Statistical analysis for chemical compound based on several species of aquilaria essential oil

Abstrak

The paper examines the characterization of Aquilaria essential oils from different species, namely Aquilaria malaccensis, Aquilaria beccariana, Aquilaria crassna, and Aquilaria subintegra, renowned for agarwood production in Malaysia. Gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID) were employed for extracting essential oil data, facilitating compound identification. Subsequently, a preliminary analysis focused on classifying significant chemical compounds in the samples. The study then utilized boxplot pre-processing for visualizing and interpreting data distribution. The statistical analyses were performed using MATLAB software version R2021b, considering two key parameters which are the peak area (%) of significant chemical compounds and the classification of Aquilaria species (A. beccariana, A. malaccensis, A. crassna, and A. subintegra) based on their chemical composition. The results, presented through boxplot analyses, demonstrated a clear representation of the parameters and their distribution in the data. This method not only confirmed the potential of boxplot analysis in statistical evaluation of significant compounds in Aquilaria essential oil but also suggested its applicability for further classification work.