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The keeping quality and shelf life of fermented and unfermented Chrysichthys nigrodigitatus were monitored in this study. Four kilograms of fresh Chrysichthys nigrodigitatus was minced into fine particles (with an initial pH of 7.2 before distribution into 8 samples). Samples 1-4 are unfermented cooked while Samples 5-8 were fermented, not cooked. All the 8 prepared samples barely lasted for two weeks, while samples 1, 3 and 7 lasted for six weeks. Total Volatile Base (TVB) ranged higher (24.12 - 29.43) mg/100gm in Samples 1-4 than (14.23 - 18.09) mg/100gm recorded in Samples 5-8. In Samples 1-4, FFA values were not significantly (P > 0.05) different; also followed a narrow range of (6.14 - 6.45)% while higher range of (6.42 - 12.27)% recorded in samples (5-8). Peroxide values (PV) increased in all the 8 samples in the second, fourth and sixth week, however higher values were recorded in Samples 5-8. Acidity generally increased with length (weeks) of fermentation with a gradual drop in pH from 7.2 (in the fresh fish) to pH 4.5 (sample 7), the worst sample at six weeks. Sample 4 with bacteria load of 5.05 × 105 at second week and sample 7 (8.2 × 105) at sixth week became unfit for consumption having exceeded the 5.0 × 105 ICMSF standard for safe fish product. Five bacteria species (Lactobacillus sp, Proteus spp, Staphylococcus aureus, Staphylococcus epidermis, Bacillus sp) with the exception of Proteus spwere not represented in sample 1 (due to salt content). Strong positive correlation (r = 0.97, P < 0.01) exists between PV and FFA. Acidityof the fermented products increased over the weeks with strong negative correlation (r = -0.121, P < 0.01) exists between pH and FFA. Acidity (i.e drop in PH) with increasing rancidity since (r = -0.313, P < 0.05) exists between PV and pH.
Forecasts can either be short term, medium term or long term. In this work we considered short term forecast because of the problem of limited data or time series data that is often encounter in time series analysis. This simulation study considered the performances of the classical VAR and Sims-Zha Bayesian VAR for short term series at different levels of collinearity and correlated error terms. The results from 10,000 iteration revealed that the BVAR models are excellent for time series length of T=8 for all levels of collinearity while the classical VAR is effective for time series length of T=16 for all collinearity levels except when ρ = -0.9 and ρ = -0.95. We therefore recommended that for effective short term forecasting, the time series length, forecasting horizon and the collinearity level should be considered.