VIP-CARS. The results show that VIP-CARS extracts 128 FWPs, a reduction of 49.41% compared to CARS. Moreover, the distribution of these FWPs is more concentrated around the TPs and their vicinity. The accuracy of MPs by the ANN model based on VIP-CARS is generally higher than that of CARS. EIS-VIP-CARS extracts 55 FWPs, representing a reduction of 58.65% and 57.03% compared to EIS and VIP-CARS, respectively. The overall distribution of these points closely aligns with the distribution of functional groups. The ANN model based on EIS-VIP-CARS can achieve a similar accuracy for MPs as the model based on EIS, both greater than 99%, demonstrating good generalization ability. The accuracies of the MNN and convolutional neural network (CNN) models are higher than those of the SNN model, but the modeling time is longer. The ANN model established using the EIS-VIP-CARS is an efficient and precise approach for the identification of MPs in infrared spectroscopy. This study provides technical references for the research on the environmental behavior of MPs and is also of significant importance for the classification and management of plastic waste.  | 
    
| ArticleNumber | 121916 | 
    
| Author | Li, Jun Ding, Qiang Qiang, Jing Zhang, Shuangsheng Zhou, Junjie Chen, Jian Liu, Hanhu Qian, Kuimei  | 
    
| Author_xml | – sequence: 1 givenname: Shuangsheng surname: Zhang fullname: Zhang, Shuangsheng organization: College of Environmental Engineering, Xuzhou University of Technology, Xuzhou, 221018, China – sequence: 2 givenname: Jing surname: Qiang fullname: Qiang, Jing email: qiangjingcumt@163.com organization: School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China – sequence: 3 givenname: Hanhu surname: Liu fullname: Liu, Hanhu organization: School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China – sequence: 4 givenname: Junjie orcidid: 0000-0002-6222-4515 surname: Zhou fullname: Zhou, Junjie organization: School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China – sequence: 5 givenname: Jun surname: Li fullname: Li, Jun organization: Huaibei City Drainage Co., Ltd, Huaibei, 235000, China – sequence: 6 givenname: Jian surname: Chen fullname: Chen, Jian organization: Huaibei City Drainage Co., Ltd, Huaibei, 235000, China – sequence: 7 givenname: Qiang surname: Ding fullname: Ding, Qiang organization: Beijing Capital Eco-Environmental Protection Group Co., Ltd, Beijing, 100032, China – sequence: 8 givenname: Kuimei orcidid: 0000-0002-0955-8102 surname: Qian fullname: Qian, Kuimei organization: College of Environmental Engineering, Xuzhou University of Technology, Xuzhou, 221018, China  | 
    
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40404082$$D View this record in MEDLINE/PubMed | 
    
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| Keywords | CARS PP PS DR FWPs ML HSI PBT EIS PCA POM MPs EVA TSS AVRC PCL Microplastics WPs ANN CNN PLA FTS POPs LDA SVM MNN FS PPO FT RMSECV ABS PPS SNN FGs FTIR PLS TPU SNT GA UMAP SBS Equal interval sampling PTFE CL kNN RMSE VIP score FTTS HLs NPs Feature spectra PC PMMA PE TPs PVA MCS VIP PVC PET PES  | 
    
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| SubjectTerms | Algorithms ANN CARS Environmental Monitoring - methods Equal interval sampling Feature spectra Microplastics Microplastics - analysis Neural Networks, Computer Spectrophotometry, Infrared - methods VIP score  | 
    
| Title | An efficient and precise (micro)plastic identification method: feature infrared spectra extraction based on EIS-VIP-CARS and ANN modeling | 
    
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