International Journal of Food Microbiology 204 (2015) 75–80

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International Journal of Food Microbiology journal homepage: www.elsevier.com/locate/ijfoodmicro

Effect of food processing organic matter on photocatalytic bactericidal activity of titanium dioxide (TiO2) Veerachandra K. Yemmireddy, Yen-Con Hung ⁎ Department of Food Science and Technology, University of Georgia, 1109 Experiment Street, Griffin, GA 30223-1797, USA

a r t i c l e

i n f o

Article history: Received 28 January 2015 Received in revised form 16 March 2015 Accepted 19 March 2015 Available online 27 March 2015 Keywords: TiO2 Bactericidal activity Organic matter Kinetics E. coli O157:H7

a b s t r a c t The purpose of this study was to determine the effect of food processing organic matter on photocatalytic bactericidal activity of titanium dioxide (TiO2) nanoparticles (NPs). Produce and meat processing wash solutions were prepared using romaine lettuce and ground beef samples. Physico-chemical properties such as pH, turbidity, chemical oxygen demand (COD), total phenolics (for produce) and protein (for meat) content of the extracts were determined using standard procedures. The photocatalytic bactericidal activity of TiO2 (1 mg/mL) in suspension with or without organic matter against Escherichia coli O157:H7 (5-strain) was determined over a period of 3 h. Increasing the concentration of organic matter (either produce or meat) from 0% to 100% resulted in 85% decrease in TiO2 microbicidal efficacy. 'Turbidity, total phenolics, and protein contents in wash solutions had significant effect on the log reduction. Increasing the total phenolics content in produce washes from 20 to 114 mg/L decreased the log reduction from 2.7 to 0.38 CFU/mL, whereas increasing the protein content in meat washes from 0.12 to 1.61 mg/L decreased the log reduction from and 5.74 to 0.87 CFU/mL. Also, a linear correlation was observed between COD and total phenolics as well as COD and protein contents. While classical disinfection kinetic models failed to predict, an empirical equation in the form of “Y = menX” (where Y is log reduction, X is COD, and m and n are reaction rate constants) predicted the disinfection kinetics of TiO2 in the presence of organic matter (R2 = 94.4). This study successfully identified an empirical model with COD as a predictor variable to predict the bactericidal efficacy of TiO2 when used in food processing environment. © 2015 Elsevier B.V. All rights reserved.

1. Introduction More than two thirds of all fresh water abstraction worldwide goes toward food production (Kirby et al., 2003). From the primary production of food to subsequent processing requires copious amounts of water. One challenge for the food industry is to minimize water consumption and waste water discharge rates (Olmez and Kretzschmar, 2009). Current trends toward sustainable production practices necessitate food industry to reuse the water after proper treatment. However, it should be noted that water serves as a source of cross-contamination as reusing processing water may result in the buildup of microbial loads, including undesirable pathogens from the crop (Gil et al., 2009). Several recent outbreaks related to foods can be traced back to contaminated process wash water and irrigation water with pathogens. This shows inadequacy of existing physical and chemical disinfection technologies. Among several water disinfection technologies, chlorination is the most extensively used for the last three decades (Pigeot-Remy et al., 2012). However, studies show that in many cases chlorinated water is

⁎ Corresponding author. Tel.: +1 770 412 4739; fax: +1 770 412-4748. E-mail addresses: [email protected] (V.K. Yemmireddy), [email protected] (Y.-C. Hung).

http://dx.doi.org/10.1016/j.ijfoodmicro.2015.03.019 0168-1605/© 2015 Elsevier B.V. All rights reserved.

not fully effective in reducing pathogens (Zhang et al., 2009) and has potential to generate harmful chlorinated disinfection by-products like trihalomethanes, haloacetic acids, haloketones, and chloropicrin in presence of organic matter (Gil et al., 2009; López-Gálvez et al., 2010). Moreover, pathogens such as viruses, protozoa, or helminthes are generally more resistant to chlorine than bacteria by varying degrees (Kirby et al., 2003). Other commonly used treatments such as ozonation and filtration also have certain inherent limitations. In this context, advanced oxidation processes (AOPs) involving photocatalytic nanoparticles (NPs) are gaining popularity as a viable alternative to existing disinfection technologies. Among various photocatalysts, titanium dioxide (TiO2) has been extensively studied in the last 25 years for its photocatalytic disinfection properties (Hitkova et al., 2012). TiO2 photocatalysts generate strong reactive oxygen species (ROS) such as the hydroxyl radical (·OH), superoxide radical (O.-2 ), and hydrogen peroxide (H2 O2) when illuminated with UV light with a wavelength of less than 385 nm. The photogenerated ROS has proven to exhibit excellent microbicidal activity and is responsible for mineralization of organic compounds. TiO2 is non-toxic and has been approved by the American Food and Drug Administration (FDA) for use in human food, drugs, cosmetics, and food contact materials (Chawengkijwanich and Hayata, 2008). Bactericidal and fungicidal effects of TiO2 on Escherichia coli, Salmonella choleraesuis,

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Vibrio parahaemolyticus, Listeria monocytogenes, Pseudomonas aeruginosa, Staphylococcus aureus, Diaporthe actinidiae, and Pencillium expansum have been discussed by Foster et al. (2011). Among several commercial and synthesized TiO2 NPs, Degussa P-25 is considered as a standard for determining photocatalytic activity (Mills and Le Hunte, 1997). Several studies in the past have explained the disinfection mechanism of TiO2 (Foster et al., 2011) and explored the effect of nanoparticle size, concentration, UV light intensity, pH, bacterial cell concentration, inorganic salts, and model organic matter on the disinfection properties of TiO2 (Rincon and Pulgarian, 2004). However, the effect of food processing organic matter on the bactericidal activity of TiO2 NPs is not well reported. In general, the majority of the research studies concerning the evaluation of sanitizing agents on the reduction of pathogenic microorganisms during washing do not take into account the presence of organic matter (Stopforth et al., 2008). When potable water is used to evaluate different sanitizing agents, it might lead to unrealistic results with no practical application (Gil et al., 2009). Meat and produce wash operations in food processing industries release abundant phenolic, protein, and lipid rich organic matter along with several viable or nonviable pathogenic and spoilage microorganisms. Any study exploring the optimum conditions for inactivation of pathogens and the effect of organic matter on photocatalytic disinfection properties of UV activated TiO2 would help to apply these novel technologies in still unexplored sectors like food processing waste water treatment. Also, identifying the disinfection mechanism in suspension consisting organic matter would help to develop effective strategies while coating these NPs on abiotic surfaces and packaging materials. Hence, the overall objective of this study was to determine the effect of organic matter on bactericidal activity of TiO2 NPs. Specific objectives include the following: i) to determine the bactericidal efficacy of TiO2 in wash water rich in phenolic and protein contents ii) to identify the factors those are most useful to predict the disinfection potential of TiO2 NPs in real food processing environments

2.2. Analysis of wash water properties The physico-chemical properties such as pH, turbidity, COD, total phenolics (for produce), and protein (for meat) contents of the wash solutions were determined. The pH of the samples was determined using a pH meter (Model # AR50, Fischer Scientific, Pittsburgh, PA, USA). The turbidity was measured using a turbidity meter (Model # 19952, HF Scientific, Fort Myers, FL, USA) and expressed as nephelometric turbidity units (NTU). The COD was determined by following reactor digestion method (Jirka and Carter, 1975). Briefly, 1 mL of an appropriate dilution of the sample was added to the COD reagent vial (P/N# TT20711, Orbeco, Sarasota, FL, USA), and the contents were mixed thoroughly. The samples were digested for 2 h on a heating block preheated to 150 °C. The digested samples in the vials were cooled down to room temperature, and the COD values were read on a colorimeter (Model # DR/890, HACH®, Loveland, CO, USA) and expressed as mg/L. Total phenolic content of produce wash solution was determined using the Folin-Ciocalteu assay as outlined by Singleton and Rossi (1965). One milliliter of sample was added to 70 mL deionized water in a 125 mL screw cap bottle then 5 mL Folin-Ciocalteu's phenol reagent (Sigma-Aldrich Co., St Louis, MO, USA) was added to the solution. After thorough mixing, 15 mL of 20% (w/v) sodium carbonate solution was added followed by enough water to bring the total volume to 100 mL. The mixtures were sealed and incubated for at least 2 h at room temperature. The samples were then read at 750 nm in a 1 cm quartz cuvette using a DU 520 UV/Vis spectrophotometer (Beckman Coulter Inc., Brea, CA, USA). The total phenolic content of a test sample was calculated using catechol as a standard and reported as mg/L. Total protein content of meat wash solution was determined using the Bradford assay (Bradford, 1976). Briefly, 0.1 mL of sample was mixed with 5 mL Bradford's reagent (Sigma-Aldrich Co., St Louis, MO, USA). The samples were then read at 595 nm in a 1 cm quartz cuvette using a DU 520 UV/Vis spectrophotometer mentioned earlier. The total protein content of a test sample was calculated using bovine serum albumen as a standard and reported as mg/L. 2.3. Bacterial strains and inoculum preparation

2. Materials and methods 2.1. Preparation of wash water containing organic matter Wash waters rich in organic matter representing produce and meat processing operations were used in this study. Romaine lettuce was purchased from a local supermarket (Griffin, GA, USA) and stored at 4 °C until use. Any wilted and damaged outer leaves of the lettuce were removed and discarded, while internal leaves were cut into about 2.5 cm2 pieces using clean and sterile scissors. Subsequently, 50 g of lettuce were placed into stomacher bags (Whirl Pak®) containing 200 mL sterile deionized water, and the mixture was homogenized for 2 min in a stomacher (Seward Stomacher®, 80 biomaster, Worthing, UK). Ground beef samples were prepared by separating lean and visible fat portions from primal cuts of beef chuck (ExcelTM, Cargill Meat Solutions Corporation, Wichita, KS, USA). The separated lean meat portions were ground in a meat grinder (LEMTM, Size #8, West Chester, OH, USA) to obtain a near 100% lean ground beef samples. Later, a 10 g sample of ground beef at different lean to fat weight ratios (100:0, 80:20, 60:40, 40:60, 20:80, and 0:100) were weighed into a stomacher bag containing 200 mL sterile deionized water and homogenized as described earlier. The resultant extracts were filtered through a sterile Whatman® filter paper (No. 2, 185 mm diameter, 8 μm pore size) and further diluted by a 1:2, 1:3, and 1:4 factor of lettuce or beef extract to deionized water in order to provide different levels of the organic load. These solution were referred as produce (lettuce extracts), and meat (beef extracts) wash solutions and kept at 4 °C in darkness prior to use.

Five strains of E. coli O157: H7 isolated from different sources: E009 (beef), EO932 (cattle), O157-1 (beef), O157-4 (human), and O157-5 (human) were used in this study. All bacterial strains were stored at -70 °C in tryptic soy broth (TSB) (Difco, Becton Dickinson, Sparks, MD, USA) containing 20% glycerol. Prior to the experiment, cultures were activated at least twice by growing them overnight in 10 ml of TSB at 37 °C. Later, each bacterial stain was cultured separately in 10 ml of TSB and kept on a shaking incubator at 230 rpm and 37 °C for 16 h. Following the incubation, bacterial cells were harvested by sedimentation at 4000 ×g for 12 min and re-suspended in a sterile phosphatebuffered saline (PBS, pH 7.2), and equal volumes of each strain suspension were combined to obtain 10 mL of a five strain cocktail containing approximately 108 CFU/mL. Cell concentration was adjusted by measuring the absorbance of bacterial suspension at 600 nm using a UV/Vis spectrophotometer and confirmed by plating 100 μL portions of the appropriate serial dilution on tryptic soy agar (TSA) (Difco Laboratories) plates incubated at 37 °C for 24 h. 2.4. Photocatalytic disinfection experiments Based on our previous study (Yemmireddy and Hung, 2015), most efficient TiO2 NPs (Aeroxide® P25, Sigma-Aldrich, St. Louis, MO, USA) with a surface area of 50 m2 g-1 and a particle size of ~ 21 nm (as per supplier specifications) were used in this study. Suspensions of TiO2 (1 mg/mL) in produce and meat wash solutions were prepared by sonication in water-bath (Model # FS30, Fisher Scientific, Waltham, MA, USA) for about 1 h at 23 °C. Photocatalytic disinfection experiments were carried out by adding 2 mL bacterial culture in 18 mL NP

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suspension at 2 mW/cm2 UVA light intensity by following method of Yemmireddy and Hung (2015). Briefly, the procedure involves, 20 mL bacteria-NP suspension, which was added into a sterile glass Petri dish (90 × 18 mm2; diameter × depth) mounted on a magnetic stirrer (Model# H1190M, Hanna Instruments, Smithfield, RI, USA) and illuminated with a UVA light system fitted with four 40 W lamps (American DJ®, Model UV Panel HPTM, LL-UV P40, Los Angeles, CA, USA) from the top under continuous stirring. The intensity of the light was measured using UV radiometer (Peak sensitivity 365 nm, UVP®, Upland, CA, USA). A control sample of bacterial culture suspended in wash water without photocatalyst under UVA light was also included. All the experiments were conducted at room temperature using indoor air as oxidant. A 1 mL sample was withdrawn from the treatment solution at every 1 h for 3 h and added into 9 mL sterile PBS. Appropriate serial dilutions of the samples were prepared, and the surviving bacteria from the control and treatments were enumerated on Sorbitol Macconkey Agar (SMAC). The plates were incubated at 37 °C for 24 h, and the colonies were counted and recorded as log CFU per mL. All the experiments with produce wash were replicated five times and meat wash were duplicated. 2.5. Kinetic models The kinetics of photocatalytic bacterial inactivation is usually described using empirical equations. The following five well-known disinfection kinetic models were considered in order to find a best-fit model for the experimental results involving photocatalytic bactericidal activity of TiO2 in the presence of organic matter: The Chick–Watson model (Chick, 1908; Watson, 1908) with a constant concentration of photocatalyst: log ðN=N0 Þ ¼ −kt

ð1Þ

where N/N0 is the reduction in bacterial concentration, k is the kinetic constant of inactivation, and t is the treatment time. The delayed Chick–Watson model (Cho et al., 2004) to accommodate if there exists any initial lag time (t0) in the disinfection is computed as follows:  logðC=C 0 Þ ¼

0 −kðt−t 0 Þ

for t ≤ t 0 for t Nt 0

ð2Þ

The modified Chick–Watson model (Cho et al., 2003) to accommodate either the existence of a shoulder at the beginning of the reaction or a tail at the end of the reaction: log ðC=C 0 Þ ¼ k1 ½1− expð−k2 t Þ

F¼

mean regression sum of squares mean squared error

77

ð7Þ

3. Results and discussion 3.1. Kinetics of TiO2 disinfection and the effect of organic matter Fig. 1 shows the results of the photocatalytic disinfection of E. coli O157:H7 using TiO2 aqueous suspensions with different levels of organic matter from meat and produce extract solutions. TiO2 in suspension without organic matter has showed a reduction of around 5.78 log CFU/mL after 3 h treatment. While, TiO2 suspended in meat and produce organic matter extracts at 25% level of incorporation in the reaction mixture showed a reduction of only 3.7 and 2 log CFU/mL, respectively. Further increasing the organic matter concentration to 100% in the reaction mixture significantly reduced the disinfection potential of TiO2 to below 1 log CFU/mL. This shows that increasing the organic matter content in the reaction mixture has detrimental effect on the TiO2 bactericidal activity. This can be explained based on the premise that the process of decomposing organic matters and photo-killing of microbes is perceived to follow the similar mechanisms of the attack by ROS (Chen et al., 2009). However, the organic matter present in the reaction mixture competes with bacteria for hydroxyl radical (OH·), which is a major ROS responsible for the killing of bacteria and also hinders the interaction between the bacteria and the TiO2 catalyst (Grieken et al., 2010). The same phenomena might be the reason for decreased bactericidal activity of TiO2 in the current study. However, the effect of specific components of organic matter in the meat and produce extract solutions on the photocatalytic disinfection efficacy of TiO2 need to be further investigated. The photocatalytic disinfection kinetics of TiO2 with or without organic matter has followed a non-linear trend with a shoulder (Fig. 1). The experimental data were fitted with most commonly used empirical models that are described earlier. When the reaction mixture is free from any organic matter, almost all the tested empirical models were able to fit the experimental data well with an R2 value greater than 0.94 (Table 1). When considering both R2 and F-statistic values to predict the goodness of fit, only the modified Chick–Watson and the modified Homs model were able to give the best fit with an R2 value of 0.98 and F-value of 274.81. However, due to lack of a tail region at the end of photocatalytic disinfection treatment, modified Homs model is insignificant to fit the data obtained from the current study. Hence, the modified Chick–Watson model to accommodate initial lag or shoulder

ð3Þ

The Homs model (Hom, 1972) when the inactivation rate deviates log-linear behavior is calculated as follows: log ðC=C 0 Þ ¼ −k t

h

ð4Þ

where h is the second parameter. If h = 1, Homs model becomes a Chick–Watson linear equation, h N 1 for existence of a shoulder, h b 1 for existence of a tail. The modified Homs model (Cho et al., 2003) to accommodate shoulder, log-linear, and tail regions is calculated as follows: log ðC=C 0 Þ ¼ k1 ½1− expð−k2 t Þ

k3

ð5Þ

To determine which model best described the data, the estimated coefficient of determination (R2) and the F-value were calculated using the following equations: 2

R ¼ 1−

residual sum of squares uncorrected total sum of squares

ð6Þ

Fig. 1. Effect of different levels of organic matter from produce and meat extract solutions on the log reduction of E. coli O157:H7 by TiO2 photocatalysis.

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Table 1 Comparison of kinetic models to predict the TiO2 disinfection efficacy with or without organic matter. Type

Model

R2

F-statistic

TiO2 without organic matter TiO2 without organic matter TiO2 without organic matter TiO2 without organic matter TiO2 without organic matter TiO2 with 25% produce extract TiO2 with 25% meat extract

Chick–Watson Delayed Chick–Watson Hom's model Modified Hom's model Modified Chick–Watson Modified Chick–Watson Modified Chick–Watson

0.947 0.948 0.985 0.982 0.982 0.933 0.993

194.98 200.56 233.28 274.81 274.81 125.69 417.94

effect was found to be the most appropriate model to predict the disinfection kinetics of TiO2. With the incorporation of organic matter like produce or meat extract in the reaction mixture, none of the tested empirical models were able to fit the TiO2 disinfection data well. Only the modified Chick–Watson model was able to predict the disinfection trend of TiO2 for up to 25% level of organic matter from both meat (R2 = 0.993) and produce (R2 = 0.933) extracts (Table 1). However, the modified Chick–Watson model failed to predict the TiO2 disinfection kinetics when organic matter concentration was more than 25%. This further supports the hypothesis that effect of individual components of organic matter need to be accounted to better predict the disinfection kinetics of TiO2 in the presence of organic matter.

3.2. Effect of pH The effect of pH of produce and meat extract solutions on the disinfection potential of TiO2 was shown in Table 2. Decreasing the organic load in wash solutions from 100% to 25% increased the log reduction of TiO2 from 0.54 to 2.07 for produce and 0.87 to 3.7 for meat extract solutions, respectively. Although, the pH values of produce and meat extract solutions were significantly different from each other, they are not different within the same type of extract solutions at different levels of organic load. In both types of extracts, even at same level of pH, the log reductions are significantly different from each other. For example, produce extract solution at concentration of organic load 50% and 75% with pH 6.2 showed significantly different reductions of 1.4 and 0.74 log CFU/mL, respectively. This implies that under tested conditions, pH of the solutions containing organic matter alone does not have an effect on the disinfection potential of TiO2. Gumy et al. (2006) reported that the electrostatic attraction between the E. coli and the Degussa P-25 TiO2 is not a controlling factor in the pH range of 4.5 to 6.0 since E. coli is negatively charged between pH 3 and 9 and TiO2 is positively charged up to pH 7. In another study by Rincon and Pulgarian (2004), modification of pH of TiO2 suspension did not show any effect on the E. coli inactivation rate in the pH range of 4 and 9. Similarly, the pH range (5 to 6.27) of produce and meat extract solutions used in the current study may not have an effect on the photocatalytic disinfection efficacy of TiO2.

3.3. Effect of turbidity The effect of turbidity of produce and meat extract solutions on the disinfection potential of TiO2 was shown in Fig. 2. Increasing the turbidity of produce extract from 36 to 148 NTU decreased the log reduction from 2.08 to 0.54 CFU/mL. Similarly, increasing the turbidity of meat extract solutions from 17 to 50 NTU decreased the log reduction from 3.7 to 0.38 CFU/mL. This shows that increasing the turbidity of wash solutions decreased the bactericidal efficacy of TiO2. Turbidity caused by the presence of components leaching from tissues of the cut produce surface and meat, is a measure of the waters ability to scatter and absorb light, which depends on a number of factors such as size, number, shape, and refractive index of the particles and the wave length of incident light (WHO, 1996). The photogenerated ROS, such as hydroxyl radical are highly active for both the oxidation of organic substances and the inactivation of bacteria (Kim et al., 2003). Both the bacteria and the organic matter present in the suspension compete for the ROS generated through photocatalytic disinfection process. This condition reduces the disinfection potential of TiO2 to inactivate bacteria. In addition, increasing the turbidity of the reaction mixture decreases the penetration power of UVA light into the solution and limits the ability of TiO2 NPs to generate ROS. Selma et al. (2008) studied the turbidity effect of various fresh-cut vegetable wash waters on the disinfection potential of TiO2. Their study reported that differences in water turbidity were associated with different bacterial inactivation rate. Onion wash water with highest turbidity (5040 NTU) has least bacterial inactivation rate and carrot wash water with lowest turbidity (0.6 NTU) has highest inactivation rate. However, lettuce (87.4 NTU), escarole (95.7 NTU), chicory (42.4 NTU), and spinach (88.9 NTU) wash waters with intermediate level of turbidity have showed lower bacterial inactivation. In our study, upon gradual decrease in the lean to fat ratio of meat extract solution from 100:0 to 0:100 resulted in almost 18% to 40% decrease in the turbidity (results not shown). However, no significant increase in the bactericidal activity of TiO2 was observed. This clearly shows that turbidity itself is not a rate limiting factor and the presence of other components of the organic matter such as fat content may affect the bactericidal efficacy of TiO2. The presence of components such as protein and fat in the reaction mixture might have blocked the surface active sites on TiO2 NPs to generate ROS and reduced the efficiency of photocatalytic disinfection process. This implies that the efficacy of the photocatalytic system will be highly depend on the physicochemical characteristics of the suspension containing organic matter and increasing the turbidity of the suspension reduced the photocatalytic inactivation rate of bacteria.

Table 2 Effect of pH of wash solution containing organic matter on the bactericidal activity of TiO2. Type Produce

Meat

Organic load (vol%) 100 75 50 25 100 75 50 25

pHa

Reductiona at 3 h (log CFU/mL) A

6.11 6.20A 6.20A 6.27A 5.30B 5.04B 5.08B 5.07B

0.54E 0.74E 1.40CD 2.07B 0.87DE 1.04CDE 1.60CB 3.70A

a Mean values with the same superscript within the same column are not significantly different (p N 0.05).

Fig. 2. Effect of turbidity of produce and meat extract solutions on the log reduction of E. coli O157:H7 by TiO2 photocatalysis for 3 h.

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3.4. Effect of total phenolics and its correlation with COD Fig. 3 presents the effect of total phenolics content in the produce extract on photocatalytic bactericidal activity of TiO2. Total phenolics content in the suspension showed significant effect on the bacterial inactivation. For example, increasing the total phenolics content in the suspension from 20.4 to 113.6 mg/L decreased the log reduction from 2.7 to 0.38 CFU/mL. The reduction trend can be best fitted with an exponential equation in the form of Y = A eBX (where Y is the log reduction in CFU/mL, X is the total phenolics in mg/L, and A and B are constants) with an R2 value of 0.943. Also, a linear correlation was observed between total phenolics and COD of the produce extract solution (Fig. 3). It followed a regression trend of Y = 40.22X − 220.77 (where Y = COD in mg/L, X = Total phenolics in mg/L) with a correlation coefficient 0.928. One possible reason for the decreased photocatalytic activity of TiO2 can be attributed to the increased concentration of phenolic compounds in the suspension. Phenolic compounds such as tocopherols, flavonoids, and phenolic acids are well known for their antioxidant activity. In general, these compounds inhibit or delay the oxidation of other molecules by inhibiting the initiation or propagation of oxidizing chain reactions. TiO2 photocatalysis, which involves series of oxidation and reduction reactions, is highly dependent on the generation of ROS. The phenolic compounds present in the produce extract might have quenched the generated ROS by irradiated TiO2 NPs, which in turn reduced its efficacy to inactivate bacteria. Rincon and Pulgarian (2004) reported a significant decrease in the TiO2 photocatalytic inactivation of E. coli in the presence of organic compounds such as dihydroxybenzenes, hydroquinone, catechol, and resorcinol. They reported that the formation of an optical screen on TiO2 surface by organic and inorganic components for light penetration as well as competition of organic compounds for OH radicals are some reasons for decreased photocatalytic efficacy. Similar phenomena can be attributed to the decreased bactericidal activity of TiO2 in the presence of phenolic-rich organic matter used in the current study. 3.5. Effect of protein and its correlation with COD The effect of protein content in meat extract on the log reduction was shown in Fig. 4. As expected, increasing the protein content from 0.12 to 1.61 mg/L in the reaction mixture decreased the log reduction from 5.74 to 0.84 CFU/mL. The reduction trend can be represented with an exponential equation in the form of Y = A eBX (where Y is the log reduction in CFU/mL, X is the protein content in mg/L, and A and B are constants) with R2 value of 0.904. Like phenolics, a linear correlation between protein and COD of the meat extract was noticed with an R2 value of 0.725 (Fig. 4). Variable proportions of lean to fat ratios (100:0 to 0:100), and the relative complexity of meat extract might be one

Fig. 3. Correlation between total phenolics and COD of produce extract as well as total phenolics and log reduction of E. coli O157:H7 by TiO2 photocatalysis.

Fig. 4. Correlation between protein content and COD of meat extract as well as protein and log reduction of E. coli O157:H7 by TiO2 photocatalysis.

possible reason for the distorted trend and poor correlation of the protein with log reduction and COD. In general, TiO2 NPs tend to agglomerate in aqueous solutions in the absence of agitation. The presence of organic matter rich in protein further enhances the formation of agglomerated NPs in suspension irrespective of agitation. In addition, it is possible that the fat molecules present in the meat extract forms an outer layer on the surface of TiO2 NPs, which results in blockage of surface active sites for the photocatalytic reaction to takes place and subsequent generation of ROS. Gumy et al. (2006) reported that out of several surface properties, the aggregate size of several commercial NPs in suspension played an important role during the interfacial charge transfer between TiO2 and E. coli leading to bacterial abatement. Agglomerated condition reduces the effective surface area of NP available for bacteria to come in contact with while stirring the suspension during photocatalytic disinfection. The same might be the reason for the decreased bacterial inactivation rate of TiO2 in the presence of organic matter rich in protein. However, further studies need to be conducted to understand the effect of individual components on photocatalytic disinfection mechanism of TiO2 in complex food systems such as meat extract. 3.6. Effect of COD Although total phenolics and protein contents are reasonably good in predicting the bactericidal efficacy of TiO2 in the presence of organic matter, using a common factor such as COD might be practically more beneficial. As discussed before, the COD of produce and meat extract

Fig. 5. Relationship between COD of produce and meat organic matter extracts and the log reduction of E. coli O157:H7 by TiO2 photocatalysis .

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References

Table 3 Comparison of fitted isotherm parameters of empirical model. Type of organic matter

m

n

R2

F-statistic

Produce Meat Combined

3.5181 7.7101 6.7116

−0.00065 −0.00157 −0.00134

0.972 0.960 0.944

315.37 266.64 353.92

Model equation Y = menX, where Y = log reduction (CFU/mL), X = COD (mg/L), and m and n are reaction rate constants.

solutions had a linear correlation with phenolics (R2 = 0.92) and protein contents (R2 = 0.72), respectively. Hence, COD can be used as a predictor variable to determine the kinetics of TiO2 bacterial inactivation in the presence of organic matter. Fig. 5 shows the correlation between the COD of the produce or meat extract solutions and the log reduction of E. coli O157:H7. Increasing the COD values of both meat and produce extracts decreased the log reduction. Experimental data from both meat and produce extract solutions were best fitted with an empirical model in the form of Y = menX (where Y is the log reduction, X is the COD of organic matter, and m and n are reaction rate constants) (Table 3). A study conducted by Selma et al. (2008) on different types of produce wash waters reported that onion wash water with highest COD was associated with the least bacterial reduction after treatment with the photocatalytic system. According to these results, it appears that the inactivation data can be better correlated with the COD of organic matter in the suspension. TiO2 photocatalytic action was attributed to the promotion of peroxidation of phospholipid components of the lipid membrane, inducing cell membrane disorder, subsequent loss of essential functions such as respiratory activity, and cell death (Ibanez et al, 2003). The generation of hydroxyl radical induced by UV radiation rapidly overcomes the self-protection mechanisms of the bacterial cell, and as a result microbial counts decrease exponentially. In the last period of photo-treatment, the rate of microbial inactivation becomes slower because OH radicals produced by the irradiated TiO2 act against the few active bacteria remaining in the UV-irradiated water but also against the inactivated bacteria and the metabolites released during the photocatalytic treatment (Rincon and Pulgarian, 2003). A similar mechanism can be attributed to the decrease in photocatalytic bactericidal efficacy of TiO2 NPs in the presence increasing levels of organic matter.

4. Conclusions The results of this study showed that the presence of organic matter from both produce and meat extract solutions has a significant effect on the bactericidal efficacy of TiO2. Under tested conditions, the pH level of the produce and meat wash solutions had no significant effect on the bactericidal activity of TiO2, whereas turbidity, COD, total phenolics, and protein content had a significant effect on the bactericidal efficacy of TiO2. A linear correlation was observed between COD and total phenolics as well as COD and protein content. While classical disinfection models failed to predict, an empirical equation with COD as predictor variable successfully fit the experimental data. The empirical equation proposed in this study helped to predict the photocatalytic disinfection efficacy of TiO2 in the presence of food processing organic matter.

Acknowledgments Funding for this study was provided by the Agriculture and Food Research Initiative grant no. 2011-68003-30012 from the USDA National Institute of Food and Agriculture, Food Safety: Food Processing Technologies to Destroy Food-borne Pathogens Program (A4131).

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