Jan. 1, 2018 - Dec. 31, 2018Amount Awarded
Daniel Munther, Ph.D.
Cleveland State University
Parthasarathy Srinivasan, Ph.D., Chandrasekhar Kothapalli, Ph.D.Resources
Food borne diseases associated to fresh produce continue to cause serious difficulties for public health in the United States. To offset this burden, the produce wash stage has received much attention as a critical control point. However, recent studies indicate a limited understanding of the dynamics of sanitizer control during washing. One problem is that the relationships between water quality constituents and sanitizer levels have only been described via experimental/correlative approaches or by risk models that are difficult to parameterize accurately. Accordingly, there is an urgent need to mathematically describe the fundamental dynamics that generate the observed relationships between sanitizer levels and water quality parameters.
Based on such formulations, our long-term goal is to develop optimal sanitizer strategies that are easily automatable and adjustable to specific commodities and washing practices. The primary objective of this proposal is to develop data-informed modeling tools which quantitatively link easily measurable water quality parameters (e.g. turbidity/total dissolved solids) to commodity specific organic load and free chlorine consumption during recirculated wash conditions. Based on USDA experimental data and our recent modeling results, we hypothesize that by using our modeling tools, the industry can obtain reliable predictive capabilities that are not possible with correlations alone.
As globalization has broadened the fresh produce supply chain and increased its complexity, more sophisticated methods of surveillance are needed to ensure the safety of fresh products. In particular, the processing juncture is a critical control point that has received much attention. While many wash systems are being used in good faith and according to standard protocols, it is clear that a greater understanding of in-practice dose measurement in relation to water quality constituents and commodity-specific parameters is needed. Part of the problem is that the relationships between sanitizer levels and water quality parameters have mainly been described through experimental and correlative approaches or by risk models that are difficult to parameterize accurately. While these results are important, they alone cannot be used to make precise predictions, or for taking real-time corrective measures in dynamic circumstances. Accordingly, there is an urgent need to mathematically describe the fundamental dynamics that generate the observed relationships between sanitizer levels and water quality parameters. Not meeting this need represents a serious problem because without clear scientific foundations informing sanitizer control, the increased demand for fresh produce translates into increased potential for widespread food borne disease outbreaks.
From this perspective, our long-term goal is to develop optimal sanitizer strategies that update in real-time and are easily automated/adjusted to produce type, cut operation and washing practice. Building toward this goal, our main objective for this proposal is to construct data- based modeling tools which quantitatively link easy to measure water quality parameters to commodity specific organic load and free chlorine (FC) levels during recirculated wash conditions. Based on USDA experimental data and our previous modeling results, we hypothesize that a combination of turbidity and total dissolved solids (TDS) can be used to predict organic load and subsequently FC decay kinetics relative to produce/cut type, produce to water ratio and incoming produce rate.
The above objective will be realized via the following two tasks for particular cut types of green cabbage and carrots, respectively.
Task 1: Combining correlative techniques and dynamical systems theory, we will build commodity specific mathematical models that quantitatively link TDS and turbidity to FC consumption kinetics. Task 2: Test and validate model predictions against lab and pilot-plant scale data. This will be accomplished with experimental results from Dr. Yaguang Luo (USDA- ARS) and our own experiments at Cleveland State University. Using sensitivity and uncertainty analysis coupled with this data, we will clearly quantify the range of processing conditions within which our model predictions hold.
In terms of immediate industrial impact, our modeling tools can be used to help minimize FC variability under certain processing conditions. In addition to providing a first step for informing the next generation of online chlorine controllers, our model results will provide guidelines for further experimentation, streamlining the scope and frequency of expensive pilot-plant/commercial scale data acquisition.