Chemical analysis on the honey of Heterotrigona itama and Tetrigona binghami from Sarawak, Malaysia

This study aims to compare the chemical composition of honey samples produced by Heterotrigona itama and Tetrigona binghami which originated from Sarawak, Malaysia. One hundred and six (106) honey samples were collected from local bee farms and analysed in terms of their chemical profiles. The chemical analysis conducted includes physicochemical composition such as moisture, total phenolic content, sugar, 5-hydroxymethylfurfural (5-HMF), pH and organic acids and proximate analysis which included ash, protein, carbohydrates and energy. Independent T-test was used as a statistical tool to investigate the significant difference between the composition of both honey samples. The results showed that honey samples of Heterotrigona itama and Tetrigona binghami possessed significant difference (p<0.05) in moisture, total phenolic content, fructose, glucose, pH, protein, gluconic acid, acetic acid, ash, carbohydrates and energy. The honey samples of Heterotrigona itama exhibited significantly higher fructose and glucose at the average of 22.00 ± 3.48 g/ 100 g and 23.45 ± 3.23 g/100 g, respectively. Besides, the honey samples also possessed higher pH value, gluconic acid, ash, carbohydrates and energy. Meanwhile, Tetrigona binghami honey samples possessed significantly (p< 0.05) higher moisture content, total phenolic content, protein and acetic acid compared to the Heterotrigona itama’s honey samples. To conclude, the geographical and floral origins of honey are the two important quality parameters which fundamentally affect the physical-chemical properties as well as biological activities of honey samples

Neuro fuzzy classification and detection technique for bioinformatics problems

Bioinformatics is an emerging science and technology which has lots of research potential in the future. It involves multi-interdisciplinary approaches such as mathematics, physics, computer science and engineering, biology, and behavioral science. Computers are used to gather, store, analyze as well as integration of patterns and biological data information which can then be applied to discover new useful diagnosis or information. In this study, the focus was directed to the classification or clustering techniques which can be applied in the bioinformatics fields based on the Sugeno type neuro fuzzy model or ANFIS (adaptive neuro fuzzy inference system). It is very important to identify new integration of classification or clustering algorithm especially in neuro fuzzy domain as compared to conventional or traditional method. This paper explores the suitability and performance of recurrent classification technique, fuzzy c means (FCM) act as classifier in neuro fuzzy system compared to subclustering method. A package of software based on neuro fuzzy model (ANFIS) has been developed using MATLAB software and optimization were done with the help from WEKA. A set diabetes data based on real diagnosis of patient was used