Extraction and Characterization of Litopenaeus vannamei’s Shell as Potential Sources of Chitosan Biopolymers

Chitin is the second most abundant polysaccharide,produced mainly as an industrial waste stream during crustacean processing.Chitin can be derived into chitosan through the deacetylation process.Conversion of shrimp waste into chitosan via the deacetylation process could be considered a practical approach for shell waste remediation.In this study,chitosan’s physicochemical characteristics extracted from two types of Pacific white leg shrimp,L.vannamei’s shell(i.e.,rough and smooth),were compared with commercial chitosan.The yield,moisture,ash,solubility,water and fat binding capacity were measured.The degree of deacetylation(DDA)was calculated using FTIR,and their chemical Structure was confirmed using XRD and SEM-EDS.Both extracted chitosan showed no significant difference in yield,moisture,ash,solubility and water binding capacity but showed a significant difference with commercial chitosan.Moreover,the fat binding capacity of commercial chitosan showed the lowest percentage(408.34±0.83%)as compared to extracted chitosan(smooth shell 549.59±12.48%;rough shell 500.55±12.10%).The DDA indicated that extracted chitosan from the smooth and rough shell was considered good chitosan as compared to commercial chitosan with 84.08±1.27%,80.78±0.79%and 74.99±1.48%,respectively.Additionally,the presence of hydroxyl and amino groups from FTIR and a good crystallinity index was recorded using XRD of extracted chitosan.Based on observed characteristics,shrimp shell waste from L.vannamei can achieve chitosan standard quality as a biopolymer and highly potential to be applied in various industrial applications.

Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.

Spatial Model of Sky Brightness Magnitude in Langkawi Island, Malaysia

Sky brightness is an essential topic in the field of astronomy, especially for optical astronom- ical observations that need very clear and dark sky conditions. This study presents the spatial model of sky brightness magnitude in Langkawi Island, Malaysia. Two types of Sky Quality Meter （SQM） manu- factured by Unihedron are used to measure the sky brightness on a moonless night （or when the Moon is below the horizon）, when the sky is cloudless and the locations are at least 100 m from the nearest light source. The selected locations are marked by their GPS coordinates. The sky brightness data obtained in this study were interpolated and analyzed using a Geographic Information System （GIS）, thus producing a spatial model of sky brightness that clearly shows the dark and bright sky areas in Langkawi Island. Surprisingly, our results show the existence of a few dark sites nearby areas of high human activity. The sky brightness of 21.45 mag arcsec-2 in the Johnson-Cousins V-band, as the average of sky brightness equivalent to 2.8 × 10-4cd m-2 over the entire island, is an indication that the island is, overall, still relatively dark. However, the amount of development taking place might reduce the number in the near future as the island is famous as a holiday destination.

Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study in Muda River Basin

This study constructs downscaling statistical model in analyzing the hydrological modeling in the study area which faces the risk of flood occurrence as the impact of climate change. The combination of chemometric method and time series analysis in this study show that even during the monsoon season, rainfall and stream flow are not the major contribution towards the changing of water level in the study area. Based on Correlation Test, it shows that suspended solid and water level show high correlation with p-value < 0.05. Factor Analysis being carried out to determine the major contribution to the changes of Water Level and the result show that Suspended Solid shows a strong factor pattern with value 0.829. Based on Control Chat Builder for time series analysis, the Upper Control Limit for water level and suspended solid are 7.529 m and 1947.049 tons/day and the Lower Control Limit are 6.678 m and 178.135 tons/day. This shows that human development in the area gives high impact towards climate change and risk of flood in the study area which commonly faces flood during monsoon season.