STANDARDIZATION OF PROCESS PARAMETERS FOR READY TO EAT FISH PRODUCTS IN INDIGENOUS POLYMER COATED TIN FREE STEEL CANS

In the present work, Indigenous polymer coated Tin Free Steel cans were analyzed fortheir suitability for thermal processing and storage of fish and fish products following standard methods. The raw materials used for the development of ready to eat thermally processed fish products were found to be of fresh condition. The values for various biochemical and microbiological parameters of the raw materials were well within the limits. Based on the analysis of commercial sterility, instrumental colour, texture, WB-shear force and sensory parameters, squid masala processed to F0 value of 8 min with a total process time of 38.5 min and cook value of 92 min was chosen as the optimum for squid masala in tin free steel cans while shrimp curry processed to F0 7 min with total process time of 44.0 min and cook value of 91.1 min was found to be ideal and was selected for storage study. Squid masala and shrimp curry thermally processed in indigenous polymer coated TFS cans were found to be acceptable even after one year of storage at room temperaturebased on the analysis of various sensory and biochemical parameters. Analysis of the Commission Internationale d’ Eclirage L*, a* and b* color values showed that the duration of exposure to heat treatment influenced the color parameters: the lightness (L*) and yellowness (b*)decreased, and the redness (a*) significantly increased with the increase in processing time or reduction in processing temperature.Instrumental analysis of texture showed that hardness-1 & 2 decreased with reduction in retort temperature while cohesiveness value did not show any appreciable change with decrease in temperature of processing. Other texture profile parameters like gumminess, springiness and chewiness decreased significantly with increase of processing time. W-B shear force values of mackerel meat processed at 130 °C were significantly higher than those processed at 121.1 and 115 °C. HTST processing of mackerel in brine helped in reducing the process time and improving the quality.The study also indicated that indigenous polymer coated TFS cans with easy openends can be a viable alternative to the conventional tin and aluminium cans. The industry can utilize these cans for processing ready to eat fish and shell fish products for both domestic and export markets. This will help in reviving the canning industry in India.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.