1. S. Van Haute*,**, I. Sampers**, K. Holvoet*,**, and M. Uyttendaele*
Modelling chlorine disinfection for fresh-cut lettuce wash water processes
S. Van Haute*,**, I. Sampers**, K. Holvoet*,**, and M. Uyttendaele*
* Laboratory of Food Microbiology and Food Preservation, Department of Food Safety and Food Quality, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
**Research Group EnBiChem, Department of Industrial Engineering and Technology, University College West Flanders, Association Ghent University (Howest-AUGent), Graaf Karel de Goedelaan 5, B-8500 Kortrijk, Belgium
Introduction
Chlorine is the most used water disinfectant in fresh-cut lettuce washing processes in order to maintain the microbial quality and safety of the lettuce. The commonly applied chlorine concentrations are in the range 50 – 200 mg/L free chlorine, in order to reduce the microbial load on the fresh-cut lettuce. However, these reductions are limited to 1 – 2 log CFU/g. In order to maintain the wash water quality, much lower concentrations are necessary. In this study, a semi-mechanistic model was constructed to predict the E. coli O157 wash water contamination during a dynamic lettuce washing process, in order to comprehend the microbial contamination in function of microbial lettuce contamination, microbial transfer from lettuce to water, amount of lettuce washed and free chlorine residual.
Materials & methods
For experiments in oxidant demand free buffer, 6 log CFU/ mL E. coli O157 was inoculated in phosphate buffer (pH 6.5) containing free chlorine (0.2,0.3,0.4,0.5 mg/L). For the washing experiments, artificial process water (APW) was made by homogenizing tap water and butterhead lettuce. Experiments were executed in tap water, and APW of COD 500 and 1000 mg O2/L. Portions of 50 g of lettuce (4 log CFU/g E. coli O157) were consecutively passed for 1 min through the same washing bath (4 L, mechanical agitation), in which a residual of 1 mg/L free chlorine was maintained during the 1 hour trials by measuring the free chlorine residual (DPD method) and changing the flow of a chlorine dosing pump accordingly. After washing, each portion of lettuce was rinsed with tap water. Also, trihalomethanes in the water and on the lettuce were measured at the end of the trials with GC-MS. Figure 1 shows the method used for constructing the model. .The model was based on the following assumptions: i) knowledge of the residual free chlorine in the washing bath can be used to estimate the microbial kill-off regardless the physicochemical load, ii) the discrete experimental setup is interpreted as a continuous process where lettuce is continuously added instead of in intervals of 1 min.
Free chlorine (mg/L) r² Um Ur Ud
TIC
0.0±0.0
0.58
0.02
0.05
0.93
0.028
1.17±0.26 (in tap water)
0.00
0.29
0.71
0.077
Figure 2: Prediction quality of the E. coli O157 contamination models in tap water
A considerable part of the microbial measurements in APW 500 and 1000 mg O2/L yielded measurements below the LOQ (1.6 log CFU/100 mL). The higher added chlorine dosage in combination with a more rapid chlorine decomposition due to organic matter caused a greater variation in free chlorine residual (Table 1). These larger chlorine dosages could equally well inactivate E. coli O157 as being consumed by the COD of the water. These two factors could explain both the greater variation as well as the overall lower contamination in water with APW 500 and 1000 mg O2/L than in tap water. After 1 hour of lettuce washing in APW 1000 mg O2/L, total trihalomethanes were above the limit imposed by EU drinking water legislation (max 100 µg/L), although none were found on the lettuce after rinsing.
Figure 1: Method for construction of the semi-mechanistic model
The origin of the model deviation from the measured values was assessed with Theil’s decomposition of the mean square error: Um = bias; Ur = deviation regression line from perfect fit line; Ud = random error; Um+Ur+Ud=1. and the overall quality with Theil’s inequality coefficient (TIC).
TIC= i ( y i − y m,i )² i y² i i y² i,m
Table 1. Measured chlorine consumption and total trihalomethanes production
Tap water
COD 500 mg O2/L
COD 1000 mg O2/L
COD (mg O2/L)
36 ± 13
500 ± 25
1017 ± 4
Free chlorine residual (mg/L)
1.17 ± 0.26
1.16 ± 0.33
1.09 ± 0.39
Free chlorine dose (mg/L/min)
0.3 ± 0.02
2.6 ± 0.2
6.6 ± 1.2
Chlorination breakpoint (mg/L)
1.9 ± 0.2
81.0 ± 14.4
244.5 ± 19.1
Cumulative dose (mg/L)
17.1 ± 2.1
235.8 ± 23.6
609.0 ± 59.4
E. coli O157 wash water contamination (log CFU/100mL)
2.2 ± 0.3
1.2 ± 0.7
1.2 ± 0.8
Total trihalomethanes (water) (µg/L)
<6.3
27.8 ± 5.4
124.5 ± 13.4
Chloroform
111.1 ± 17.3
Bromodichloromethane
13.4 ± 2.9
Total trihalomethanes (lettuce) (µg/g)
Results & Discussion
In the absence of free chlorine, the model predicted the microbial build-up accurately, while in the presence of free chlorine some bias was observed (Um =0.29) (Figure 2). Nonetheless, a good overall prediction quality was found (Theil’s inequality coefficient (TIC) = 0.077).
Conclusions
The greater variation in E. coli O157 contamination in COD 500 and 1000 mg O2/L compared to tap water, as well as the better inactivation at higher COD, shows that the assumption that knowledge of the chlorine residual is insufficient for predicting inactivation in varying COD. Improvements might be made by incorporating added dose and decomposition rate in function of COD to supplement free chlorine residual. This study shows that low chlorine residuals (although higher than the 1 ppm used here to allow quantitative modelling) in the wash water may be considered safe towards the consumer and effective for E. coli O157 inactivation.
For more information: Van Haute, S., Sampers, I., Holvoet, K., Uyttendaele M. (2013). Physicochemical Quality and Chemical Safety of Chlorine as a Reconditioning Agent and Wash Water Disinfectant for Fresh-Cut Lettuce Washing, Applied and Environmental technology, 79,9.
The research leading to these results has been facilitated by the European Community's Seventh Framework Program (FP7) under grant agreement no (project VEG-i-TRADE). :