Microbiological Shelf Life of Pasteurized Milk in Bottle and Pouch

Shelf life of pasteurized milk in Brazil ranges from 3 to 8 d, mainly due to poor cold chain conditions that prevail throughout the country and subject the product to repeated and/or severe temperature abuse. This study evaluated the influence of storage temperature on the microbiological stability of homogenized whole pasteurized milk (75 degrees C/15 s) packaged in high-density polyethylene (HDPE) bottle and low-density polyethylene (LDPE) pouch, both monolayer materials pigmented with titanium dioxide (TiO(2)). The storage temperatures investigated were 2, 4, 9, 14, and 16 degrees C. Microbiological evaluation was based on mesophilic and psychrotrophic counts with 7 log CFU/mL and 6 log CFU/mL, respectively, set as upper limits of acceptability for maintaining the quality of milk. The microbiological stability for pasteurized milk packaged in HDPE bottle and stored at 2, 4, 9, 14, and 16 degrees C was estimated at 43, 36, 8, 5, and 3 d, respectively. For milk samples packaged in LDPE pouch, shelf life was estimated at 37, 35, 7, 3, and 2 d, respectively. The determination of Q(10) and z values demonstrated that storage temperature has a greater influence on microbiological shelf life of pasteurized milk packaged in LDPE pouch compared to HDPE bottle. Based on the results of this study, HDPE bottle was better for storing pasteurized milk as compared to LDPE pouch.

On estimation and influence diagnostics for log-Birnbaum-Saunders Student-t regression models: Full Bayesian analysis

The purpose of this paper is to develop a Bayesian approach for log-Birnbaum-Saunders Student-t regression models under right-censored survival data. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the considered model. In order to attenuate the influence of the outlying observations on the parameter estimates, we present in this paper Birnbaum-Saunders models in which a Student-t distribution is assumed to explain the cumulative damage. Also, some discussions on the model selection to compare the fitted models are given and case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback-Leibler divergence. The developed procedures are illustrated with a real data set. (C) 2010 Elsevier B.V. All rights reserved.