Microbial quality of shrimp products of export trade produced from aquacultured shrimp

Bacteriological quality of individually quick frozen (IQF) shrimp products produced from aquacultured tiger shrimp (Penaeus monodon) has been analysed in terms of aerobic plate count (APC), coliforms, Escherichia coli, coagulase-positive staphylococci, Salmonella, and Listeria monocytogenes. Eight hundred forty-six samples of raw, peeled, and deveined tail-on (RPTO), 928 samples of cooked, peeled, and deveined tail-on (CPTO), 295 samples of headless, undeveined shell-on (HLSO), and 141 samples of raw, peeled, and deveined tail-off (RPND) shrimps were analysed for the above bacteriological parameters. Salmonella was isolated in only one sample of raw, peeled tail-on. Serotyping of the strain revealed that it was S. typhimurium. While none of the cooked, peeled tail-on shrimp samples exceeded the aerobic plate count (APC) of 105 colony forming units per gram (cfu/g), 2.5% of raw, peeled, tail-on, 6.4% of raw, peeled tail-off, and 7.5% of headless shell-on shrimp samples exceeded that level. Coliforms were detected in all the products, though at a low level. Prevalence of coliforms was higher in headless shell-on (26%) shrimps followed by raw, peeled, and deveined tail-off (19%), raw, peeled tail-on (10%), and cooked, peeled tail-on (3.8%) shrimps. While none of the cooked, peeled tail-on shrimp samples were positive for coagulase-positive staphylococci and E. coli, 0.6–1.3% of the raw, peeled tail-on were positive for staphylococci and E. coli, respectively. Prevalence of staphylococci was highest in raw, peeled tail-off (5%) shrimps and the highest prevalence of E. coli (4.8%) was noticed in headless shell-on shrimps. L. monocytogenes was not detected in any of the cooked, peeled tail-on shrimps. Overall results revealed that the plant under investigation had exerted good process control in order to maintain superior bacteriological quality of their products

A Study on India’s Trade Relationship with SAARC Countries with Special Reference to SATA

The present study reveals that there are enormous opportunities for forging closer economic relations among SAARC countries. These opportunities could be fully utilized through the twin processes of trade liberalization and industrial restructuring which are complementary to each other. The SAARC Preferential Trade Arrangement (SAPTA) is the first step in trade liberalization. However, the scope of SAPTA has to be sufficiently widened in order to derive substantial benefits from preferential trading agreements. It is suggested that the SAARC countries adopt a combined approach for tariff elimination, tariff reduction and preferential or concessional tariffs. This process will help in moving quickly towards the creation of a Free Trade Area in the SAARC region. It is necessary to emphasis that, in any regional organization, smaller countries may feel that greater trade co-operation with their larger neighbors may result in larger countries taking over their economies. India occupies 70% of the SAARC region, both geographically and economically, and the remaining 6 nations of the SAARC borders only with India and not with each other. As the biggest, and the most industrialized trading partner among the SAARC countries, India has to recognize that a special responsibility devolves on her and take a lead in making the Regional Economic Co-operation a reality in South Asia.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year