Browsing by Author "Rathnayaka, N.S."

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    Development of a Cassava (Manihot esculenta) Starch Flour based Noodle
    (Uva Wellassa University of Sri Lanka, 2013) Missriya, M.N.F.; Mahindarathne, M.G.P.P.; Bandara, S.M.I.P.G.; Rathnayaka, N.S.
    Wheat flour is used as the major gluten source for manufacturing of noodles. But, it is quite expensive and contains less nutritional value. It is important to reduce the dependency on imported wheat flour as a food raw material. Therefore, Sri Lankan food industry must focus on substitution of wheat flour with locally available food raw materials for commercial food manufacturing. Cassava (Manihotesculenta) is locally available root crop which gives higher yield during the period of harvesting. Cassava flour can be proposed as a substitute for wheat flour due to closely similar nutritional composition of cassava flour and wheat flour, relatively easy cultivation and high productivity of cassava which results lower price for cassava. This research is an attempt to develop a cassava starch flour based noodles while addressing the specific objectives of determination of the best ration of cassava flour to wheat flour to obtain preferred organoleptic characteristics of the final product, to determine the most suitable time period to steam the selected sample and to determine the chemical composition and to determine the shelf life of the products. Methodology The research was designed in three experiments in each according to complete randomized design. In the first experiment, the cassava flour based noodles product was developed after determination of the best ration of cassava and wheat flour from the five different treatments tested. Treatments were prepared incorporating cassava flour in levels 60% (A), 50% (B), 40% (C), 30% (D) and 20% (E) with wheat flour. The best ratio was determined by a sensory evaluation. The organoleptic properties of flavor, odor, texture, color and overall acceptability were evaluated in each treatment based on a five point hedonic scale. In the second experiment, the selected sample was dipped in 100 C boiling water for 1, 2, 3, 4 and 5min to determine the best time period to steam the selected noodles product using a sensory evaluation by thirty semi-trained panelists based on a five point hedonic scale. Same sensory properties were evaluated as in the first experiment. Then, the sensory properties of the selected noodle were compared with control sample prepared only using wheat flour. In the third experiment, nutrient composition, microbial quality and shelf life analysis were done in the cassava flour based noodles developed according to the findings of the above experiments. Moisture, crude protein, crude fat, crude fiber, total carbohydrate and total ash contents were determined in proximate analysis and gruel content was measured to analyze the chemical composition of the final product. Total plate count was calculated and shelf life analysis was done. Results and Discussion Analyzed statistical data of the first experiment revealed that Treatment D (incorporated with 30% cassava flour (D) occupies the outermost boundary for all the sensory attributes tested other than odor (Figure 1). There were significant differences (P<0.05) in the sensory attributes. Best selected noodle (30% cassava flour) has the higher preference than control in all the attributes other than texture
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    Modification of information gain measure to select the best group of attributes in a data set for a binary decision tree inducer
    (Uva Wellassa University of Sri Lanka, 2015) Rathnayaka, N.S.; Wijerathna, J. K.
    Classification is one of the frequently used techniques in data mining processes which can be applied to accurately predict the target class for each case in a data set. The Decision tree (DT) algorithms are one of the powerful classification and prediction methods which facilitate decision making in sequential decision making for a given dataset (Han & Kamber, 2006; Bramer, 2007). The major strengths of the DT algorithms are their ability to generate understandable rules, to handle both numerical and categorical attributes and also provide a clear indication of which attributes are most salient for prediction or classification (Kangaiammal, 2013). ID3 and C4.5 are multi splitting algorithms and developed by J. Ross Quinlan in1986 and 1993 respectively. That can be used to Entropy, information gain (IG) and Gain ratio as attribute selection measures. These measurements can be utilized to make the binary decision tree to reduce the complexity of the decision tree. If the algorithm identifies more than one attributes with equal IG in the data set, then it will select the initial attribute as a splitting node of a tree. This attribute may not be the best attribute for decision making when it is compared with the other attributes of equal IG. Therefore, the aim of this study is to improve the IG measure to select the best attribute in a dataset and plot a binary decision tree.