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All Journal International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Siti Mariatul Hazwa Mohd Huzir
Universiti Teknologi MARA
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Stepwise regression of agarwood oil significant chemical compounds into four quality differentiation Siti Mariatul Hazwa Mohd Huzir; Aqib Fawwaz Mohd Amidon; Anis Hazirah ‘Izzati Hasnu Al-Hadi; Nurlaila Ismail; Zakiah Mohd Yusoff; Saiful Nizam Tajuddin; Mohd Nasir Taib
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp735-741

Abstract

This paper gives precise summary on the application of stepwise regression model based upon the pre-process analysis of boxplot for four chemical compounds into four different qualities of agarwood oil. In the global market, agarwood oil is acknowledged as a pricey and valuable nature product owing to its benefits. Unfortunately, there is no standard grading method for agarwood oil grade classification. Intelligent model in grading the quality of agarwood oil is crucial as one of the efforts to classify the agarwood quality. The main model chosen in this study is stepwise regression by concerned specific parameter which is the value of correlation coefficient, R2. To achieve this goal, four out of eleven significant compounds of agarwood oil that consist of 660 data samples from low, medium low, medium high and high quality are representing the input. The independent variables are X1, X2, X3 and X4 which refer to the ɤ-Eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin compounds, respectively. MATLAB software version r2015a has been chosen as the simulation platform for this research work. The result showed that the stepwise regression model has a correlation coefficient of 0.756 and p-value less than 0.05 significance level which successfully passed the performance criteria toward regression value.
A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading Siti Mariatul Hazwa Mohd Huzir; Noratikah Zawani Mahabob; Aqib Fawwaz Mohd Amidon; Nurlaila Ismail; Zakiah Mohd Yusoff; Mohd Nasir Taib
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1358-1365

Abstract

Essential oils extracted from trees has various usages like perfumes, incense, aromatherapy and traditional medicine which increase their popularity in global market. In Malaysia, the recognition system for identifying the essential oil quality still does not reach its standard since mostly graded by using human sensory evaluation. However, previous researchers discovered new modern techniques to present the quality of essential oils by analyse the chemical compounds. Agarwood essential oil had been chosen for the proposed integrated intelligent models with the implementation of k-nearest neighbor (k-NN) due to the high demand and an expensive natural raw world resource. k-NN with Euclidean distance metrics had better performance in terms of its confusion matrix, sensitivity, precision accuracy and specificity. This paper presents an overview of essential oils as well as their previous analysis technique. The review on k-NN is done to prove the technique is compatible for future research studies based on its performance.
Statistical analysis of agarwood oil chemical compound exists in four species of Aquilaria Amir Hussairi Zaidi; Anis Hazirah ‘Izzati Hasnu Al-Hadi; Siti Mariatul Hazwa Mohd Huzir; Zakiah Mohd Yusoff; Nurlaila Ismail; Ali Abd Almisreb; Saiful Nizam Tajuddin; Haji Mohd Nasir Taib
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp727-732

Abstract

Aquilaria, renowned for its agarwood, and valued for its aromatic wood and rich resin, finds use in cosmetics, fragrances, incense, and medicine. Identifying the agarwood-producing species among 21 species of Aquilaria is challenging. This study analyzes chemical compounds in agarwood oil from 4 Aquilaria species: Aquilaria beccariana, Aquilaria crassna, Aquilaria malaccensis, and Aquilaria subintegra using gas chromatography-flame ionization detector (GC-FID). Statistical analysis explores compound abundance, employing methods like mean and Z-score tests. This analysis summarizes those 14 compounds that are consistently present based on zero and non-zero observations, and the Z-score test highlights five chemical compounds, with three compounds appearing in both analyses. These compounds can serve as a reference for future studies on Aquilaria species and agarwood oil, enhancing classification efforts.
Co-Authors Ali Abd Almisreb Amir Hussairi Zaidi Anis Hazirah ‘Izzati Hasnu Al-Hadi Aqib Fawwaz Mohd Amidon Haji Mohd Nasir Taib Mohd Nasir Taib Noratikah Zawani Mahabob Nurlaila Ismail Saiful Nizam Tajuddin Saiful Nizam Tajuddin Zakiah Mohd Yusoff