An Application of Bayesian Method in Packaged Food Quality Control

Conventional method of making statistical inference regarding food quality measure is absolutely based upon experimental data. It refuses to incorporate prior knowledge and historical data on parameter of interest. It is not well suited in the food quality control problems. We propose to use a Bayesian approach inferring the conformance of the data concerning quality run. This approach integrates the facts about the parameter of interest from the historical data or from the expert knowledge. The prior information are used along with the experimental data for the meaningful deduction. In this study, we used Bayesian approach to infer the weight of pouched ghee. Data are taken selecting random samples from a dairy industry. The prior information about average weight and the process standard deviation are taken from the prior knowledge of process specification and standards. Normal-Normal model is used to combine the prior and experimental data in Bayesian framework. We used user-friendly computer programmes, ‘First Bayes' and ‘WinBUGS' to obtain posterior distribution, estimating the process precision, credible intervals, and predictive distribution. Results are presented comparing with conventional methods. Fitting of the model is shown using kernel density and triplot of the distributions. Key words: credible interval; kernel density; posterior distribution; predictive distribution; triplot DOI: 10.3126/njst.v9i0.3163 Nepal Journal of Science and Technology 9 (2008) 41-48