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Multi-Angle Scattering and Image-Derived Feature
Summary
Researchers built an AI-powered system that can identify the type of microplastic (like PVC or nylon) and measure how much is present in a sample, using light-scattering patterns and image analysis instead of slower, more expensive lab methods. This kind of tool matters because faster, cheaper microplastic detection could help scientists and regulators better track these pollutants in water, food, and the environment—an important step toward understanding how much plastic we're actually exposed to and what it might mean for our health.
This repository contains the complete dataset, raw data, and reproducibility code supporting research into the automated classification and concentration quantification of microplastics using optical scattering and image-derived features. The work establishes a dual-pipeline framework designed to characterize Nylon and PVC microplastics efficiently: Concentration Regression Pipeline: Utilizes multi-angle scattering intensity profiles ($S_1, S_2, S_3, S_4$) across a concentration range of $0.100$ to $9.983$ to predict sample concentrations via non-linear regressors (e.g., Support Vector Regression with RBF kernel and Random Forests). Polymer Classification Pipeline: Leverages image-derived morphological features to classify the polymer type, evaluated against computer vision benchmarks. File Descriptions RAW_DATASET.zip: NYLON and PVC imaging final_dataset.csv: The clean, curated dataset containing aligned multi-angle scattering values ($S_1$–$S_4$), morphological image-derived parameters, concentration target values, and ground-truth polymer labels (NYLON / PVC). (Note: The companion executable Jupyter Notebook final_code.ipynb handles the end-to-end execution of these files, from preprocessing to model tuning). Methodology & Machine Learning Framework The framework addresses a rigorous validation protocol to ensure robustness and reproducibility: Hyperparameter Tuning & Validation: Implements systematic K-Fold Cross-Validation alongside dedicated hold-out testing to monitor variance and prevent data leakage. Metrics Evaluated: Regression models are assessed via RMSE, MAE, $R^2$, and calculated Limits of Detection/Quantification (LOD/LOQ). Classification pipelines are benchmarked using Confusion Matrices, Precision-Recall Curves, ROC-AUC, and mean Average Precision (mAP). Explainable AI (XAI): Features feature importance mappings via tree-based MDI (impurity), permutation importance, and SHAP (SHapley Additive exPlanations) values for model transparency. Computer Vision Integration: Includes evaluation structures for real-world deployment compatibility testing using YOLO object detection models.