Ganoderma boninense (G. boninense) has been identified as a major problem in oil palm industry which caused basal stem rot disease. Identification of metabolite variation of healthy and G. boninense-infected oil palm leaves at 14 days postinfection using NMR metabolomics approach followed by characterization of an electrochemical sensor based on a functionalized multiwalled carbon nanotube (MWCNT) layer-by-layer framework on modified screen-printed carbon electrode has been successfully determined. Significant differences from the 1 H NMR data were observed between healthy and G. boninense-infected oil palm leaves, according to principal component analysis. Gold nanoparticle-functionalized MWCNT and chitosan-functionalized MWCNT were deposited on a screen-printed carbon electrode and were applied for the electrochemical detection of healthy and G. boninense-infected oil palm leaves. The electrocatalytic activities of a modified electrode towards oxidation of healthy and G. boninense-infected oil palm leaves at a concentration of 100 mg/L were evaluated using cyclic voltammetry and linear sweep voltammetry. The limits of detection of healthy and G. boninense-infected oil palm leaves were calculated to 0.0765 mg/L and 0.0414 mg/L, respectively. The modified electrode shows a good sensitivity and reproducibility due to the unique characteristics of gold nanoparticles, chitosan, MWCNTs, and synergistic interaction between them.

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Basal stem rot is the major disease in oil palm industry that caused by a fungal named Ganoderma boninense (G. boninense) species. Infected palms are symptomless at the early stage of this disease which imposes difficulties in detecting the disease. Therefore, this study was carried out to obtain the 1H NMR metabolomic profiling of both non-infected and G. boninense infected oil palm leaf at 30 days post-infection (dpi). This combination has provided a rapid approach in investigating the changes in the compound variations of non-infected and G. boninense infected oil palm leaf. Non-infected and G. boninense infected oil palm leaf at 30 dpi was extracted using aqueous methanol (methanol: water, 80: 20 v/v). The crude extracts obtained were analyzed by 1H NMR-based metabolomics approach. Analysis of metabolomics data from 1H NMR was conducted by multivariate data analysis of principal component analysis (PCA). Significant differences were found between the two groups. Compared to the non-infected leaf, the G. boninense infected leaf had higher relative abundance of choline, asparagine, alanine, succinic acid, gallic acid, epicatechin, trimethylamine, N-acetylglucosamine, N-acetyltyrosine, β-sitosterol, 2,3-butanediol, lactic acid, caffeic acid, p-hydroxybenzoic acid, α-tocopherol, β-cryptoxanthin and kaempferol. The non-infected leaf showed higher level of sucrose, xylose, α-glucose, S-sulfocysteine and indole-3-acetic acid. NMR-based metabolomics applied in this study reveals that G. boninense alters a manifold of primary and secondary compounds in oil palm leaf.

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A visual driver support system was developed to reduce accidents involving motorcyclist. The system used Matlab software as a platform to detect motorcycle image. The detection system was designed to detect still images and moving objects images for different resolutions. A motorcycle was defined as the target object in this case. The results showed that the visual driver support system is able to detect image of motorcycle in still and in moving condition. The percentage of correct detection of motorcycle image is 83.3% and 50% for low and high image resolutions respectively.