A proforma for assessment of the levels of sanitation in fish processing establishments

Over the past 15 years of its development, the fish processing industry in India has shown considerable improvement in maintenance of hygiene during handling of the raw material, processing and marketing of the finished product. This is best manifested in the lowering of upper limits of bacterial loads in factory environs and in processed products (Pillai, 1971). More care and attention is given by the processors in recent years in the scientific cleaning and sanitizing of utensils and equipment, chlorination of water supplies and personnel hygiene. An example of sanitation score form is given to help scientists and technologists to evaluate the hygienic status of the processing units.

Scientific statistics and graphics on the Macintosh

The personal computer has become commonplace on the desk of most scientists. As hardware costs have plummeted, software capabilities have expanded enormously, permitting the scientist to examine extremely large datasets in novel ways. Advances in networking now permit rapid transfer of large datasets, which can often be used unchanged from one machine to the next. In spite of these significant advances, many scientists still use their personal computers only for word processing or e-mail, or as "dumb terminals". Many are simply unaware of the richness of software now available to statistically analyze and display scientific data in highly innovative ways. This paper presents several examples drawn from actual climate data analysis that illustrate some novel and practical features of several widely-used software packages for Macintosh computers.

Statistical quality control charts on attributes in fish processing technology

Apart from the use of statistical quality control chart for variables or attributes of food products in a food processing industry, the application of these charts for attributes of fishery products is explained. Statistical quality control chart for fraction defectives is explained by noting defective fish sausages per shift from a sausage industry while control chart for number of defectives is illustrated for number of defective fish cans in each hour of its production of a canning industry. C-chart is another type of control chart which is explained here for number of defects per single fish fillet sampled a1l random for every five minutes in a processing industry. These statistical quality control charts help in the more economic use of resource, time and labour than control charts for variables of products. Also control charts for attributes exhibit the quality history of finished products at different times of production thereby minimizing the risk of consumer rejection.