Long-term kinetics of Salmonella Typhimurium ATCC 14028 survival on peanuts and peanut confectionery products.

Due to recent large outbreaks, peanuts have been considered a product of potential risk for Salmonella. Usually, peanut products show a low water activity (aw) and high fat content, which contribute to increasing the thermal resistance and survival of Salmonella. This study evaluated the long-term kinetics of Salmonella survival on different peanut products under storage at 28°C for 420 days. Samples of raw in-shell peanuts (aw = 0.29), roasted peanuts (aw = 0.39), unblanched peanut kernel (aw = 0.54), peanut brittle (aw = 0.30), paçoca (aw = 0.40) and pé-de-moça (aw = 0.68) were inoculated with Salmonella Typhimurium ATCC 14028 at two inoculum levels (3 and 6 log cfu/ g). The Salmonella behavior was influenced (p<0.05) by aw, lipid, carbohydrate and protein content. In most cases for both inoculum levels, the greatest reductions were seen after the first two weeks of storage, followed by a slower decline phase. The lowest reductions were verified in paçoca and roasted peanuts, with counts of 1.01 and 0.87 log cfu/ g at low inoculum level and 2.53 and 3.82 log cfu/ g at high inoculum level at the end of the storage time. The highest loss of viability was observed in pé-de-moça, with absence of Salmonella in 10-g after 180 days at low inoculum level. The Weibull model provided a suitable fit to the data (R2≥0.81), with δ value ranging from 0.06 to 49.75 days. Therefore, the results demonstrated that Salmonella survives longer in peanut products, beyond the shelf life (>420 days), especially in products with aw around 0.40.

Asymptotic behavior of the scaled mutation rate estimators.

Important aspects of population evolution have been investigated using nucleotide sequences. Under the neutral Wright-Fisher model, the scaled mutation rate represents twice the average number of new mutations per generations and it is one of the key parameters in population genetics. In this study, we present various methods of estimation of this parameter, analytical studies of their asymptotic behavior as well as comparisons of the distribution's behavior of these estimators through simulations. As knowledge of the genealogy is needed to estimate the maximum likelihood estimator (MLE), an application with real data is also presented, using jackknife to correct the bias of the MLE, which can be generated by the estimation of the tree. We proved analytically that the Waterson's estimator and the MLE are asymptotically equivalent with the same rate of convergence to normality. Furthermore, we showed that the MLE has a better rate of convergence than Waterson's estimator for values of the parameter greater than one and this relationship is reversed when the parameter is less than one.