Multi-Angle Scattering and Image-Derived Feature

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.