Mailina Jamil

Analysis of chemical compounds of agarwood oil based on headspace-solid phase microextraction combined with gas chromatography mass-spectrometry

The quality of the agarwood oils plays an important role as it reflects the price of the oil in the market. High quality agarwood oil is traded extensively. In order to obtain good quality agarwood oil, temperature condition during the extraction is one of the important parameter instead of type and time of extraction. An analysis of chemical compounds of agarwood oil involves of three different temperature conditions such as 40°C, 60°C and 80°C has been carried out in this study. The analytical work involves HS-SPME combined with GC-MS analysis to extract and identify the chemical compounds composition in agarwood oil. The zscore technique was introduced to identify the significant compounds that contribute to the aroma of agarwood oil. The result shows that five compounds were identified and they were aromadendrane, β-agarofuran, α-agarofuran, 10-epi-□-eudesmol and □-Eudesmol. These compounds were found to have similar pattern of plot but different in chemical composition due to the different chemical properties of the oils.

Chemical profiling and biological properties of Neolitsea kedahense Gamble essential oils

Abstract Hydrodistillation of the fresh stem and leaf of Neolitsea kedahense Gamble, collected from Gunung Jerai, Malaysia followed by the GC-FID and GC-MS analysis revealed the detection of a total of 47 constituents of which 28 (86.4%) from the stem and 31 (96.4%) constituents from the leaf. δ-Cadinene (17.4%), 1-epi-cubenol (11.8%), cyperotundone (9.0%), cis-cadin-4-en-7-ol (7.7%), τ-cadinol (7.1%) and α-cadinol (7.1%) were the principle constituents in the stem oil, whereas β-caryophyllene (18.9%), bicyclogermacrene (18.6%) and trans-muurola-4(14),5-diene (9.8%) were the major constituents in the leaf oil. Among the identified constituents, three constituents namely 7-epi-α-selinene, junenol and cis-cadin-4-en-7-ol have not been found previously from Neolitsea oils. The stem and leaf oils were screened for their α-glucosidase inhibitory and antibacterial activities. Both oils displayed potential α-glucosidase inhibitory activity, while the stem oil possessed weak antibacterial activity against Bacillus subtilis.

Classification of Selected Essential Oil from Family Zingiberaceae Using E-Nose and Discriminant Factorial Analysis (DFA) Techniques: An Initial Study

Essential oils are very valuable natural resources and considered as secondary metabolites. They are produced from several parts of aromatic plant by using different type of extraction techniques. Each technique produced slightly different output oil yield and smell however they produced the same major chemicals compound markers when they are analysed using chemical analysis and profiling technique. Pure essential oils are known to have very strong odor and there are several techniques used to differentiate the volatile odor generated. In this study, Electronic Nose (E-Nose) technology is used to distinguish the smell among 8 samples selected within the same Zingiberaceae family. Their pattern recognition profiles were examined by statistical analysis using Discriminant Factorial Analysis (DFA). The result shows that the E-Nose technology combined with DFA were successfully discriminating all 8 samples within the same family with significant p-values < 0.05 across all samples and 100% recognition value.

Application of ANN in agarwood oil grade classification

This paper presents the application of Artificial Neural Network (ANN) in agarwood oil grade classification. The work involved of the extraction of chemical compounds by GC-MS, identification the significant chemical compounds using Z-score, generating the synthetic data using a dedicated formulae and application of ANN classification. The ANN classification is performed and its performance is measured using accuracy, sensitivity and specificity. The result showed that the performance of ANN classification for original GC-MS data is increasing when the data is added with synthetic data. This study showed that the ANN application in this study required a large number of sample size for it to have high accuracy in classification.

The grading of agarwood oil quality using k-Nearest Neighbor (k-NN)

This paper presents the application of k-Nearest Neighbor (k-NN) in grading the quality agarwood oil. Six agarwood oil samples obtained at Forest Research Institute Malaysia (FRIM) were extracted and their chemical compounds were examined by GC-MS. The work is followed by the grading system using the proposed k-NN. The study shows that there are 10 significant chemical compounds of agarwood oils. They are β-agarofuran, α-agarofuran, 10-epi-□-eudesmol, □-eudesmol, longifolol, oxo-agarospirol, hexadecanol and eudesmol. These compounds are used as inputs to the k-NN algorithm for grading them. The performance of the k-NN is measured and the highest accuracy obtained by k-NN which is above 83.3% shows that k-NN is a reliable classifier in grading the agarwood oil quality.

Agarwood (Aquilaria malaccensis) Oils

Abstract Agarwood is a fragrant wood usually derived from the diseased timber of Aquilaria trees. It is highly expensive and widely valued for its unique scent and medicinal properties. Agarwood is often graded according to its scent and resin formation by traders; low-quality wood is often distilled to produce oils. Agarwood and its essential oils have been described as stimulants, carminatives, and sedatives. They have been used as perfumery ingredients and also have been introduced into health food products as flavor and aroma enhancers.

Chemical compositions and antimicrobial activity of twig essential oils from three Xylopia (Annonaceae) species

The chemical composition of twig essential oils of Xylopia malayana , Xylopia elliptica and Xylopia fusca were analyzed using GC and GCMS. Both X. malayana (12.9%) and X. fusca (11.8%) twig oils contained spathulenol as the major constituent while X. elliptica twig oil was found to be rich in terpinen-4-ol (11.9%). The antimicrobial activities of the essential oils were evaluated against six gram positive bacteria, two gram negative bacteria and yeast by using the broth microdilution method. The twig oil of X. elliptica at a concentration ranging from 156 to 625 μg/ml was found to be active against all bacterial and yeast strains tested. Both X. malayana and X. fusca twig oils demonstrated weak activity toward all microorganisms tested with the MIC value of 5000 μg/ml. Gram negative bacteria’s, Pseudomonas aeruginosa and Escherichia coli seemed to be resistant to the essential oils tested. α-Pinene showed moderate to strong inhibitory effect against all the microorganism strains tested with the MIC value ranging from 325 to 2500 μg/ml, as compared to the other standard compounds which showed weak inhibitory even at the highest concentrations used. This finding demonstrated that the twig oil of X.elliptica possesses antimicrobial activity which may be useful and potential ingredient in the production of health care products. Key words: Xylopia twigs, essential oils, GC & GCMS analysis, antimicrobial activity.

Classification of pure and mixture Agarwood oils by Electronic Nose and Discriminant Factorial Analysis (DFA)

This paper describes the performance of a commercial AlphaMOS 4000 Electronic Nose coupled with Discriminant Factorial Analysis (DFA) as statistical tool used in discriminating the differences between pure and mixture agarwood oils by their volatile properties. The proposed techniques in this paper for testing and evaluating the capability of E-Nose for classifying and testing two different groups pure and mixtures Agarwood oil is successfully conducted. E-Nose coupled with DFA as a statistical tool is capable to discriminate ten oils from two separate groups pure and mixtures by their different odor uniqueness with significant p-value <; 0.05 and both groups are recognized as different samples with 100% accuracy. Lastly, for selected unknown sample projection, the projection accuracy is 90%.

Chemical Constituents of the Rhizome Oils of Three Varieties of Scaphochlamys kunstleri (Bak.) Holtt.

Abstract The rhizome oils of three varieties of Scaphochlamys kunstleri (Bak.) Holtt. viz. S. kunstleri (Bak.) Holtt. var. kunstleri, S. kunstleri (Bak.) Holtt. var. rubra Ridl. and S. kunstleri (Bak.) Holtt. var. speciosa C. K. Lim var. nov. were investigated by capillary GC and GC/MS. The oils of S. kunstleri var. kunstleri and S. kunstleri rubra were rich in monoterpenoids and found to possess some compositional similarities but quantitative differences in the concentration of each component. However, the oils could be distinguished from each other by the levels of terpinolene, p-cymen-8-ol, δ-3-carene and p-cymene. In addition, the oil of S. kunstleri var. rubra contained significantly higher concentrations of terpinolene (67.1%) and δ-3-carene and relatively lower concentrations of p-cymen-8-ol and p-cymene than the other two oils. The oil of S. kunstleri var. speciosa comprised mainly of sesquiterpenoids, with (Z)-β-farnesene (7.5%) and γ-cadinene (7.4%) as the major representatives. Geraniol (9.1%) was the most dominant monoterpenoid present in the oil.