Purpose: This study was conducted to develop models capable of predicting chlorine demand for various fresh and fresh-cut produce wash water.
Methods: Ten fresh and fresh-cut fruit and vegetable simulated wash waters at different chemical oxygen demand (COD) were prepared. The chlorine demand and wash water quality parameters including pH, ORP, UV254, COD, turbidity, total protein content, total phenolic content and color difference between water and tested samples (ΔE) were measured. The correlations between variables were determined.
Results: The results shows that UV254 was most correlated with chlorine demand of various fresh produce (R=0.77) among all the tested parameters. Further analysis of chlorine demand and UV254 data shows two clusters exist: clusters for produce with high phenolic content and low phenolic content. The phenolic-to-protein/ΔE ratio (PPC) was created to identify which cluster each produce wash water belongs. Empirical models for predicting chlorine demand were developed as chlorine demand = 295.23 UV254 + 6.97, if PPC<0.6; or chlorine demand = 119.77 UV254 + 2.41, if PPC≥0.6. The validation results show that models can predict chlorine demand for the same produce tested at different COD as well as other produce that were not used for model development, with prediction error of 11.3 and 8.16 mg/L, respectively.
Significance: The results demonstrate that the predictive models developed using water quality parameters could be used to estimate the chlorine demand of different produce wash water.