Can UV absorbance rapidly estimate the chlorine demand in wash water during fresh-cut produce washing processes?

Authors: VAN HAUTE, SAM - US Department Of Agriculture (USDA); Luo, Yaguang - Sunny; SAMPERS, IMCA - Ghent University; MEI, LEI - University Of Maryland; TENG, ZI - University Of Maryland; Zhou, Bin; BORNHORST, ELLEN - US Department Of Agriculture (USDA); WANG, QIN - University Of Maryland; Millner, Patricia

Submitted to: Postharvest Biology and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/7/2018
Publication Date: 4/10/2018
Citation: Van Haute, S., Luo, Y., Sampers, I., Mei, L., Teng, Z., Zhou, B., Bornhorst, E., Wang, Q., Millner, P.D. 2018. Can UV absorbance rapidly estimate the chlorine demand in wash water during fresh-cut produce washing processes? Postharvest Biology and Technology. 142:19-27. https://doi.org/10.1016/j.postharvbio.2018.02.002.
DOI: https://doi.org/10.1016/j.postharvbio.2018.02.002

Interpretive Summary: Chlorine is used in industrial ready-to-eat fresh-cut produce washing to avoid spread of harmful bacteria. In commercial processing of fresh fruits and vegetables, very large amounts of produce are continuously cut and washed in correspondingly large amounts of chlorinated water. However, the soil, plant debris, and juices from the harvested fruits or vegetables react with the chlorine in the wash water. This reaction reduces the levels of chlorine that are effective in preventing spread of bacteria in the water and results in the need to replenish chlorine during the produce washing process. In the dynamic conditions of commercial produce washing, maintaining a target level of chlorine requires a rapid means of assessing the required amount needed to avoid the spread of harmful bacteria (i.e., chlorine demand of the wash water). A real-time means of determining chlorine demand could help in the development of an improved program for replenishing chlorine particularly when it already has been met partially, as is the case during commercial, continuous produce washing. Using cabbage, carrot, lettuce, and onion as model food products, results showed that ultra-violet light absorbance (UVA) changed relative to the amount of organic material in the water and the remaining chlorine demand. Dual wavelength UVA was necessary to obtain sufficient information about the chlorine demand. For the studied vegetables, the usable UV wavelength ranged from 240 to 290 nm, and the exact wavelengths depended on the type of vegetable. The described UVA method for predicting chlorine demand shows promise for online application and further study should incorporate the possible variability in crop composition as well as other possible interferences with the UVA signal. These results will be of interest to commercial vegetable processing operators who rely on free chlorine use in industrial fresh-cut produce washing.

Technical Abstract: Free chlorine is used in industrial fresh-cut produce washing to avoid cross-contamination of pathogenic and spoilage microorganisms. Knowledge of the chlorine demand (CLD) of the wash water could help to control chlorine-dosing. Previous research has shown that the CLD of non-chlorinated fresh produce wash water (CLDmax) correlates with UV absorbance (UVA) at 254 nm (UVA254). The goal of this study was to estimate CLD when it had been met partially (partially chlorinated water), as is the case during industrial, continuous produce washing. This was done for cabbage, carrot, Greenleaf lettuce and onion. UVA changed with both CLDmax and remaining CLD. At least two wavelengths were necessary to predict the CLD: UVAmin, which changed minimally due to chlorination and had maximum correlation with CLDmax, and UVAmax, which changed maximally with chlorination and had maximum correlation with the fraction of the remaining CLD. Results showed that UVAmin and UVAmax were between 240-290 nm, and the exact wavelength depended on the vegetable. A case study on Greenleaf showed that CLD can be predicted by a model of the form f(UVAmin) x g(UVAmax /UVAmin); optimal predictability of the model using external validation data was when both f and g were expressed as quadratic equations (SD/RMSE =3.55 ; R² = 0.93). The described UVA method for predicting CLD shows promise for online application and further study should incorporate the possible variability in crop composition as well as other possible interferences with the UVA signal.