Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/2663
Title: Effect of bacteriophage control and artificial neural networks prediction in the inactivation of Listeria monocytogenes on fresh produce
Authors: Oladunjoye, Adebola Olubukola 
Issue Date: 2017
Abstract: 
There has been a global increase in fresh produce consumption, due to its attendant nutritional a nd health benefits. On the other hand, increase in the outbreak of diseases, accompanied with health and economic implications, have been traced to this deve lopment. A good number of pathogenic contaminants along the food chain have been identified as causative agent s with Listeria monocytogenes identified as one of such. Among other control strategies, the use of bacteriophage, was recommended as a palliative measure. Furthermore, the a ppli cation of artificial neural networks (ANN) in food safety remains an emerging concept in risk assessment study. Therefore, the aim of this research is to investigate the effect of bacteriophage or phage control and artificial neural network prediction in the inactivation of L. monocytogenes ATCC 7644 on fresh produce. Fresh-cut tomato and carrot were artificially inoculated with L. monocytogenes (108 CFU/ml) and subjected to antimicrobial treatment of Listex P100 bacteriophage (108 PFU/ml), sucrose monolaurate (SML at 100, 250 and 400 ppm), with chlorine (sodium hypochlorite at 200 ppm) used as control. Also, application of ANN to predict the risk effect of antimicrobial treatments of bacteriophage, sucrose monolaurate and chlorine was evaluated on the fresh-cut produce. Mathematical models were developed using a linear regression and sigmoid (hyperbolic and logistic) activation function-(120). Data sets were trained using Back propagation ANN, containing one hidden layer with four hidden neurons. Furthermore, carbon utilization profile of phage-treated L. monocytogenes using phenotypic micro array method was evaluated. In the first phase, susceptibility of L. monocytogenes subjected to certain stress-adapted conditions (acid,-adapted AA, chlorine-adapted CA, heat-adapted HA) and non-adapted-NA to phage treatment inoculated on the fresh-cut produce stored for 10 days at 4, 10 and 25oC was evaluated. The second phase investigated the combination of bacteriophage and sucrose monolaurate (using chlorine at 200 ppm as control) to inhibit the L. monocytogenes growth on the fresh-cut produce stored for 6 days at 4, 10 and 25oC. Physicochemical properties (pH, titratable acid-TTA, total soluble solids-TSS, and colour values-CIE L* a* b*) of the fresh produce after treatment were evaluated. In the third phase, ANN as a predictive tool was used to evaluate the risk involved in the relationship among the initial bacterial load, fresh-produce type, antimicrobial concentration and residual bacteria. In the final phase, 100 µL of phage-treated L. monocytogenes was introduced into a 96-micro well plate impregnated with a tetrazolium dye. The Carbon utilization profile was evaluated at intervals of 4 hours for 48 hours using a biolog micro station. Generally, L. monocytogenes grew on both fresh-cut produce and the storage temperature did not adversely affect the lytic ability of the phage treatment. Antimicrobial treatment of phage and sucrose monolaurate had minimal variations on the physicochemical properties of both fresh-cut samples. All stress-adapted and non-adapted L. monocytogenes were (p ≤ 0.05) susceptible to bacteriophage control. Phage treatment reduced non-adapted, acid adapted, chlorine-adapted, and heat-adapted L. monocytogenes population by 0.57, 0.81, 0.86 and 0.95 log CFU/ml in fresh-cut tomato, and 2.26, 2.41, 2.49 and 2.54 log CFU/ml in fresh cut carrot respectively. Furthermore, the additive effect of SML at 100 and 250 ppm had no significant effect on phage lysis. However, combination of phage with SML at 400 ppm significantly (p ≤ 0.05) resulted in 1 and 3 fold reductions in tomato and carrot respectively. Control treatment with chlorine resulted in 1-2 log reductions on both fresh produce. Algorithm data set trained using ANN gave 100% accuracy. Prediction with logistic activation function showed the highest positive correlation relationship between predicted and observed values with ~ 0.99 R2-value and MSE of 0.0831. Carbon utilization profile showed hexose and pentose sugars-ribose, glucose, fructose and sugars were maximally utilized while oligosaccharide sugars of sucrose, cellobiose and gentiobiose were similarly observed to be utilized. Notably, utilization of glucose-6-phosphate which determines L. monocytogenes pathogenicity was not very pronounced in the carbon profile. Bacteriophage application in the inactivation of L. monocytogenes contamination of fresh produce provides a safe means of control. Its perceived limitation however, can be overcome by combining with other antimicrobials. Similarly, the use of artificial neural networks prediction, remains an improved approach to harness the potential risk that could occur through this method.
Description: 
Submitted in fulfillment of the academic requirement for a degree in Doctor of Philosophy (Ph.D.) in Food Science and Technology, Durban University of Technology, Durban, South Africa, 2017.
URI: http://hdl.handle.net/10321/2663
DOI: https://doi.org/10.51415/10321/2663
Appears in Collections:Theses and dissertations (Applied Sciences)

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