ABSTRACT
Microbiological quality of smoked catfish (Clarias gariepinus) locally known as Mlamba was assessed in this study where traditional and improved smoking kilns were used to smoke fish. Catfish is common fish caught in abundance in the Lake Chilwa basin, and the fish is usually smoked to reduce postharvest losses and increase shelf life. Samples were collected in sterile polythene bags, well labeled, and collected in cooler boxes transported ready for laboratory analysis. One gram representative sample was obtained aseptically from the muscle of the fresh and smoked catfish (Mlamba) samples. The samples were grounded, and fourfold serial dilutions (10-1-10-4) of the homogenized samples were made using sterile distilled water. Fish samples were analyzed for total plate count (TPC), Escherichia coli counts, and pathogenic organisms (Salmonella) following the methods prescribed by AOAC (Official methods of analysis, Association of Official Analytical Chemistry, Arlington, VA, 2000). Each analysis was carried out in triplicates. There were significant differences (p = 0.05), with respect to total viable bacterial counts between traditional kiln smoked and improved kiln smoked catfish (5.6 × 106 cfu/g, 1.9 × 106 cfu/g, respectively). Traditional kiln smoked catfish harbored significantly higher total viable counts as well as a higher population of E. coli compared to improved kiln smoked catfish. However, for both types of smoking kilns there were detected levels of pathogenic bacteria Salmonella with traditional kiln smoked catfish containing 2.1 × 104 cfu/g which were significantly higher than amount found in improved kiln smoked catfish (1.5 × 104 cfu/g; p = 0.05). Salmonella is a microbe of public health importance and has implications on the handling and source of the fish.
ABSTRACT
BACKGROUND: Neonatal mortality contributes a large proportion towards early childhood mortality in developing countries, with considerable geographical variation at small areas within countries. METHODS: A geo-additive logistic regression model is proposed for quantifying small-scale geographical variation in neonatal mortality, and to estimate risk factors of neonatal mortality. Random effects are introduced to capture spatial correlation and heterogeneity. The spatial correlation can be modelled using the Markov random fields (MRF) when data is aggregated, while the two dimensional P-splines apply when exact locations are available, whereas the unstructured spatial effects are assigned an independent Gaussian prior. Socio-economic and bio-demographic factors which may affect the risk of neonatal mortality are simultaneously estimated as fixed effects and as nonlinear effects for continuous covariates. The smooth effects of continuous covariates are modelled by second-order random walk priors. Modelling and inference use the empirical Bayesian approach via penalized likelihood technique. The methodology is applied to analyse the likelihood of neonatal deaths, using data from the 2000 Malawi demographic and health survey. The spatial effects are quantified through MRF and two dimensional P-splines priors. RESULTS: Findings indicate that both fixed and spatial effects are associated with neonatal mortality. CONCLUSIONS: Our study, therefore, suggests that the challenge to reduce neonatal mortality goes beyond addressing individual factors, but also require to understanding unmeasured covariates for potential effective interventions.