Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Food & Water Human Health Effects Marine & Wildlife Policy & Risk Remediation Sign in to save

A Hybrid MIR-spectrum Processing Algorithm for Microplastics Analysis

2024 2 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 50 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Qikun Yang, Zhengke Chen, Qikun Yang, Qikun Yang, Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Qikun Yang, Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Bowen He, Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Ting Xia, Ting Xia, Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Qikun Yang, Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Qikun Yang, Dongyu Cui, Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Qikun Yang, Qikun Yang, Dongyu Cui, Zhuoqing Yang, Qikun Yang, Qikun Yang, Wei Huang Wei Huang Qikun Yang, Qikun Yang, Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Zhuoqing Yang, Wei Huang Wei Huang Wei Huang Wei Huang Qikun Yang, Wei Huang Faheng Zang, Wei Huang Wei Huang Qikun Yang, Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang Wei Huang

Summary

Researchers developed a hybrid algorithm for classifying microplastics using their mid-infrared spectral signatures, targeting polypropylene, polyethylene, and polystyrene. The model combines principal component analysis with machine learning techniques to improve classification accuracy. The study offers an automated approach that could make routine microplastic identification faster and more reliable for environmental monitoring.

Polymers

PE PP PS

Microplastics with sizes in microscale can be collected by marine animals and enter our food chain, causing potential harms to the environment and health. Accurate classification of microplastics can help trace their sources and mechanism of degradation, which is a key part of marine environmental monitoring. In this study, a microplastic classification model is proposed to classify polypropylene (PP), polyethylene (PE) and polystyrene (PS) using their mid-infrared spectral signatures. Both Principal Component Analysis-Support Vector Machine(PCA-SVM) and Linear Discriminant Analysis(LDA) methods were integrated and analyzed for more accurate prediction based on the field sample spectra training. The developed LDA algorithm demonstrated an accuracy of more than 95% compared to the PCA-SVM algorithm. The results demonstrate the potential of the model in improving the accuracy and efficiency of detecting and classifying microplastics.

Read via DOI

Share this paper

Post Share Share Pin Email