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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohd Nasir Taib
Universiti Teknologi MARA
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Data-driven modelling of Aquilaria essential oils via dual GC profiling and multicollinearity diagnostics Nur Athirah Syafiqah Noramli; Noor Aida Syakira Ahmad Sabri; Muhammad Ikhsan Roslan; Nurlaila Ismail; Zakiah Mohd Yusoff; Mohd Nasir Taib
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10870

Abstract

Aquilaria-derived essential oils are chemically diverse and hold significant value in pharmaceuticals, fragrances, and traditional medicine. However, the complexity of their chemical composition presents challenges in statistical modelling, particularly due to multicollinearity among biosynthetically related compounds. This study investigates the extent of multicollinearity in Aquilaria essential oil data using multiple linear regression (MLR) and variance inflation factor (VIF) analysis. A regression model was constructed using three compounds, ? -guaiene, 10-epi ? -eudesmol, and ? -eudesmol, across 360 samples, with VIF and collinearity diagnostics applied to assess model validity. The model explained 93% of the variance in species classification, which is substantially higher than values typically reported in earlier chemometric studies of Aquilaria oils. This demonstrates that even a limited number of carefully selected compounds, when supported by diagnostic safeguards, can achieve strong classification accuracy. These findings emphasize the importance of applying multicollinearity diagnostics to improve the interpretability and reliability of chemometric analyses. The study contributes a robust analytical framework for future research and practical applications in species authentication, essential oil quality control, and conservation of Aquilaria resources.
Boxplot analysis of 4 grade agarwood essential oil for various grades Anis Hazirah 'Izzati Hasnu Al-Hadi; Aqib Fawwaz Mohd Amidon; Siti Mariatul Hazwa Mohd Huzir; Nurlaila Ismail; Zakiah Mohd Yusoff; Saiful Nizam Tajuddin; Mohd Nasir Taib
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp238-244

Abstract

Agarwood essential oil is used in most perfumery ingredients, as an incense and in traditional medical preparations. Agarwood essential oil, called "Black Gold," is extremely valued to the global community due to its numerous benefits. As of now, there is still no standard technique of grading different grades of agarwood essential oil. The current grading technique is inefficient since the agarwood essential oil is graded by using human sensory panel. Different people might have different perspective on grading the agarwood essential oil hence, the technique is not practical to adapt it globally. Due to the current technology, numerous intelligent techniques for verifying the grades of agarwood essential oil have been proposed and implemented. The study has conducted a statistical analysis on 4 grade agarwood essential oil using boxplot. Boxplot analysis summarizes the abundances for each chemical compounds from four different grades of agarwood essential oil with a high grade as a reference. This study shows the analysis of boxplot investigated 10-epi-δ-eudesmol, α-agarofuran, β-agarofuran, δ-eudesmol and dihydrocollumellarin as most important chemical compounds in high grade of agarwood essential oil. The chemical compounds that have been identified in high grade of agarwood essential oil can be a reference for future research studies.
Pre-processing technique of Aquilaria species from Malaysia for four different qualities Siti Mariatul Hazwa Mohd Huzir; Anis Hazirah 'Izzati Hasnu Al-Hadi; Amir Hussairi Zaidi; Nurlaila Ismail; Zakiah Mohd Yusoff; Mohamad Hushni Haron; Ali Abd Almisreb; Mohd Nasir Taib
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5577

Abstract

The paper interprets data distribution by using boxplot pre-processing in classify the quality of Agarwood oil for eleven chemical substances into four different qualities. The varieties usage of Agarwood oil makes it considered as an expensive and valuable product on the essential oil market. Perfumes, fragrances, incense, aromatherapy, and traditional medicine are the most popular Agarwood oil applications. However, the classification of Agarwood oil grades does not yet have standard grading method. This because it has been graded manually into different qualities by using human sensory evaluation. Boxplot analysis involving eleven chemical subtances that will be focusing in this study by concerned the quality for low, medium low, medium high and high. ɤ-eudesmol, ar-curcumene, β-dihydro agarofuran, ϒ-cadinene, α-agarofuran, allo aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin compounds are the selected significant compounds that represent the input for boxplot. Agarwood oil consist 660 data samples from low, medium low, medium high, and high quality. The result in this study showed that the four selected significant compounds (ɤ-eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin) are important as a marker for Agarwood oil quality classification. The identification of chemical substances on high quality done as reference for future research studies.
Co-Authors Aisyah Hartini Jahidin Aisyah Hartini Jahidin Ali Abd Almisreb Amir Hussairi Zaidi Anis Hazirah 'Izzati Hasnu Al-Hadi Anis Hazirah ‘Izzati Hasnu Al-Hadi Aqib Fawwaz Mohd Amidon Dyg Suzana Awang Megat Syahirul Amin Megat Ali Mohamad Hushni Haron Muhammad Ikhsan Roslan Noor Aida Syakira Ahmad Sabri Noor Hidayah Ros Azamin Noor Syafina Mahamad Jainalabidin Nooritawati Md Tahir Noratikah Zawani Mahabob Nur Athirah Syafiqah Noramli Nurlaila Ismail Saiful Nizam Tajuddin Saiful Nizam Tajuddin Siti Mariatul Hazwa Mohd Huzir Zakiah Mohd Yusoff Zakiah Mohd Yusoff