A Chemometric Approach to Timber Species Classification Using Near-Infrared Spectroscopy (NIRS) and SIMCA
Aasheesh Raturi *
Department of Physics, Dolphin PG Institute of Biomedical and Natural sciences, Dehradun - 248007, India.
Shailendra Kumar
Forest Products Division, Forest Research Institute, Dehradun - 248006, India.
Bhanu Prakash R
Science4u Analytics and Research Solutions Pvt. Ltd. Bangalore, India.
*Author to whom correspondence should be addressed.
Abstract
This study investigates the potential of using Soft Independent Modeling of Class Analogy (SIMCA) in conjunction with Fourier transform Near-Infrared Spectroscopy (NIRS) as a non-destructive and effective alternative method for classifying four commercially significant Indian timber species: Cedrus deodara (CD), Dalbergia sissoo (DS), Shorea robusta (SR), and Tectona grandis (TG). NIR absorbance spectra were collected using an FT-NIR spectrophotometer and SIMCA models developed through the Unscrambler 10.1 software. Class-specific Principal Component Analysis (PCA) models were employed to construct the classification algorithm. The developed training models achieved perfect classification performance, recording 100% accuracy for TG, CD, DS, and 97.5% for SR. Validation using independent wood samples yielded 100% prediction accuracy for all four species. Additionally, Eucalyptus tereticornis (ET), which was not part of the model calibration, was accurately recognised as distinct, confirming the model’s specificity. The NIRS-SIMCA combination thus provides a dependable and efficient solution for timber species determination, enhancing quality assurance and regulatory adherence in the wood industry.
Keywords: Near-infrared spectroscopy (NIRS), SIMCA classification, timber species non-destructive testing, model’s specificity