{"ID":2868566,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19367","arxiv_id":"2509.19367","title":"Low-Cost Sensor Fusion Framework for Organic Substance Classification and Quality Control Using Classification Methods","abstract":"We present a sensor-fusion framework for rapid, non-destructive classification and quality control of organic substances, built on a standard Arduino Mega 2560 microcontroller platform equipped with three commercial environmental and gas sensors. All data used in this study were generated in-house: sensor outputs for ten distinct classes - including fresh and expired samples of apple juice, onion, garlic, and ginger, as well as cinnamon and cardamom - were systematically collected and labeled using this hardware setup, resulting in a unique, application-specific dataset. Correlation analysis was employed as part of the preprocessing pipeline for feature selection. After preprocessing and dimensionality reduction (PCA/LDA), multiple supervised learning models - including Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF), each with hyperparameter tuning, as well as an Artificial Neural Network (ANN) and an ensemble voting classifier - were trained and cross-validated on the collected dataset. The best-performing models, including tuned Random Forest, ensemble, and ANN, achieved test accuracies in the 93 to 94 percent range. These results demonstrate that low-cost, multisensory platforms based on the Arduino Mega 2560, combined with advanced machine learning and correlation-driven feature engineering, enable reliable identification and quality control of organic compounds.","short_abstract":"We present a sensor-fusion framework for rapid, non-destructive classification and quality control of organic substances, built on a standard Arduino Mega 2560 microcontroller platform equipped with three commercial environmental and gas sensors. All data used in this study were generated in-house: sensor outputs for t...","url_abs":"https://arxiv.org/abs/2509.19367","url_pdf":"https://arxiv.org/pdf/2509.19367v1","authors":"[\"Borhan Uddin Chowdhury\",\"Damian Valles\",\"Md Raf E Ul Shougat\"]","published":"2025-09-19T03:16:11Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\",\"stat.ML\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}