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In Sri Lankan traditional cooking, coconut and spices are incorporated to enhance the taste, flavor, and aroma. However, little attention has been given to assess the effect of these ingredients on the nutritional and chemical composition of the consumed food. The objective of this study was to ascertain the effect of traditional cooking methods on the chemical composition of vegetables, cereals and cereal-based foods, legumes, and selected nonvegetarian food varieties consumed in the daily diet. The results indicate that the addition of coconut milk (CM), coconut scraps, and coconut oil (CO) had a significant impact on the fat content of the prepared foods ( p < 0.05 ). Cooking facilitated the incorporation of fat into food. According to the results, more percentage increases of fat content were observed in tempered string beans (97.51%) and cauliflower milk curry (96.6%). Data revealed that boiling helped to reduce the fat content in cereals and legumes. The cooked foods prepared using traditional recipes with CM, CO, or scraps have higher nutritional content than raw foods and have a significant nourishing potential that meets the daily energy requirements ( p < 0.05 ). It can be concluded that the chemical composition of cooked food serves as a more realistic guideline in recommending dietary interventions in disease and weight management.

Many real world applications are associated with more than one variable and hence, identifying multivariate distributions associated with real world problems portrays great importance today. Many studies can be found in the literature in this aspect and most of them are associated with two variables/dimensions and the maximum dimension of multivariate distribution found in the literature is four. Different optimization techniques have been used by researchers to find multivariate distributions in their studies. Numerical methods can be identified as more preferable than analytical methods when the dimension of the problem is high. The main objective of this study is to identify the multivariate distribution associated with the return series of Australian all ordinary index (AORD) and those of the related financial markets and compare it with the multivariate distribution of return series of the US GSPC index and its related financial markets. No research were found in the literature which were aimed at finding aforesaid multivariate distribution and comparisons. Moreover no evidence found for identifying a multivariate distribution with six dimensions. Five financial markets: Amex oil index, Amex gold index, world cocoa index, exchange rate of Australian dollar to United States dollar and US GSPC index were found to be associated with AORD. Hence the attempt was to derive the multivariate distribution of return series of AORD and these five return series and therefore the optimization problem of the study is a six dimension problem which associated with forty three parameters need to be estimated. A local optimization technique and a global optimization technique were used to estimate the parameters of the multivariate distribution. Results exhibit that the parameter estimates obtained from the global optimization technique are better than the parameter estimates obtained from the local optimization technique. The multivariate distribution of return series of AORD and related financial markets is central, less peaked and have fat tails. A comparison was done with another multivariate distribution of a return series of a leading stock market index: GSPC and return series of its associated financial markets and found that both distributions are alike in shape. Two periods were identified in the AORD series and found that the shape of the multivariate distribution of one period is similar to the shape of the multivariate distribution of full data set while the shape of the multivariate distribution of the other period is dissimilar to that of full data set.

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