Growth potential of Salmonella spp. and Listeria monocytogenes in nine types of ready-to-eat vegetables stored at variable temperature conditions during shelf-life

Growth potential (delta) is defined as the difference between the population of a microorganism at the end of shelf-life of specific food and its initial population. The determination of 6 of Salmonella and Listeria monocytogenes in RTE vegetables can be very useful to determine likely threats to food safety. However, little is known on the behavior of these microorganisms in several RTE vegetables. Therefore, the aim of this study was to determine the delta of both pathogens in nine different types of RTE vegetables (escarole, collard green, spinach, watercress, arugula, grated carrot, green salad, and mix for yakisoba) stored at refrigeration (7 degrees C) and abuse temperature (15 degrees C). The population of aerobic microorganisms and lactic acid bacteria, including those showing antimicrobial activity has been also determined. Results indicated that L monocytogenes was able to grow (delta >= 0.5 log(10)) in more storage conditions and vegetables than Salmonella. Both microorganisms were inhibited in carrots, although a more pronounced effect has been observed against L monocytogenes. The highest 5 values were obtained when the RTE vegetables were stored 15 degrees C/6 days in collard greens (delta=3.3) and arugula (delta=3.2) (L monocytogenes) and arugula (delta=4.1) and escarole (delta=2.8) (Salmonella). In most vegetables and storage conditions studied, the counts of total aerobic microorganisms raised significantly independent of the temperature of storage (p<0.05). Counts of lactic acid bacteria were higher in vegetables partially or fully stored at abuse temperature with recovery of isolates showing antimicrobial activity. In conclusion, the results of this study show that Salmonella and L monocytogenes may grow and reach high populations in RTE vegetables depending on storage conditions and the definition of effective intervention strategies are needed to control their growth in these products. (C) 2012 Elsevier B.V. All rights reserved.

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

A log-Birnbaum-Saunders regression model with asymmetric errors

This paper introduces a skewed log-Birnbaum-Saunders regression model based on the skewed sinh-normal distribution proposed by Leiva et al. [A skewed sinh-normal distribution and its properties and application to air pollution, Comm. Statist. Theory Methods 39 (2010), pp. 426-443]. Some influence methods, such as the local influence and generalized leverage, are presented. Additionally, we derived the normal curvatures of local influence under some perturbation schemes. An empirical application to a real data set is presented in order to illustrate the usefulness of the proposed model.

A log-linear regression model for the beta-Birnbaum-Saunders distribution with censored data

The beta-Birnbaum-Saunders (Cordeiro and Lemonte, 2011) and Birnbaum-Saunders (Birnbaum and Saunders, 1969a) distributions have been used quite effectively to model failure times for materials subject to fatigue and lifetime data. We define the log-beta-Birnbaum-Saunders distribution by the logarithm of the beta-Birnbaum-Saunders distribution. Explicit expressions for its generating function and moments are derived. We propose a new log-beta-Birnbaum-Saunders regression model that can be applied to censored data and be used more effectively in survival analysis. We obtain the maximum likelihood estimates of the model parameters for censored data and investigate influence diagnostics. The new location-scale regression model is modified for the possibility that long-term survivors may be presented in the data. Its usefulness is illustrated by means of two real data sets. (C) 2011 Elsevier B.V. All rights reserved.