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Herath HMWAI and Jinendra BMS*
Department of Agricultural Engineering and Environmental Technology, Faculty of Agriculture, University of Ruhuna, Sri Lanka.
Abstract
Agarwood is a highly valued fragrant resin produced inside a few tree species belonging to the family Thymalaeaceae as a self-defense response to plant stress. The amount of resin developed inside the tree cannot be estimated by outside inspection. Consequently, harvesting trees before they reach their potential yield is a severe drawback to the Agarwood industry. Therefore, developing effective techniques for detecting Agarwood resin status inside the tree species has become a critically important task for the agarwood industry to increase productivity. The present study evaluates the factors affecting Near-Infrared Spectroscopy (NIRS) models when predicting agarwood formation inside
A. crassna trunks using NIR spectroscopy. The research used 110 wood specimens obtained from well-grown Agarwood trees in a commercial plantation in Nawimana GS Division, Matara District, Sri Lanka. NIR meter FQA-NIR Gun (588-1100nm) with a custom-made probe was used to acquire NIR reflectance spectra without outside light interference. SIMCA models were built to identify the agar resin-developed wood log areas from the normal wood areas in the tree trunk. SIMCA prediction models were built to investigate three influencing factors, namely present or absent outside tree bark, surface roughness and wood thickness agarwood prediction. Better prediction results were obtained from the bark-removed samples (at the accuracy rates of 97%) to the bark present (85%), smooth wood surfaces (98%) to the rough surface (90%) and 2mm thickness (98%) to the other thickness. The most effective wavelength for the separation of Agarwood present and absent samples was located at 978 nm of NIR. The study has demonstrated the potential possibility of using NIR spectroscopy to identify the agarwood formation in
A. crassna in non-destructive and rapid mode.
Keywords: Agarwood, A. crassna, Rapid detection, NIR spectroscopy, SIMCA
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