명태 유통 중 Pseudomonas spp. 농도의 예측 모델링과 민감도 분석
Dynamic Modeling and Sensitivity Analysis for Predicting the Pseudomonas spp. Concentration in Alaska Pollack along the Distribution Path
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초록
Dynamic modeling was used to predict the Pseudomonas spp. concentration in Alaska pollack under dynamic temperature conditions in a programmable incubator using Euler’s method. The model evaluation showed good agreement between the predicted and measured concentrations of Pseudomonas spp. In the simulation, three kinds of distribution path were assumed: consumers buying from a distribution center (A), manufacturer (B), or direct market (C). Each of these distribution paths consists of six phases: shipping, warehousing/ shipment, warehousing/storing, processing, market exhibition, and sale/consumption. Sensitivity analysis of each phase was also implemented. The Pseudomonas concentrations and sensitivities (Sk) at the terminal phases of the three paths were estimated to be (A) 11.174 log CFU/g and 10.550 log Sk, (B) 10.948 log CFU/g and 10.738 log Sk, and (C) 8.758 log CFU/g and 9.602 log Sk, respectively. The sensitivities indicated that path A has the highest risk of failure in managing the relevant phases.
Abstract
Dynamic modeling was used to predict the Pseudomonas spp. concentration in Alaska pollack under dynamic temperature conditions in a programmable incubator using Euler’s method. The model evaluation showed good agreement between the predicted and measured concentrations of Pseudomonas spp. In the simulation, three kinds of distribution path were assumed: consumers buying from a distribution center (A), manufacturer (B), or direct market (C). Each of these distribution paths consists of six phases: shipping, warehousing/ shipment, warehousing/storing, processing, market exhibition, and sale/consumption. Sensitivity analysis of each phase was also implemented. The Pseudomonas concentrations and sensitivities (Sk) at the terminal phases of the three paths were estimated to be (A) 11.174 log CFU/g and 10.550 log Sk, (B) 10.948 log CFU/g and 10.738 log Sk, and (C) 8.758 log CFU/g and 9.602 log Sk, respectively. The sensitivities indicated that path A has the highest risk of failure in managing the relevant phases.
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