Datasets of Chemical Compounds in Three Different Species of Aquilaria using GC-MS coupled with GC-FID Analysis

Published: 9 February 2024| Version 1 | DOI: 10.17632/9kpt7k5dd3.1
zakiah yusoff,


The datasets presented in this article consist of detailed chemical analyses of three different species of Aquilaria using GC-MS and GC-FID techniques. The datasets consist of two samples from each of the three species of Aquilaria, specifically A. crassna (designated as ACS1 and ACS2), A. malaccensis (designated as AMS1 and AMS2), and A. subintegra (designated as ASS1 and ASS2). The datasets include information on the retention times, Tr (min), peak area (%), identified compounds, molecular formulas, molecular weights, identification modes, and similar chemical compounds found in every species of Aquilaria. in each sample, were meticulously tabulated in MS Excel. Concurrently, the chemical analyses collectively unveil that agarwood oil is predominantly composed of a mixture of compounds. These compounds can be categorized into four distinct groups: carboxylic acid, other compounds, sesquiterpene, and sesquiterpenoid. This comprehensive dataset facilitates a thorough understanding of the chemical profiles and analytical nuances across the different Aquilaria species, enhancing the robustness and interpretability of the datasets.


Steps to reproduce

The extraction process was conducted by the BioAromatic Research Centre of Excellence (BARCE) at Universiti Malaysia Pahang (UMP). Various extraction parameters were applied to optimize the conditions for obtaining the agarwood oil extract. Preceding the extraction process, the ground agarwood chip underwent sequential water soaking for several days to facilitate the breakdown of parenchymatous and oil glands. Subsequently, the oil extraction was performed over a period of 3 to 5 days utilizing the hydro distillation process. For GC-MS and GC-FID analyses, the samples were diluted in dichloromethane (DCM) of analytical grade. Identification in GC-MS involved a comparison of the mass spectrum generated from sample analysis with the National Institute of Standards and Technology (NIST) library, requiring a minimum similarity of ≥80%. GC-FID identification relied on linear retention indices, determined relative to the retention times on a DBI column of a homologous series of C. The GC-MS system, an Agilent 7890B GC System coupled with an Agilent 5977A MSD, featured an inlet temperature set at 250 °C, employing an Agilent DB-1ms column (30 m × 250 µm × 0.25 µm) with a helium flow rate of 1.0 mL/min. The oven program initiated at 80 °C, with a 3 °C/min increase until reaching 250 °C, held for 3 minutes. Additionally, the auxiliary heater was set at 260°C, the MS source at 230°C, and the MS quad at 150°C, utilizing Electron Impact (EI) mode with an energy of 70 electron volts (eV). In contrast, the GC-FID system used an Agilent 7890B GC System with an inlet temperature of 250 °C, the same Agilent DB-1ms column, and a helium flow rate of 1.0 mL/min. The oven program mirrored that of the GC-MS system, starting at 80 °C, increasing by 3 °C/min until 250 °C, and maintaining this temperature for 3 minutes. Unlike the GC-MS system, the GC-FID system did not employ an auxiliary heater; instead, the FID detector operated at a temperature of 250 °C. For chemical component identification, mass spectral libraries (HPCH2205.L, Wiley7Nist05.L, and NIST05a.L) were consulted, and the findings were expressed in terms of peak areas using peak counts.


Universiti Teknologi MARA


Meta Dataset