Hybrid Extended Kalman Filtering and Noise Statistics Optimization for Produce Wash State Estimation

Vahid Azimi, Daniel Munther, Seyed Abolfazl Fakoorian, Thang Tien Nguyen, Dan Simon

Food-borne diseases from fresh produce consistently cause serious public health issues. Although sanitization is used to enhance the safety of fresh produce, pathogen cross-contamination via process water continues to be associated with major disease outbreaks. Because of the complex and time-varying nature of the produce wash process, its effectiveness is limited. There is thus an urgent need for new approaches in produce washing to reduce the probability of outbreaks. As an important step in this direction, we design a hybrid extended Kalman filter (HEKF) and a particle swarm optimization (PSO)-based noise statistics optimization algorithm for a produce wash system. The HEKF uses discrete-time free chlorine (FC) measurements, and we use PSO to optimize the process noise model. We apply the HEKF to estimate the chemical oxygen demand, FC concentration, E. coli concentration in the wash water, and E. coli level on the lettuce.

The MATLAB software that was used to derive the results in the paper can be downloaded in this zip file (start with the "README.TXT" file).

 

Reference

 

V. Azimi, D. Munther, S. A. Fakoorian, T. T. Nguyen, and D. Simon, "Hybrid Extended Kalman Filtering and Noise Statistics Optimization for Produce Wash State Estimation," Journal of Food Engineering, submitted for publication, October 2016 - pdf, 942 KB

 


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Department of Electrical Engineering and Computer Science

 

Cleveland State University

 


Last Revised: October 3, 2016