Non-Destructive Coal Type Classification Using FT-NIR Spectroscopy and Machine Learning

Soham Mahendra Dhanokar; Dhruv Ghanshyam Bangani; Umamah Hozefa Darwajawala; Swara Tushar Hande; Kirti Ramchandra Karande

doi:10.36948/ijfmr.2026.v08i01.69699

Non-Destructive Coal Type Classification Using FT-NIR Spectroscopy and Machine Learning

Author(s)	Mr. Soham Mahendra Dhanokar, Mr. Dhruv Ghanshyam Bangani, Ms. Umamah Hozefa Darwajawala, Ms. Swara Tushar Hande, Ms. Kirti Ramchandra Karande
Country	India
Abstract	Reliable and rapid coal quality assessment is critical for power generation, steel, cement and other coal-dependent industries, yet traditional laboratory-based methods such as proximate/ultimate analysis and calorific value determination are slow, destructive, and sample-intensive. This paper presents a non-destructive coal classification system that uses Fourier Transform Near-Infrared (FT-NIR) spectroscopy combined with machine learning to distinguish among anthracite, bituminous and lignite coals and provide quality-related information directly from spectral data. Diffuse reflectance FT-NIR spectra are acquired in the 900–2500 nm range, preprocessed using first-derivative and scaling operations, and compressed via principal component analysis (PCA). A supervised classifier (support vector machine and random forest variants) is then trained on the reduced feature space using stratified cross-validation. The resulting model is encapsulated in a deployable pipeline that accepts spectral files in CSV format and returns coal type predictions to a Flutter-based front-end through a Python backend API. The proposed approach demonstrates how modern spectroscopic techniques and machine learning can be integrated into a practical, end-to-end workflow for intelligent, non-destructive coal classification and forms a basis for future real-time coal quality monitoring solutions.
Keywords	Coal classification, FT-NIR spectroscopy, machine learning, support vector machine, random forest, non-destructive testing
Field	Computer
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-24
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.69699

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2 Cover Page Vol 8 Isu 1 Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Non-Destructive Coal Type Classification Using FT-NIR Spectroscopy and Machine Learning

Share this