Saiful Nizam Tajuddin

Fatty Acid Composition and Antibacterial Activity of Neem (Azadirachta indica) Seed Oil

Pneumonia and skin diseases are some types of severe infections occur worldwide due to pathogenic bacteria such as Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). These bacterias do contribute towards other infections in human and thus lead towards unhealthy life. The aim of this study was to determine the fatty acid composition and to investigate the toxic effect of seed oil of Neem, genus Azadirachta of Azadirachta indica (Family: Meliaceae) against two multi-drug resistant bacteria, namely; E. coli and S. aureus. The oil was extracted for 6 h through soxhlet method using hexane as solvent. The fatty acid composition was analyzed via Gas Chromatography-Mass Spectrometry (GC-MS). The composition resulted in detection of 8 fatty acids, whereby the dominant compound is linoleic acid of 34.69% and followed by oleic-, stearic-, palmitic-, arachidic-, behenic-, lignoceric-, and palmiticoleic acid at the percentage of 20.46, 20.42, 18.66, 3.59, 0.80, 0.55, and 0.17%, respectively. The bioassays were conducted through in vitro activity via disc diffusion method with five (5, 20, 50, 80, and 100 mg/mL) different concentrations. Streptomycin and 1% Dimethyl sulfoxide (DMSO) were used as positive and negative controls respectively. The bioassay provided inhibition zone that lies between 8.7 to 11.7 mm and 8.7 to 13.0 mm for E. coli and S. aureus respectively at the concentration of 5 to 100 mg/mL. The antibacterial activity of Neem (A indica) seed oil against these bacteria depends on the correlation of its concentration and thus isolation of bioactive components should be conducted.

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.

Neuroprotective effect from stem bark extracts of Knema laurina against H2O2- and Aβ1–42-induced cell death in human SH-SY5Y cells

The stem bark extracts of Knema laurina inhibited the hydrogen peroxide (H2O2)- and aggregated amyloid β-peptide 1–42 length (Aβ1–42)-induced cell death in differentiated SH-SY5Y cells. Exposure of 250 μM H2O2 or 20 μM Aβ1–42 to the cells for 24 h reduced 50% of cell viability. Pretreatment of cells with ethyl acetate extract (EAE) or n-butanol extract (BE) at 300 μg/mL and then exposure to H2O2 protected the cells against the neurotoxic effects of H2O2. Besides, methanolic extract (ME) at 1 and 10 μg/mL exerted neuroprotective effect on Aβ1–42-induced toxicity to the cells. These results showed that EAE, BE and ME exhibited neuroprotective activities against H2O2- and Aβ1–42-induced cell death. Flavonoids (3–6) and β-sitosterol glucoside (8) were isolated from the EAE. Compound 1 was isolated from hexane extract, and compounds 2 and 7 were isolated from dichloromethane extract. All these observations provide the possible evidence for contribution in the neuroprotective effects.

The in vivo anti-tumor effect of curcumin derivative (2E,6E)-2,6-bis(4-hydroxy-3-methoxybenzylidene)cyclohexanone (BHMC) on 4T1 breast cancer cells

Curcumin is one of the promising natural products extracted from the rhizomes of curcuma longa and has been extensively investigated by researchers to explore its potential as a chemopreventive and therapeutic agent against several chronic diseases. To further enhance the cytotoxic potential of curcumin, its derivative (2E,6E)-2,6-bis(4-hydroxy-3-methoxybenzylidene)cyclohexanone (BHMC) has been synthesized and investigated, and its antitumor effect on tested on 4T1 challenged mice. BHMC was recorded with in vitro cytotoxicity on murine 4T1 breast cancer cells with IC50 value 13.66 μM, which was 2 times lower than curcumin after 72 hours of treatment. An in vivo study indicated that BHMC possessed antitumor effect on the 4T1 cells of the challenged mice by induction of apoptosis, antiproliferation, anti-inflammation and antimetastasis. This effect is better compared to curcumin treatment at the same evaluated concentration. Thus, BHMC is a potential antitumor agent against breast cancer.

Identification of significant compounds of agarwood incense smoke different qualities using Z-Score

This paper presents the proposed Z-Score in identifying the significant chemical compounds of agarwood incense smoke. The technique is applied to commercial, low and high quality of agarwood samples and the extraction of the compounds were performed by gas chromatography - flame ion detector (GC-FID) and gas chromatography - mass spectrometry (GC-MS) coupled with solid phase microextraction (SPME) analysis. The Z-Score has successfully identified eight significant compounds in those qualities of agarwood smoke. They are β-maaliene, nor-ketoagarofuran, epoxybulnesene, 10-epi-γ-eudesmol, agarospirol, α-eudesmol, epi-α-bisabolol and rotundone. Among all, epoxybulnesene gave the highest abundant of 14.81%. The finding from this study showed that the chemical compounds for agarwood smoke is varied depending on their qualities. The identified compounds are useful for further study in agarwood smoke quality grading analysis.

A review on agarwood and its quality determination

This paper presents a review on agarwood and its quality determination. It was found that Aquilaria Malaccensis is the main species in Malaysia and Indonesia. The agarwood has different quality or grades; similarly its essential oil. They are traded at different price according to their quality. Scientific reason showed that chemical compositions of agarwood make them different from each other, thus affect their quality. Physical appearances such as odour and colour have been used to grade their qualities. However the method has drawbacks in terms of subjectivity, poor reproducibility, time consumption and large labour expense. Therefore there is a requirement for agarwood and its oil to be graded according to their chemical profiles. This interest began to attract the researchers and also is mainly to ensure the quality of agarwood oil.

Investigation of common compounds in high grade and low grade Aquilaria Malaccensis using correlation analysis

This paper presents a correlation analysis between high grade and low grade of Aquilaria Malaccensis (AM). Seven selected samples of AM were identified using Gas Chromatograph - Mass Spectra (GC-MS) and several statistical techniques have been employed. The samples were separated into two grades such as high grade and low grade. The analysis involves of normality test such as Shapiro Wilk, standard deviation, skewness, kurtosis and correlation analysis. All the analysis was done using SPSS ver. 15.0. The result shows that there are no significant correlation between high grade and low grade and it is a high correlation among compounds in similar grade. Three common compounds and highly correlated found in both grade and they are hexadecanoic acid, valencene and α-guaiene.

Analysis on agarwood vapour using headspace volatile DVB-CAR-PDMS SPME with different sampling time

Due to its popularity and high market demand, critical analysis on agarwood vapour chemical compounds may provide an alternative quality discrimination of agarwood oil. The proposed work involves the extraction of high quality agarwood using headspace volatile divinylbenzene-carboxen-polydimethysiloxane (DVB-CAR-PDMS) solid phase microextraction (SPME) with different sampling time at 15, 30 and 60 minutes. Then, Gas chromatography - Mass Spectroscopy (GC-MS) is performed to identify the chemical compounds. Generally, agarwood vapour is rich in terpene group especially monoterpene, sesquiterpene and oxygenated sesquiterpene. Analysis showed that at least 52, 50 and 54 compounds are extracted at 15, 30 and 60 minutes, respectively. Among all, duration of 60 minutes produced the highest abundance (%) for caryophellene oxide. The finding proves that caryophellene oxide as one of the important compounds in high agarwood and different sampling time plays a major role that effects the extraction. Thus, the analysis in this study is significant and brings benefit especially to the agarwood and its essential oil research area.

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.