Hybrid extended Kalman filtering and noise statistics optimization for produce wash state estimation

Abstract

Food-borne diseases associated with fresh produce consistently cause serious public health issues. Although sanitization measures are utilized to enhance the safety of fresh produce, strategies that neglect the dynamic nature of commercial wash processes are limited, creating the potential for pathogen cross-contamination and major disease outbreaks. In light of this risk, there is an urgent need for new control approaches during produce washing to reduce the probability of outbreaks. As an important step in this direction, a hybrid extended Kalman filter (HEKF) and particle swarm optimization (PSO)-based noise statistics optimization are designed for a produce wash system. The HEKF uses discrete-time free chlorine (FC) measurements, and PSO is used to optimize the noise statistics of the process noise model. The process model and HEKF enable the estimation of chemical oxygen demand (COD) in the water wash, FC concentration, Escherichia coli concentration (PC) in the water wash, and E. coli level (P) on the lettuce. Although control is not explicitly addressed in this paper, the estimation technique proposed here will enable not only monitoring but also advanced control methods. The HEKF is applied to estimate E. coli O157:H7 contamination of shredded lettuce during an industrial wash. The HEKF estimates COD with a root mean square error (RSME) of 8.24 mg/L, FC concentration with an RMSE of 0.09 mg/L, PC in the wash water with an RMSE of 0.19 MPN/ml, and P on the lettuce with an RSME of 0.04 MPN/g. A sensitivity analysis demonstrates that the estimator has good robustness.