Antioxidative properties and proximate analysis of spent coffee ground (SCG) extracted using ultrasonic-methanol assisted technique as a potential functional food ingredient

Spent coffee ground (SCG) produced in tons by restaurants and cafeterias and domestic consumers is a potentially good source of natural antioxidants because it contains substantial amounts of bioactive compounds. The purpose of this study was to identify the physicochemical and antioxidant properties of three different types of spent coffee ground (SCG), namely Robusta, Arabica and Liberica extracted using ultra-sonicmethanol assisted technique. DPPH, FTC, TBA, total phenolic content (TPC) and total flavonoid content (TFC) were used to measure the antioxidant properties. Robusta SCG exhibited the highest DPPH inhibition 41.63±0.04%), FTC (60.42±0.03%) and TBA analysis (73.09±0.08%). The total phenolic compounds in the samples varied widely ranging from 18.94±0.06 to 26.23±0.86 mg GAE/g sample, with Robusta SCG showing the highest value among the three, while Arabica SCG depicted the highest amount total flavonoid content (47.62±0.05 to 56.20±0.08). A strong correlation between antioxidant activity and total phenolic content were observed in this study. Compared to Arabica and Liberica SCGs expended, Robusta SCG demonstrated a stronger beneficial effect against lipid peroxidation. This study reveals that SCGs can be regarded as a new useful source of natural antioxidant with a view to increasing the use of antioxidant synthetics by using the ingredient of agro-industrial residues in food production especially ingredients for functional food

Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data

Several methods exist for remote sensing image classification. They include supervised and unsupervised approaches. Accuracy assessment of a remote sensing output is a most important step in classification of remotely sensed data. Without accuracy assessment the quality of map or output produced would be of lesser value to the end user. However, supervised and unsupervised techniques show different levels of accuracy after accuracy assessment was conducted. This paper describes a study that was carried out to perform supervised and unsupervised techniques on remote sensing data for land cover classification and to evaluate the accuracy result of both classification techniques. The study used SPOT 5 satellite image taken on January 2007 for 270 / 343 (path / row) as a primary data and topographical map and land cover maps as supporting data. The land cover classes for the study area were classified into 5 themes namely vegetation, urban area, water body, grassland and barren land. Ground verification was carried out to verify and assess the accuracy of classification. A total of 72 sample points were collected using Systematic Random Sampling. The sample point represented 25% of the total study area. The results showed that the overall accuracy for the supervised classification was 90.28% where Kappa statistics was 0.86, while the unsupervised classification result was 80.56% accurate with 0.73 Kappa statistics. In conclusion, this study found that the supervised classification technique appears more accurate than the unsupervised classification

Remote Sensing For Mapping Ramsar Heritage Site At Sungai Pulai Mangrove Forest Reserve, Johore, Malaysia

The Sungai Pulai Mangrove Forest Reserve (SPMFR) is the largest riverine mangrove system in Johore State. In 2003 about 9,126 ha of the Sungai Pulai mangrove was designated as a RAMSAR site. RAMSAR sites are wetland areas that are deemed to have international importance and are included in the List of Wetlands of International Importance. The SPMFR playa significant role for socio-economic to the adjacent 38 villages. The villagers depends on the estuary as its mudflats, an ideal feeding, spawning and fattening ground, support a significant proportion of fish species. Other mangrove uses include wood cutting, charcoal production, aquaculture activities and eco-tourism. The current development of Iskandar Development Region (IDR) and construction of a new port at the river estuary may represent a direct impact on the mangrove ecosystem, causing shorelines erosion and water pollution from associated reclamation and dredging works. However the conservation management of the site is managed in line with Integrated Management'Plan for the sustainable use of mangroves in Iohor state. Satellite remote sensing'is a useful source of information as it provides timely and complete coverage for vegetation mapping especially in mangroves where the access is difficult. This study was carried out to identify and map land cover types using SPOT 4 image in the Sungai Pulai-RAMSAR site and its surrounding areas. Unsupervised classification technique has produce eight classes of land cover type over the study site. Ground verification was carried out to verify and assess the accuracy of classification. The results showed that approximately 90% of the area was classified correctly. Vegetation density was classified into five levels namely very high, high, medium, low and very low based on crown density scale. The study concludes that SPOT 4 was capable to discriminate mangrove area clearly from other land covers type, meanwhile vegetation indices model can be used as a tool for mapping vegetation density level in the SPMFR and its surrounding area. Results clearly showed usefulness of remote sensing for monitoring, management and development of mangrove forest for sustainable management and preserve the SPMFR as a RAMSAR site in Peninsular Malaysia

Development of a protocol for Malaysian Important Plant Areas criterion weights using Multi-criteria Decision Making - Analytical Hierarchy Process (MCDM-AHP)

Globally, in the selection of potential Important Plant Areas (IPAs), the preferred method used is scoring method because of its simplicity and ease of evaluation. However, the criterion weights developed from this method are vague and suffer from judgement uncertainties. In this study, we developed and propose a methodology for the assessment of Malaysian IPA criterion weights using Multi-Criteria Decision Making (MCDM) - Analytical Hierarchy Process (AHP). The criterion weights were assessed from data obtained through questionnaire surveys of Malaysian biodiversity experts. As a result, threatened habitats are given the highest score (0.337), followed by threatened species (0.327), endemism (0.245), and botanical richness (0.091). This study, therefore, proposes the MCDM-AHP as a decision-making tool for prioritising and formulating criterion weights for Malaysian IPA