A novel application of artificial neural network for classifying agarwood essential oil quality

• Published in: International Journal of Electrical and Computer Engineering
• Main Author: Mahabob N.Z.; Yusoff Z.M.; Amidon A.F.M.; Ismail N.; Taib M.N.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139019056&doi=10.11591%2fijece.v12i6.pp6645-6652&partnerID=40&md5=3a1c66238e76adcfb0aab35fd8f4ed13

This work studies the agarwood oil classification into high and low quality by using two different techniques. Initially, the Forest Research Institute Malaysia (FRIM) and Universiti Malaysia Pahang (UMP) are where the sample preparation and compound extraction of agarwood oil is collected. The data collections were done from the previous researcher consists of 96 samples from seven significant agarwood oil compounds. The artificial neural network (ANN) and the proposed stepwise regression technique were used in this study. The stepwise regression was done the feature selection and successfully reduced agarwood oil compounds from seven to four. Then, the ANN technique was used to classify agarwood oil into high and low using input from seven and four compounds separately. The performance of ANN with different inputs is compared (ANN with seven inputs compared with ANN with four inputs). All the experimental work was performed using the MATLAB R2017b using the "patternet" implemented Levenberg Marquardt algorithm and ten hidden neurons. It was found that the ANN technique using seven compounds obtained the best performance according to high accuracy and lower mean square error (MSE) value. Finally, 1 hidden neuron in ANN with seven inputs selected as the best neuron for grading the agarwood oil compounds. © 2022 Institute of Advanced Engineering and Science. All rights reserved.