By Ebele Orakpo
In Nigeria, people buy dairy products produced in uncer-tain environments, from hawkers. To monitor the shelf-life of such products, the trio of Balogu, T., Gurusu, H. and Balogu of the Ibrahim Badamasi Babangida University’s Centre for Applied Sciences and Technology Research, developed computerized graphic interface software for shelf-life prediction of dairy products using microbial growth kinetic models.
To assess the behaviour and effect of microbial seeds challenged in two Nigerian indigenous fermented dairy products (Nono and Kindirimo) processed and stored in uncertain conditions.
The researchers randomly collected a total of 500 samples from unsuspecting vendors and producers. Samples were stored at unregulated (uncertain) environment. “Variation on the nutritional composition (moisture, ash & lipid) of samples were significant and this validates the inherent uncertainty factors of processing methods.
Microbial growth rate, pH and temperature kinetic parameter models resolved with polynomial 6th order equations with R2 (0.95 – 0.99) and probability density functions resolved the uncertain factor on the hypothesis of H0: E(Y|X=x) = µ; where Y=µ. H1: E(Y|X=x) = ß0 + ß1x + ß2×2 + ß3×3 + ß4×4 + ß5×5 + ß6×6.
“Stochastic (uncertain) effect of uncertainty conditions were resolved to achieve perfect R2=1.0. The normalised models were used to develop computerized predictive software that forcast the shelf-life of indigenous diary products at uncertain conditions with 95% Confidence band.
Basic capability of the software include instant prediction of sample shelf-life of raw milk, fermented products etc.
Uncertainties considered and harmonized
“The essence of every predictive model is to predict the future responses at any period. Considering the uncertain nature of the adopted model parameters, the generated output cannot be predicted in a deterministic manner; rather, it will be random variables, which is characterized by means of some probability density function (Balogu et al., 2014). Uncertainties may also include scare monitoring parameters, or the inability to neither measure them nor even stimulate them in the laboratory. Sources of uncertainty and variability in predictive microbiology include: unknown exact initial microbial load or its composition, the extrinsic and intrinsic processing conditions that determine the microbial behaviour, e.g., temperature and product composition, usually vary within the same product, between products and between subsequent batches.
Discrepancies between Challenge test and laboratory test for assessment of food shelf-life are limited due to intervening uncertain factors.
The predictive models of this study normalises these uncertain factors to achieve high predictive precision at 95% confidence limit.
The researchers called on government agencies and private food industrialists to fund research in this direction “to develop complete database of predictive shelf-life of indigenous foods to drive the growth of our food industry.
On-site monitoring and risk assessment of fresh foods vendors to unsuspecting customers could be possible with this system.”