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The long-term goal of this project is to continuously improve the washing process in the food industry while enhancing food safety and quality in the fresh-cut produce sector. The specific overall goal of this project is to develop a novel hurdle technology by combining ultrafine bubbles and ozone to enhance the next generation washing strategy for fresh-cut produce. The technology development is supported by an integrated approach combining a mechanistic based approach and artificial intelligence (AI) based approach.The following specific aims will be addressed:Characterization of a UO3B system. The relevant physicochemical properties of the UO3B solution to be used for washing fresh-cut produce will be characterized by carrying out systematic experiments. In particular, we will focus on the influence of processing conditions. Different processing conditions include both the UO3B generation (ozone inlet concentration, liquid flow rate, type of ultrafine bubble generator, and generation duration) and washing conditions (temperature, organic compound load, and UO3B flow rate) on UO3B properties (mass transfer coefficient kLa, bubble size, bubble size distribution, z potential, and ozone concentration) will be established. The established process property relation will be critical in the systematic evaluation of the UO3B washing effect in Aim 2 and 3.Initial screening and optimization of a UO3B system. To systematically evaluate the influence of a wide range of processing parameter space on the washing efficacy of the UO3B system, we will develop a quick and novel initial screening and optimization protocol in this Aim. The novel screening and optimization protocol combine a single strain microtiter screening system, artificial intelligence based mathematical modeling, and a multiobjective genetic algorithm (MOGA) based optimization process.This innovative protocol offers not only quick and reliable tests for initial screening, but also a large set of experimental data sets will be generated as training data as well as internal validation procedures for the neural network-based AI mathematical model developed in this Aim.Dynamic evaluation of the washing efficacy of a UO3B system. With the initially optimized processing conditions obtained from Aim2, we will conduct a series of targeted tests of the UO3B system in an engineered bench-top flume washer and evaluate the influence of the dynamic conditions on the washing efficacy. We hypothesis that the dynamic conditions associated with the flow will further enhance the microbiocidal effect of the UO3B washing system. In this Aim, we will also test to what extent that we can minimize the use of ozone while maintaining the washing efficacy and improving the quality of the fresh-cut produce by the support of both microbiology analyses and panel sensory tests. We will also conduct a microscopic analysis to gain insight into the possible mechanisms of the developed antimicrobial system.

Lu, Jiakai
University of Massachusetts
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