Listeria Innnocua Biofilm Formation on Food Contact Surfaces and Its Inactivation

Listeria innnocua Biofilm Formation on Food Contact Surfaces and Its inactivation

by Chlorine Dioxide Gas

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science

in the Graduate School of The Ohio State University

Yichao Jin

Graduate Program in Food, Agricultural, and Biological Engineering

The Ohio State University

2017

Master's Examination Committee:

Dr. Gonul Kaletunc, Advisor

Dr. Sudhir Sastry

Dr. Dennis R. Heldman Copyright by

Yichao Jin

2017

Abstract

Biofilm is a major concern in food industries. The biofilm is a matrix of bacteria protected by extracellular polymeric substance (EPS). Once the biofilm is formed on food processing equipment surfaces, it can detach from the surfaces and contaminate the food product. Biofilm can also affect the heat transfer and energy loss in food industry. Protected by EPS, the biofilm is more resistant to various sanitizing method than planktonic cells. Therefore, it is essential to efficiently inactivate the biofilm on food-contact surfaces in a food processing plant.

In this study, stainless steel, borosilicate glass, and silicone rubber were used to investigate the potential for L. innocua to form the biofilm. Biofilm formation was analyzed by crystal violet staining method and plate count method. Chlorine dioxide gas was introduced to inactivate the biofilm formed on stainless steel. The biofilm inactivation was performed at different chlorine dioxide concentrations of 2 mg/L, 3 mg/L, 5 mg/L, and 7 mg/L for various treatment times. The formation of L. innocua biofilm on stainless steel, borosilicate glass, and silicone rubber is significant different based on crystal violet staining method. For the plate count method, the differences of biofilm formation were significant between borosilicate glass, silicone rubber and stainless steel, silicone rubber. The crystal violet staining method appears to be more sensitive than plate count method to detect the biofilm. For the biofilm

ii inactivation, the lowest log reduction (0.04) was detected at 3 mg/L and 1 min treatment, while the highest logreduction (2.01) was detected at 7 mg/L and 10 min treatment. Weibull model was found to be describing the L. innocua biofilm inactivation data. Based on the Weibull model, the desired chlorine dioxide gas concentration (10 mg/L) and exposure time (54 min) was calculated to reach 5-log biofilm reduction.

Key words: Listeria innocua; food-contact surfaces; crystal violet staining; plate count; chlorine dioxide gas

iii Dedication

The work is dedicated to my family, my love, my teachers and my friends who always support me.

iv Acknowledgement

At first, I would like to express my gratitude to my advisor Dr. Gonul Kaletunc who gives me valuable advises to my experimental work, data analysis, and thesis writing.

Secondly, I would like to acknowledge Dr. Ahmed Yousef for his donation of L. innocua ATCC 33090 strain and Dr. Dennis Heldman for his donation of stainless steel coupons.

Thirdly, I would like to acknowledge Larry Heckendorn for his helping me to build the coupons for experiment.

Finally, I would like to thank my committee mumber Dr. Gonul Kaletunc, Dr. Sudhir

Sastry, and Dr. Dennis Heldman for their willingness to critique my work.

v Vita

2011~2015.……………………B.S. Biotechnology, Dalian University of Technology

2015 to present………………Graduate student, Department of Food Agricultural and

Biological Engineering, The Ohio State University

Field of study

Major Field: Food, Agricultural, and Biological Engineering

vi Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgement ...... v

Vita ...... vi

Field of study ...... vi

List of tables...... x

List of Figures ...... xii

Chapter 1: Introduction ...... 1

References: ...... 4

Chapter 2: Listeria innocua biofilm formation on different food contact surfaces ...... 6

Abstract: ...... 6

2.1. Introduction: ...... 8

2.2. Objectives: ...... 11

2.3. Materials and methods: ...... 11

2.3.1. Materials preparation: ...... 12

2.3.2. Research methods: ...... 12

2.3.2.1. Materials preparation: ...... 12

2.3.2.2. Bacteria stain and inoculum preparation: ...... 14

2.3.2.3. Biofilm inoculation on different materials: ...... 15

2.3.2.4. Biofilm formation: ...... 15

vii

2.3.2.5. Preliminary experiments for removal of biofilm by sonication: ...... 16

2.3.2.5.1. Effect of sonication on Listeria innocua viability ...... 16

2.3.2.5.2. Effect of sonication on L. innocua biofilm removal ...... 17

2.3.2.6. Microbial enumeration and staining: ...... 17

2.3.2.6.1. Plate count method: ...... 17

2.3.2.6.2. Crystal Violet staining method: ...... 18

2.3.2.7. Data analysis: ...... 18

2.4. Result and discussion: ...... 18

2.4.1. Results of preliminary study for biofilm analysis methods: ...... 18

2.4.2. Biofilm formation on different materials and analyzed by crystal violet staining and plate count methods: ...... 21

2.5. Conclusion: ...... 29

2.6. Future work: ...... 29

References: ...... 30

Chapter 3: Inactivation of Listeria innocua biofilm grown on stainless steel coupons by chlorine dioxide gas ...... 34

Abstract: ...... 34

3.1. Introduction: ...... 36

3.2. Objective: ...... 42

3.3. Materials and method: ...... 42

3.3.1. Materials preparation: ...... 42

3.3.2. Methods: ...... 43

3.3.2.1. Stainless steel coupon handling: ...... 43

3.3.2.2. Bacteria stains and inoculum preparation: ...... 43

3.3.2.3. Inoculation of bacteria on stainless steel coupon: ...... 44

viii 3.3.2.4. Biofilm incubation: ...... 44

3.3.2.5. Gaseous chlorine dioxide system and treatment: ...... 45

3.3.2.6. Microbial enumeration: ...... 47

3.3.2.7. Modeling and analysis of data: ...... 48

3.4. Results and Discussion: ...... 49

3.5. Conclusion: ...... 63

3.6. Future work: ...... 63

References: ...... 64

Appendix A. Raw data of the experiments ...... 68

References ...... 72

ix List of tables

Table 2.1. The log-value of bacteria number after sonication for different times ...... 19

Table 2.2. Biofilm formation on stainless steel, borosilicate glass, and silicone rubber by crystal violet staining and plate count methods ...... 21

Table 2.3. Oneway ANOVA analysis of the potential of different materials to form biofilm by crystal violet staining method ...... 23

Table 2.4. Oneway ANOVA analysis and connecting letter report of the potential of different materials to form biofilm by crystal violet staining method ...... 25

Table 2.5. Oneway ANOVA analysis of the potential of different materials to form biofilm by plate count method ...... 25

Table 2.6. Oneway ANOVA analysis and connecting letter report of the potential of different materials to form biofilm by plate count method ...... 27

Table 3.1. Biofilm inactivation by various sanitizers on different surfaces...... 38

Table 3.2. Parameters to generate chlorine dioxide gas by sodium chlorite and hydrochloric acid ...... 46

Table 3.3. Connecting letters report of the control groups in each batch ...... 50

Table 3.4. Raw data of biofilm inactivation ...... 52

Table 3.5. Parameter for log-survival model at each chlorine dioxide concentration 54

Table 3.6. F-value to compare the goodness of fitting of linear model and Weibull model ...... 56

Table 3.7. Parameters in Weibull model for each ClO2 gas concentration to describe biofilm inactivation ...... 57

Table 3.8. Independent constants a1, a2, a3, and a4 in Weibull model ...... 60

x Table 3.9. Calculated value for chlorine dioxide gas treatment of L. innocua biofilm on stainless steel coupons calculated from Weibull model fit to the data to reach a 5- log reduction ...... 62

Table A.1. Raw data of L. innocua biofilm inactivation by chlorine dioxide gas on stainless steel ...... 68

Table A.2. Raw data of L. innocua biofilm formation on stainless steel, borosilicate glass and silicone rubber materials detected by plate count method ...... 70

Table A.3. Raw data of L. innocua biofilm formation on stainless steel, borosilicate glass and silicone rubber materials detected by crystal violet staining method ...... 71

xi List of Figures

Figure 2.1. The dimension of the coupons ...... 13

Figure 2.2. Stainless steel, borosilicate glass and silicone rubber coupons glued with polycarbonate tubes ...... 14

Figure 2.3. Tukey-Kramer test of the effect of sonication on the activity of L. innocua planktonic cells ...... 20

Figure 2.4. Crystal violet staining method for biofilm detection ...... 22

Figure 2.5. Plate count method for biofilm detection ...... 22

Figure 2.6. All pairs Tukey-Kramer test of the potential of different materials to form biofilm by crystal violet staining method ...... 24

Figure 2.7. All pairs Tukey-Kramer test of the potential of different materials to form biofilm by plate count method ...... 26

Figure 3.1. Distribution of the log number of bacteria in the biofilm formed on stainless steel ...... 46

Figure 3.2. Distribution of the log number of bacteria in the biofilm formed on stainless steel ...... 50

Figure 3.3. L. innocua biofilm inactivation at each chlorine dioxide concentration and the data was fitted in linear model. Zero point is added at each line...... 53

Figure 3.4. L. innocua biofilm inactivation at each chlorine dioxide concentration and the data was fitted in Weibull model. Zero point is added at each line...... 55

Figure 3.5. b and n in Weibull model described as a function of chlorine dioxide concentration ...... 58

Figure 3.6. The log-reduction of L. innocua biofilm by chlorine dioxide gas as a function of chlorine dioxide concentration and treatment time ...... 61

xii Chapter 1: Introduction

Biofilm has been a major concern in food industry. When the biofilm detached from the food contact surfaces, the bacteria will contaminate the food product. Especially dairy industry is susceptible to biofilm formation and contamination (Srey et al.,

2013; Kumar et al., 1998). A biofilm is a matrix of enclosed bacteria adhere to each other and to surfaces. The biofilm is protected by extracellular polymeric substance

(EPS) and resistant to various sanitizing procedures (Chavant et al., 2002). Since the biofilm sometimes may not be thoroughly removed from food contact surfaces, after sanitization, it may contaminate the food product during subsequent food processing.

Furthermore, the biofilm attached on food processing equipment surfaces may cause a loss of energy by blocking the fluid flow and heat transfer (Kumar et al.,1998).

Therefore, it is essential to find a more effective way to inactivate the biofilm on food contact surfaces.

In food industry, various sanitizers have been applied to clean the food processing equipment or inactivate the bacteria. However, sanitizers such as hypochlorous acid, chlorine, iodine, ozone, hydrogen peroxide, peroxyacetic acid, quaternary ammonium chloride and anionic acids may encounter the limitation such as the low efficacy to inactivate microorganisms in food processing environment, the high cost, the unstable

1 under high temperature and the low sensitivity to organic matter (Grinstead et al.,

2009). Chlorine dioxide, a novel sanitizer that has been widely studied recently, has shown a considerable effect on inactivation of the biofilm (Vaid et al., 2010). The application of gaseous chlorine dioxide is preferred in juice industry for sanitizing processing equipment with the advantage of decreasing the water cost and high inactivation efficacy (Han et al., 1999).

Biofilm characterization is mainly crystal violet staining in microtiter plates, plate counting and microscopy analysis. Gram staining method is mainly applied to detect the biofilm formation on microtiter plate. Stepanović et al. (2000) stained the

Staphylococcus spp. biofilm growing in 96-well microtiter plate assay with safranin solution followed by decolorizing the stain to detect the biofilm formation and found that the S. aureus has a better potential to form the biofilm than other Staphylococcus species. Biofilm can also form on various food contact surfaces such as stainless steel, borosilicate glass, and silicone rubber. Zhong et al. (2013) grew the biofilm on stainless steel materials followed by removing the biofilm by sonicating the coupons in a centrifuge tube, and observed that the Nafion coated stainless steel has a less potential to form the Escherichia Coli DH5α biofilm than the untreated stainless steel.

Chmielewski et al. (2006) reported a 95% inactivation of the Listeria monocytogenes biofilm on buna-N rubber by heat treatment at 80°C for 15 min. Brooke et al. (2008) detected that the genetically modified Stenotrophomonas maltophilia had a better potential to form the biofilm on polyvinyl chloride, polystyrene and borosilicate glass when compared to the parental isolates. The most visual way to analyze the biofilm is

2 to detect the topography of biofilm under microscope. Alhede et al. (2012) used confocal laser scanning microscopy and scanning electron microscopy to get a comprehensive visual impression of Pseudomonas aeruginosa biofilm structure and composition. Furthermore, Alhede et al. (2012) indicated that a single microscopy method is not appropriate to analyze the biofilm matrix structure, and appropriately combination of different microscopy analysis is a better strategy. However, the cost of various microscopes is not acceptable to many research centers.

In this study, L. innocua, which is the best surrogate for L. monocytogenes, was chosen to study the biofilm formation on different surfaces (stainless steel, silicone rubber, and borosilicate glass). The biofilm was characterized by both crystal violet staining and plate count methods and compared the agreement of two methods on analyzing the biofilm was compared. Then, different concentrations of chlorine dioxide gas were applied to the L. innocua biofilm formed on stainless steel coupons for inactivation of biofilm. The results were analyzed by plate count method. Weibull model was fitted to the data to describe the inactivation of L. innocua biofilm as a function of chlorine dioxide gas concentration and the exposure time.

The result shows that the potential of different materials (stainless steel, borosilicate glass, and silicone rubber) for biofilm formation varies. The sensitivity of the methods to detect biofilm formation also varies. For the plate count method, the difference of biofilm formation between stainless steel and borosilicate glass was not detect. For the crystal violet staining method, three different materials are

3 significantly different from each other for biofilm formation. The crystal violet staining method was found to be more sensitive than plate count method for biofilm detection. The highest inactivation for L. innocua biofilm by chlorine dioxide gas was observed to be 2.01 log-reduction at 7 mg/L chlorine dioxide concentration for 10 min. The Weibull model described the inactivation of L. innocua biofilm as a function of chlorine dioxide gas concentration and exposure time.

References:

Alhede, M., Qvortrup, K., Liebrechts, R., Høiby, N., Givskov, M., & Bjarnsholt, T. (2012). Combination of microscopic techniques reveals a comprehensive visual impression of biofilm structure and composition. Fems Immunology & Medical Microbiology, 65, 2, 335-342. Brooke, J. S., Davis, N. A., Davis, N. A., & Davis, N. A. (2008). Mutation of a lipopolysaccharide synthesis gene results in increased biofilm of Stenotrophomonas maltophilia on plastic and glass surfaces. Annals of Microbiology, 58, 1, 35-40.

Chavant, P., Martinie, B., Meylheuc, T., Bellon-Fontaine, M. N., & Hebraud, M. (2002). Listeria monocytogenes LO28: surface physicochemical properties and ability to form biofilms at different temperatures and growth phases. Applied and Environmental Microbiology, 68, 2, 728-37.

Chmielewski, R. A. N., & Frank, J. F. (2006). A predictive model for heat inactivation of Listeria monocytogenes biofilm on buna-N rubber. Lwt - Food Science and Technology, 39, 1, 11-19.

Grinstead, D. (2009). Cleaning and sanitation in food processing environments for the prevention of biofilm formation, and biofilm removal. In P. M. Fratamico, B. A. Annous, & N. W. Gunther (Eds.), Biofilms in the food and beverage industries (pp. 343e347). Boca Raton, FL: CRC Press, Woodhead publishing.

Han, Y., Guentert, A., Smith, R., Linton, R., & Nelson, P. (1999). Efficacy of chlorine dioxide gas as a sanitizer for tanks used for aseptic juice storage. Food Microbiology, 16, 1, 53.

Jhass, A. (2014). A scanning electron microscope characterisation of biofilm on failed craniofacial osteosynthesis miniplates. British Journal of Oral and Maxillofacial Surgery, 52, 8.)

4 Kumar, C. G., & Anand, S. K. (1998). Significance of microbial biofilms in food industry: a review. International Journal of Food Microbiology, 42, 1, 9-27.

Park, S.-H., & Kang, D.-H. (2014). Inactivation of biofilm cells of foodborne pathogens by steam pasteurization. European Food Research and Technology : Zeitschrift Für Lebensmittel-Untersuchung Und Forschung A, 238, 3, 471-476.

S. Priya; S.Priya, Assistant Professor, PG Department of Biotechnology, S.T.E.T. Women’s College, Mannargudi, & M. Priya. (2015). Biofilm Forming and Antimicrobial Susceptibility of Clinical Isolates of Staphylococcus Species. Research & Reviews: A Journal of Microbiology & Virology.

Stepanović, S., Vuković, D., Dakić, I., Savić, B., & Švabić-Vlahović, M. (2000). A modified microtiter-plate test for quantification of staphylococcal biofilm formation. Journal of Microbiological Methods, 40, 2, 175-179.

Vaid, R., Linton, R. H., & Morgan, M. T. (2010). Comparison of inactivation of Listeria monocytogenes within a biofilm matrix using chlorine dioxide gas, aqueous chlorine dioxide and sodium hypochlorite treatments. Food Microbiology, 27, 8, 979- 984.

Zhong, L. J., Pang, L. Q., Che, L. M., Wu, X. E., & Chen, X. D. (2013). Nafion coated stainless steel for anti-biofilm application. Colloids and Surfaces B: Biointerfaces, 111, 252-256.

5 Chapter 2: Listeria innocua biofilm formation on different food contact surfaces

Abstract:

Biofilm has been a major concern in food industry. When the biofilm detached from the food contact surfaces, the bacteria will contaminate the food product. Especially, dairy industry is susceptible to biofilm formation and contamination. Once the biofilm is formed, it may not be removed thoroughly by regular cleaning procedures used. The biofilm may become stable and resistant to various sanitizer treatments.

Mainly three methods of biofilm analysis are used including Gram staining, standard plate count, and microscopy analysis to study the biofilm formation on various surfaces. However, the Gram staining method is applied only to the biofilm formed on microtiter plate, and there is a gap on introducing this method to food contact surfaces.

In this study, the potential of L. innocua biofilm formation was investigated on the surfaces of stainless steel, borosilicate glass, and silicone rubber. The plate count and crystal violet staining methods were applied to detect the biofilm and compare their effectiveness to study the biofilm. The plate count method results indicated that the L. innocua biofilm formation on borosilicate glass (4.23×108) and silicone rubber

6 (3.83×108) were not significantly different, while the results generated from crystal staining method showed that the biofilm formation on all three material surfaces were significantly different. The silicone rubber had the highest potential to form biofilm

(1.03) and the borosilicate glass had the lowest (0.85). The crystal violet staining method appeared to be more sensitive to detect the biofilm on various surfaces than standard plate count method.

Key word: Listeria innocua biofilm; stainless steel; borosilicate glass; silicone rubber; crystal violet; standard plate count

7 2.1. Introduction:

Biofilm, a major concern in food industry, can be isolated from a variety of materials, which is used in packaging and equipment, such as stainless steel, plastic, rubber, and glass (Hansen et al., 2011; Stepanović et al., 2004; Chmielewski et al., 2006; Chae, et al., 2006). When the biofilm forms on the food contact surfaces, it becomes a reservoir of bacteria and will consequently contaminate the food product in food industry (Mittelman, 1998; Araújo et al., 2014). Biofilm can survive for a long period on various materials and resistant to various sanitizing conditions. During food processing, the bacteria sheltered in the biofilm may detach and contaminate the food product (Ory et al., 1987).

The undesired aggregation of microorganisms, plants, algae and animals on solid surfaces is termed as biofouling, which is a serious problem in food industry. Various chemical sanitizers have been studied to mitigate the biofilm for decades. However, developing a more efficient way to prevent biofilm is in great demand (Baker et al.,

1998). The biofilm can be regard as part of the biofouling. The solid surfaces with a potential biofilm contamination include pipes, water distribution system, cooling facilities, power plant and membrane filtration system. Biofilm contamination may increase both cleaning and operation cost (Le-Clech et al., 2006; Liao et al., 2015).

When biofilm is formed, it may affect the membrane performance and cause an increase in trans-membrane pressure and due to hydraulic resistance and biofilm enhanced osmotic pressure (Herzberg et al., 2007; Herzberg et al., 2009).

8 Stainless steel is a commonly used material in food industry and water supply system

(Little et al., 1991; Pedersen, 1982a, b; Driessen, 1984; Lewis et al., 1987). The adhesion of bacteria on stainless steel may cause corrosion and contamination to food product (Little et al., 1991). Silicone rubber is widely used in dairy and brewing industry because it is inert to biological degradation. However, the undesired microbiological deposition on silicone rubber may influence hydrophobicity of silicone rubber surface and subsequently change the functionality of insulator

(Flemming et al., 1998). The additives in silicone rubber such as polydimethylsiloxane may provide nutrition for microorganism to grow (Wallstrom et al., 2002). Stable biofilm may consequently form on silicone rubber and the free radical provided by bacteria will play a role in the autoxidation of polymers

(Atarijabarzadeh et al., 2011). The usage of borosilicate glass is popular in food and pharmaceutical industries in plant and piping because of the following advantages:

(1) Outstanding corrosion resistance, (2) smooth pore free surface, (3) transparency,

(4) no effect on taste or odor, (5) physiological inertness. Brooke et al. (2008) detected that the biofilm can form on borosilicate glass and may contaminate the food product potentially. The formation of biofilm on stainless steel, silicone rubber, and borosilicate glass requires more attention.

There are three major methods to detect and analyze the biofilm. The detection of food-borne pathogens in food industry is performed by swabbing or scraping of surfaces. In a food processing plant or environment, a sponge-like material is used to swab or scrape the surfaces and then soak the sponge-like material in potassium

9 phosphate buffer (PPB), saline or peptone water to remove the bacteria from the sponge-like materials (Foschino et al., 2003). After taking the sample, the analysis is conducted by growing bacteria culture which may take one to two days. The Gram staining method was not commonly used for biofilm analysis on food contact surfaces in industry because of the limitation of taking samples and the toxicity of staining solution. In a laboratory, the Gram stain method for biofilm analysis is performed in microtiter plates. Biofilm generated in wells of microtiter plates is stained crystal violet or safranin followed by decolorizing the stain with ethanol or other destaining solutions such as acetic acid (Stepanovic et al., 2007). The analysis of biofilm is performed in microtiter reader at 595 nm wavelength (Djordjevic et al., 2002).

Another method applied in laboratory to analyze the biofilm grown on stainless steel coupons is to vortex the coupons with glass beads to remove the cells from the coupons to PPB or peptone water and then prepare serials dilution with the bacteria solution followed by plate counting (Vaid et al., 2010; Chmielewski et al., 2006).

Analyzing the biofilm on food contact surfaces by Gram staining method has not been studied in detail and limited to microtiter environment in which biofilm was grown on polyvinyl chloride (PVC) surfaces. The application of microscope in biofilm study provides a visual impression on biofilm formation. Various types of microscopes have been applied for biofilm studies. The scanning electron microscopy

(SEM) is most commonly used (Northmans et al., 1991; Zottola, 1991).

Epifluorescene microscopy, interference reflection microscopy, atomic force microscopy, confocal laser scanning microscopy, and environment scanning electron

10 microscopy (ESEM) has been applied to study the biofilm (Holah et al., 1988;

Wirtanen et al., 1995; Ladd et al., 1990; Caldwell et al., 1992; Beech, 1996; Debeer et al., 1997; Little et al., 1991; Hodgson et al., 1995). However, grooves, crevice, dead end are hard to access while analyzing the biofilm with a microscope. Researchers employed the fluorescent-antibody technique to directly quantify analyze the biofilm amount by measuring the florescence intensity (Leriche et al., 1995). However, the microscopy methods cannot be widely used in food industry because of the high cost and the limitation of sampling size.

In this study, borosilicate glass, stainless steel and silicone rubber were used to grow biofilm to investigate the potential biofilm formation on these materials. L. innocua, which is a commonly used surrogate for L. monocytogenes, was chosen as a model system. Biofilm were prepared after a 51-hour incubation on each selected material surface and were analyzed by plate count method and crystal violet staining method.

The reproducibility of the results within each method was evaluated and the results obtained with different analysis methods were compared.

2.2. Objectives:

The objectives of this study are (1) to investigate the potential of L. innocua biofilm formation on different materials, (2) to evaluate and compare the reproducibility of crystal violet staining method and plate count method to analyze the biofilms.

2.3. Materials and methods:

11 2.3.1. Materials preparation:

Tryptone soya broth (TSB) (CM0129; Oxoid Ltd., Basinstoke, Hampshire, England) was prepared by mixing the TSB powder and deionized (DI) water at ratio 3:10 (w/v) in a flask. Tryptone soya agar (TSA) media was prepared by mixing TSB media and agar (BP1423-500; Fisher Scientific Co., Fair Lawn, New Jersey, USA) at ratio

100:15 (v/w). 0.85% saline was prepared by dissolving sodium chloride (S641-500;

Fisher Scientific Co., Fair Lawn, New Jersey, USA) in water at a ratio of 0.85:100

(w/v). Sparkleen detergent solution was used to wash and clean the glassware and coupons and was prepared by adding a spoon of Sparkleen detergent powder (04-320-

4; Fisher Scientific Co., Pittsburgh, PA, USA) into 1 L of DI water. 70% ethanol was used for cleaning and sanitizing by diluting 96% ethanol (VXC-128; Volu-Sol, Salt lake City, UT, USA) with DI water. Crystal violet powder (C0775-25G; Sigma-

Aldrich Co., St Louis, MO, USA) was used to prepare the crystal violet solution to be used for staining by mixing 1 g of crystal violet powder and 100 ml of DI water.

2.3.2. Research methods:

2.3.2.1. Materials preparation:

Stainless steel #304, silicone rubber (41816-03; VIP Rubber & Plastic Company, Inc.,

La Habra, California, USA), and borosilicate glass (MIL-G-47033; McMaster-Carr,

Aurora, Ohio, USA) were used separately to grow biofilm. Stainless steel coupons were obtained from Food Science Department, The Ohio State University, (Fig. 2.1).

12 The silicone rubber sheet was purchased from VIP Rubber & Plastic Company.

Silicone rubber coupons were built in our laboratory, the size is same as the stainless steel coupons. The aluminum square bases and silicone rubber pieces were cut in machine shop in Food, Agricultural and Biological Department, The Ohio State

University. The silicone rubber pieces were attached to the aluminum plates by silicone rubber glue (DAP Products Inc., Baltimore, MD, USA), and air dried for two days. The silicone rubber coupons were stored in antistatic bag. The borosilicate glass coupons were prepared in the glass shop in the Chemistry Department at The Ohio

State University. The dimensions of each coupon used is shown in Fig. 2.1.

Figure 2.1. The dimension of the coupons

A polycarbonate tube (8585K108; McMaster-Carr, Aurora, Ohio, USA) was cut

9±0.2 mm in length and the cut section was polished carefully. Each coupon and 9 mm-length polycarbonate tube were sprayed with 70% ethanol to sanitize. Then, 9-

13 mm length polycarbonate tube was glued on the coupon with silicone rubber glue and was air dried for two days. The extra glue was cleaned. For the biofilm formation, the structures which are similar to microtiter wells were built. A microtiter well was constructed by attaching the polycarbonate tube to the various surfaces studied. The set up of tube is shown in Figure 2.2.

Figure 2.2. Stainless steel, borosilicate glass and silicone rubber coupons glued with polycarbonate tubes

2.3.2.2. Bacteria stain and inoculum preparation:

A strain of L. innocua ATCC 33090 was obtained from Food Science Department,

The Ohio State University (Columbus, OH, USA). Bacteria inoculum was prepared by transferring a loop of L. innocua colony into 200 ml sterile TSB media in a flask and incubate at agitating speed 150 rpm at 37°C for 20 hours in water bath (Lab-Line

14 Instruments. Inc.) until the bacteria concentration reached to about 3.5×109 CFU/ml.

The concentration of bacteria inoculum is enumerated by plate count on Tryptone

Soya Agar (TSA) plates after serial dilutions using 0.85% saline. The bacteria stock solution was prepared by mixing the bacteria inoculum and 100% glycerol at 1:1 ratio in a 2.5 ml centrifuge tube followed by vortexing the centrifuge tube gently to ensure the solution is well mixed. The centrifuge tube was then placed in -80°C freezer until future use.

2.3.2.3. Biofilm inoculation on different materials:

The coupons were placed in petri dishes, covered with aluminum foil and autoclave at

121°C for 25 min. After autoclaving, the petri dishes with coupons were placed in the biological hood followed by removing the lid and spraying ethanol on each coupon.

After 20 min, 1 ml bacteria inoculum was placed inside the polycarbonate well and 2 ml sterile water was added to the bottom of the petri dishes to prevent the inoculum from drying during incubation. Petri dishes was covered with the lid and the petri dishes with coupons was placed in the 37°C incubator for 3 hours.

2.3.2.4. Biofilm formation:

After three hours’ attachment, the petri dishes were removed from incubator to biological hood. Loose bacteria were removed from the polycarbonate well by rinsing five times with sterile 0.85% saline solution using micropipette. 1 ml fresh sterile

TSB media was transferred into the polycarbonate well and incubated at 37°C for 24

15 hours. The bacteria attached on the surface of the coupon that have not been removed were grown in the fresh sterile TSB media. After 24 hours, the loose bacteria were removed for a second time, followed by the rinsing and refilling steps. The biofilm was incubated for another 24 hours. The total biofilm growing time was 51 hours.

2.3.2.5. Preliminary experiments for removal of biofilm by sonication:

Biofilm was removed by placing the coupons within petri dishes inside a sonicator

(2510R-DTH; Branson ultrasonic corporation, Danbury, CT, USA). Preliminary experiments were performed to determine the optimum sonication time to remove biofilm without inactivation of bacteria.

Preliminary experiments included (1) testing the effect of sonication on the bacteria activity, (2) determining the optimal sonication time to remove the biofilm from stainless steel coupons to saline.

2.3.2.5.1. Effect of sonication on Listeria innocua viability

A loop of L. innocua colony was transferred into 200 ml of TSB media and was incubated at 37°C for 20 hours. Serial dilutions were prepared with 0.85% saline followed by plate counting on TSA plates to enumerate the number of bacteria. 3 ml of bacteria inoculum was transferred into 10 ml centrifuge tube and was sonicated at

42 kHz for up to 60 min, and the number of bacteria was enumerated by plate count method. This procedure was designed to evaluate the influence of sonication to L. innocua viability by comparing the number of bacteria before and after sonication.

16 2.3.2.5.2. Effect of sonication on L. innocua biofilm removal

After 51-hour biofilm formation on each coupon, all the coupons were rinsed by 20 ml of sterile 0.85% saline by micropipette and the coupon was placed inside a 250 ml beaker with 30 ml sterile 0.85% saline. All the beakers were then sonicated for 5, 10,

15, 20, 30, or 60 min. After sonication, all the coupons were removed from the beaker and dried in air for 30 min. Each coupon was stained with 300 µl 1% crystal violet solution for 45 min. The excess crystal violet solution was removed by rinsing coupons with DI water. Crystal violet residues were measured on each coupon to detect the efficacy of sonication on removing L. innocua biofilm from coupon surfaces.

2.3.2.6. Microbial enumeration and staining:

After biofilm growth for 51 hours, the coupons were removed from petri dishes and placed in the biological hood and air dry for 20 min.

2.3.2.6.1. Plate count method:

Each coupon was placed inside a sterile petri dish. Each polycarbonate well on coupon was filled with 1 ml sterile 0.85% saline and the bottom of petri dish was filled with 10 ml sterile water. Each petri dish was sealed carefully with parafilm and sonicated for 20 min. Serial dilutions were prepared by using bacteria solution in each polycarbonate well. After transferring 0.1 ml of each dilution into TSA plate, the TSA plates were spread evenly with spreaders. The TSA plates were incubated at 37°C for

17 24 hours. After 24 hours, bacteria number was enumerated by counting the colonies on each plate that has 20~200 colonies.

2.3.2.6.2. Crystal Violet staining method:

Crystal violet was added into each polycarbonate well to stain the biofilm for 45 min.

Then the excess crystal violet was washed gently with 20 ml of sterile water. After 20 min, 1 ml of 95% ethanol (v/v) was added into each polycarbonate well to decolorize the biofilm. The 1 ml of ethanol containing crystal violet was placed inside a plastic cuvette with an additional 2 ml of 96% ethanol (a total 3 ml ethanol in each plastic cuvette). The biofilm formation was analyzed by measuring the absorbance of the solution of crystal violet in ethanol at 595 nm wavelength with a spectrophotometer

(4001/4; Spectronic Instruments, USA).

2.3.2.7. Data analysis:

The data was analyzed by using JMP Pro 12 software (SAS®, Duncan, SC) by

ANOVA and Tukey-Kramer method to compare the potential of different material surfaces to form biofilm and the reproducibility and sensitivity of methods for analyzing the biofilm.

2.4. Result and discussion:

2.4.1. Results of preliminary study for biofilm analysis methods:

18 The effect of sonication time on inactivation of bacteria showed that up to 60 min of sonication number of bacteria reduced from 9.57 log unit to 9.45 log unit with increasing sonication time (Table 2.1).

Table 2.1. The log-value of bacteria number after sonication for different times Sonication time (min) Log-bacteria number 0 9.57 5 9.56 10 9.56 15 9.56 20 9.49 30 9.48 60 9.45

The data was further analyzed in JMP by Tukey-Kramer test method to evaluate if the bacteria numbers are significantly changing with sonication time.

19 9.6

9.55

9.5

Log-bacteria number Log-bacteria 9.45

9.4

0 10 15 20 30 5 60 All Pairs sonication time (min) Tukey-Kramer 0.05

Figure 2.3. Tukey-Kramer test of the effect of sonication on the activity of L. innocua planktonic cells

Figure 2.3 shows that bacteria numbers at each sonication time are not significantly different from each other. Therefore, we can conclude that the sonication less than 60 min can be applied for removal of biofilm from coupon surfaces without causing significant bacteria loss.

The sonication time to efficiently remove the biofilm from coupon surfaces was investigated by using crystal violet staining. Crystal violet was placed on each coupon after sonication to detect the biofilm. After washing out the extra crystal violet with water, the coupons sonicated up to 10 min had remaining crystal violet, 10 min’s sonicated coupon has a little crystal violet residue. The coupons sonicated longer than

15 min has no crystal violet residue, which indicate that 15 min’or longer sonication

20 will remove the biofilm thoroughly. To make the experiment efficient, the sonication time is set to 15 min in the later experiment.

2.4.2. Biofilm formation on different materials and analyzed by crystal violet staining and plate count methods:

Both crystal violet staining and plate count methods were applied to compare the potential of biofilm formation on three surfaces. For crystal violet staining method, the formation of biofilm on each surface had six replicates, while for the standard plate count method, four replicates were performed for each surface. Both crystal violet staining method and plate count method showed the formation of biofilm on all the surfaces evaluated (Table 2.2).

Table 2.2. Biofilm formation on stainless steel, borosilicate glass, and silicone rubber by crystal violet staining and plate count methods Crystal violet staining result Plate count result (Log Materials (absorbance at 595 nm)a bacteria number)b Stainless steel 0.85±0.10 8.59±0.20 Borosilicate glass 0.72±0.07 8.56±0.14 Silicone rubber 1.03±0.08 8.9±0.11 a results from crystal violet staining method that have six replicates b results from plate count method that have four replicates

The comparison of biofilm formation on coupon surfaces as detected by crystal violet staining showed that the surfaces have different potentials for biofilm formation in the

21 order of highest to lowest silicone rubber, stainless steel and borosilicate glass (Fig

2.4).

Silicone rubber

Borosilicate glass

Stainless steel 0.00 0.20 0.40 0.60 0.80 1.00 1.20 Absorbance at 595 nm

Figure 2.4. Crystal violet staining method for biofilm detection

Silicone rubber

Borosilicate glass

Stainless steel 0.00E+00 2.00E+08 4.00E+08 6.00E+08 8.00E+08 1.00E+09 Bacteria number (CFU/ml)

Figure 2.5. Plate count method for biofilm detection

22 Silicone rubber shows the highest potential to form the biofilm, while the borosilicate glass shows the least potential to form biofilm. Plate count method analysis showed a similar potential of biofilm formation for all the surfaces evaluated (Fig 2.5). The highest potential of biofilm formation on silicone rubber may due to the high nutrition that the silicone rubber may provide for bacteria to grow and the bacteria attachment on silicone rubber surface may consequently become stable. Besides, surface roughness is another important factor that may influence the biofilm formation.

Rodriquez et al. (2008) reported that the Listeria monocytogenes biofilm formation on stainless steel surfaces is higher on a surface with higher roughness value.

A statistical analysis of the potential of different materials to form biofilm by two different methods was performed by using JMP software. The crystal violet and plate count methods were analyzed separately.

Table 2.3. Oneway ANOVA analysis of the potential of different materials to form biofilm by crystal violet staining method Source DF Sum of Squares Mean square F Ratio Prob>F Materials 2 0.29 0.15 21.58 <0.0001* Error 15 0.10 0.0069

F-test was applied to comparing the variability of biofilm formation based on the sum of square. From Table 2.3, the Prob>F smaller than 0.0001, which indicate that at least one pair of the materials among borosilicate glass, stainless steel, and silicone rubber are significantly different on biofilm formation when detected by crystal violet

23 staining method. All pairs Tukey-Kramer test were applied to make further analysis of the biofilm formation among all materials.

1.1

0.9

0.8 Absorbance (595 nm) (595 Absorbance

0.7

0.6 Borosilicate glass Silicone rubber Stainless steel All Pairs Materials Tukey-Kramer 0.05

Figure 2.6. All pairs Tukey-Kramer test of the potential of different materials to form biofilm by crystal violet staining method

All pair Tukey-Kramer test was applied by comparing the difference between variables by pairs based on the standard deviation and average of each variable. From

Figure 2.6, the relationship of the potential of the biofilm formation on different surfaces was showed visually. Connecting letter report was applied to make deeper analysis to detect if all the materials are significantly different on biofilm formation.

24 Table 2.4. Oneway ANOVA analysis and connecting letter report of the potential of different materials to form biofilm by crystal violet staining method Materials Letters The average of the absorbance at 595 nm Silicone rubber A 1.03 Stainless steel B 0.85 Borosilicate glass C 0.71

From the connecting letter report result shown in Table 2.4, all materials are not connected by the same letter, which indicates that borosilicate glass, silicone rubber, and stainless steel are significantly different on biofilm formation.

Table 2.5. Oneway ANOVA analysis of the potential of different materials to form biofilm by plate count method Source DF Sum of Squares Mean square F Ratio Prob>F Materials 2 0.28 0.14 Error 9 0.22 0.02 5.74 0.0247 C. Total 11 0.49

From the F-test result, the Prob>F is 0.0247, which indicates that at least one pair of the materials are significantly different on biofilm formation. Then, the all pairs

Tukey-Kramer test was applied to make deeper analysis.

25 1.1e+9

1e+9

900000000

800000000

700000000

600000000

500000000

Bacteria number (CFU/ml) number Bacteria 400000000

300000000

200000000 Borosilicate glass Silicone rubber Stainless steel All Pairs Materials Tukey-Kramer 0.05

Figure 2.7. All pairs Tukey-Kramer test of the potential of different materials to form biofilm by plate count method

From the results generated from all pairs Tukey-Kramer, the circles represent the potential of the biofilm formation on borosilicate glass and stainless steel overlaps closely, which indicates that borosilicate glass and stainless are similar in biofilm formation, while the circle represents silicone rubber are not close to other materials.

Further connecting letter report shows that the relationship of biofilm formation on three materials is consistent to that generated from all pairs Tukey-Kramer test.

26 Table 2.6. Oneway ANOVA analysis and connecting letter report of the potential of different materials to form biofilm by plate count method Materials Letters Average of numbers of the biofilm on each surface Silicone rubber A 812500000 Stainless steel B 422500000 Borosilicate glass B 382500000

The statistical analysis of plate count method results show that although the potentials of borosilicate glass and silicone rubber, stainless steel and silicone rubber to form biofilm were significantly different, the difference between borosilicate glass and stainless steel cannot be detected. Although crystal violet method was able to differentiate among the three surfaces for their likeliness of biofilm formation, plate count method could not. The low sensitivity of plate count method in detecting biofilm formation may due to the large errors generated from serials dilutions.

However, both methods are in agreement that potential of the biofilm formation on borosilicate glass and silicone rubber are significantly different.

Based on the standard deviations given for each method in Fig. 2.4 and Fig.2.5, the standard deviation of plate count method is higher than that of the crystal violet staining method when compared to the absolute value. There are many factors contribute to the standard deviation in this experiment. At first, although the materials applied to each method are the same, the surface conditions of the same materials are not controlled strictly for each method, and this may contribute to the variance of the biofilm formation. Secondly, biofilm is grown in several batches, and the initial

27 number of the inoculum, the bacteria condition, and the moisture may be varied and subsequently affect the biofilm formation on different materials. Thirdly, serials dilutions in plate count method may contribute to an increasing of the error.

Both plate count method and crystal violet staining methods have their advantages.

Application of crystal violet staining method in food industry requires removal of the surfaces from the system for analysis while a sample of biofilm can be removed directly without the removal of the surfaces from the system by plate count method.

The crystal violet staining method is more suitable for collecting data in shorter time and detecting the biofilm more sensitively. The application of microscopy could be a direct way to describe the biofilm formation at various conditions, however, the sample size needed for microscopy is restricted to several scenes under the lens and it is too small to have a macroscopic in formation about the biofilm when compared to both plate count and staining methods. Biofilm may not be uniform over the area and information on a small area cannot be translated into a larger area. Microscopy studies use the height of the biofilm to quantify the biofilm formation which will not be uniform.

There are some limitations in this study. (1) Major limitation of crystal violet staining method is its applicability to biofilm from only Gram-positive bacteria. (2) The set up developed in this study can be improved by using a glue which will not fail to hold the system after a few use or set up can be constructed as a single piece by molding or

3D printing. (3) After using crystal violet to stain the biofilm formed on silicone

28 rubber, the silicone rubber surfaces were stained purple and the stain cannot be removed. Although this irreversible stain will not affect the biofilm formation and biofilm detection, this indicate that the crystal violet staining method will be only applied to detect the biofilm formation on silicone rubber in laboratory scale.

2.5. Conclusion:

A set up was constructed simulating a microtiter well to detect biofilm by using crystal violet staining method. The set up provides the advantage of using any surface relevant to food processing. L. innocua ATCC 33090 can form biofilm on all the materials applied in this study. Silicone rubber has the best potentials to form the biofilm while the borosilicate glass has the least, which is detected by crystal violet staining method. For the result calculated plate count method, all materials are not significantly different from biofilm formation. Furthermore, we can conclude that crystal violet staining method is more sensitive than plate count method on biofilm detection, but the crystal violet staining method can be only applied to food contact surfaces, but not food materials.

2.6. Future work:

In this study, the crystal violet staining on silicone rubber may cause an irreversible effect. Future study can focus on optimizing the crystal violet staining method on studying the biofilm formation on silicone rubber, and this can be achieved by either changing another staining solution or changing the decolorizing solution. For plate

29 count method, we applied sonication method to remove the biofilm from the coupons, however, the effect of sonication on Gram-negative bacteria has not been studied, and future study can focus on removing the biofilm formed by Gram-negative bacteria by sonication method. Besides, future study can also focus on the biofilm formation on other materials such as plastic used in other industries such as poultry industry.

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33 Chapter 3: Inactivation of Listeria innocua biofilm grown on stainless steel

coupons by chlorine dioxide gas

Abstract:

Biofilm is a major concern in food industry. When the biofilm is formed on food processing equipment surfaces, it can detach from the surfaces and contaminate the food product. The biofilm can also affect the heat transfer and cause energy loses.

The biofilm is protected by extracellular polymeric substance and makes it become more resistant to various sanitizing methods. It is essential to develop an efficient method to inactivate the biofilm on food-contact surfaces in a food processing plant.

Stainless steel is widely used in food processing plant, therefore, it was selected for the biofilm inactivation studies. Listeria innocua was chosen as model bacteria. The biofilm inactivate studies were at various chlorine dioxide gas concentrations for different exposure times at 2 mg/L: 3 min, 6 min, 12 min, 20 min, 40 min; 3 mg/L: 1 min, 5 min, 10 min, 15 min, 30 min; 5 mg/L: 0.5 min, 1 min, 3 min, 5 min, 10 min,

20 min; 7 mg/L: 1 min, 2 min, 3 min, 5 min, 10 min. The highest inactivation achieved in this study was 2.01-log reduction at 7 mg/L. Weibull model described the data significantly better than log-linear mode. Based on Weibull model prediction,

34 for a 5-log reduction of biofilm viability, an exposure time of 214 minutes is required. The exposure time can be reduced by applying chlorine dioxide gas at higher concentrations than 7 mg/L

Key words: chlorine dioxide gas; stainless steel; Listeria innocua biofilm; Weibull model

35 3.1. Introduction:

A biofilm is defined as “aggregate of microorganism in which cells are embedded within a self-produced matrix of extracellular polymeric substance (EPS) adhere to each other and/or to a surface” by International Union of Pure and Applied Chemistry

(IUPAC) (Vert et al., 2012). The formation of biofilm follows several steps. Bacteria first attaches to a variety of surfaces found in food processing plants including stainless steel, rubber, glass and polypropylene. Then layers of bacteria grow embedded in EPS to form biofilm. Biofilm can form under low nutrition environment, it can even grow in the pipes transporting underground water (Maharjan et al., 2017).

Bacteria can attach to the surfaces colonized by other bacteria and form mix-species biofilm (Hassan et al., 2004). As biofilm forms, the bacteria can detach from the surfaces and contaminate food-processing environments (Chmielewski et al., 2003;

Yang et al., 2016). The buildup of biofilm can affect the heat flow pattern and may also cause surface corrosion and a loss of energy in food processing system (Kumar et al., 1998).

Biofilm developed on food-processing equipment surfaces were not often completely cleaned (Fatemi et al., 1999). Biofilm is highly resistant to antimicrobial agents such as hydrogen peroxide and monochloramine when compared to planktonic bacteria

(Beer et al., 1994; Cochran et al., 2000). The EPS matrix prevent the antimicrobial agents from contacting to cells in biofilm by reducing the penetration capability of antimicrobial agents, and bacteria still can maintain a slow growth rate during sanitization process (Vaid et al., 2010). Multiple phenotypes development and the

36 existence of dormant cells are the factors that reduce the efficacy of sanitizers (Kim et al., 2008).

In United States, the most frequently used sanitizers are hypochlorous acid, chlorinated water, iodine, ozone gas, hydrogen peroxide, peroxyacetic acid, quaternary ammonium chloride and anionic acids such as dodecyl benzene sulfonic acid (Grinstead et al., 2009). The disinfectants widely used today are chlorine-based, but it is unstable at high temperature. Peroxyacetic acid has a risk to corrode stainless steel surfaces and has a low sensitivity to organic matter (Wang et al., 2006). Iodine based sanitizer is the costly among all sanitizer listed, and they are sensitive to organic matter (Fontaine et al., 1988). Among innovative sanitizers, chlorine dioxide shows a high oxidation activity while inactivating both bacteria and bacteria spores with a broad antimicrobial spectrum.(Foegeding et al., 1986). Most chlorine dioxide applications occur in aqueous form, only few studies exists an investigation of the efficacy of gaseous chlorine dioxide (Trinetta et al., 2012). Han et al. (1999) used the chlorine dioxide gas to sanitize juice tank. When compared to chlorinated water, the advantage of using chlorine dioxide gas included formation of less toxic disinfection by-product (trihalomethanes and haloacetic acids), effectiveness at low concentration

(chlorine dioxide has 2.5-fold greater oxidizing ability than chlorinate water), shorter exposure time and applicability over a wide range of pH (3.0-8.0) (Chang et al.,

2000;). The disinfection mechanism of chlorine dioxide was proposed to be its reaction with fatty acid and other outer membrane substance and blocking protein synthesis (Bernarde, 1967), as well as increasing permeability of the outer cell

37 membrane (Aieta and Berg, 1986). Efficacy of chlorine dioxide gas to inactivate biofilms has not been studied in detail. Jang et al. (2006) reported that the 0.4 mM aqueous chlorine dioxide provide disinfection down to 100 µm in biofilm depth, which indicate that the chlorine dioxide may not be able to reach the bacteria inside a biofilm. They proposed to increase the concentration of chlorine dioxide solution and exposure time or introduce the chlorine dioxide gas to reach bacteria in biofilm.

The effect of various sanitizers on inactivation of the biofilm formed by using several bacteria is summarized in Table 3.1).

Table 3.1. Biofilm inactivation by various sanitizers on different surfaces. Exposure Log Bacteria Surfaces Sanitizer Concentration Reference time (min) reduction

Stainless steel 2.8 Salmonella Sodium Schlisselberg Mechanical 2.1 50 ppm 10 Typhimurium dichloroisoyanurate et al., 2013 Brightsanding alum 2.2

Electropolishestainless steel 4.4

d stainless 5 0.42 Sodium steel 10 0.72 100 ppm Escherichia. hypochlorite 30 1.42 Park et al., coli Stainless steel 50 2.56 2012 O157:H7 5 0.24 Peracetic acid 100 ppm 10 0.23

30 0.46

(Continued)

38 Table 3.1. (Continued) Bacteria Surfaces Sanitizer Concentration Exposure Log Reference

time (min) reduction 100 ppm 50 >3.77

5 2.98

10 >4.0 200 ppm 30 >3.94 Escherichia. coli Peracetic acid 50 >3.77 O157:H7 5 3.16

10 >4.0 400 ppm 30 >3.94

50 >3.77

5 1.12 Sodium 10 1.09 100 ppm hypochlorite 30 1.13 Stainless Park et al., 2012 50 0.50 steel 5 0.35

10 0.43 100 ppm 30 1.41 Salmonella 50 4.37 Typhimurium 5 3.83

10 >5.11 Peracetic acid 200 ppm 30 >4.95

50 >4.40

5 4.02

10 >5.11 400 ppm 30 >4.95

50 >4.40

(Continued)

Table 3.1. (Continued) Exposure Log Bacteria Surfaces Sanitizer Concentration Reference time (min) reduction

5 1.12 Sodium 10 1.43 100 ppm hypochlorite 30 2.32

50 2.46

5 0.50

10 0.66 100 ppm 30 2.28 Listeria Stainless Park et 50 >4.58 monocytogenes steel al., 2012 5 2.83

10 4.03 Peracetic acid 200 ppm 30 >4.59

50 >4.58

5 3.92

10 4.20 400 ppm 30 >4.59

50 >4.58

Free nitrous acid 0.4 mg N/L 360 1.15 Sewage Borosilicate Jiang et Hydrogen 90 mg/L 360 0.60 microbiota glass al., 2013 Freeperoxide nitrous acid 0.4 mg N/L & 360 2.04

& Hydrogen 90 mg/L

peroxide (Continued)

40 Table 3.1. (Continued) Bacteria Surfaces Sanitizer Concentration Exposure Log Reference

time 2(min) reduction1.53

4 1.68 Chlorine 0.3 mg/L 6 2.19 dioxide gas 8 3.16 Listeria Stainless Vaid et al., 10 3.21 monocytogenes steel 2010 2 2.29 Chlorine 4 3.07 dioxide 7 mg/L 6 3.07 solution 8 3.49

10 3.74

Table 3.1 shows that inactivation of biofilm may not be efficient at lower sanitizer concentrations and at shorter treatment times. The efficacy of biofilm inactivation depends on sanitizers and bacteria species. In general, a 5-log reduction is acceptable for industry purpose, however, seldom of the biofilm inactivation listed in Table 1.1 is higher than 5-log (Center for Food Safety and Applied Nutrition (U.S.). (1998)).

The inactivation of biofilm by chlorine dioxide gas need to be investigated further

Therefore, to find build a strategy to efficiently eliminate the biofilm is necessary.

Listeria innocua, is considered as a surrogate for L. monocytogenes. Therefore, it was selected as a model system to investigate the biofilm inactivation by ClO2 gas. L. innocua biofilm was grown on stainless steel coupons at 37°C for 51 hours, followed by exposure to chlorine dioxide gas at concentrations of 2 mg/L, 3 mg/L, 5 mg/L and

41 7 mg/L and various exposure times to be able to conduct kinetic studies. Then, the

Weibull model describing the kinetics of L. innocua biofilm inactivation as a function of ClO2 concentration and exposure time was fitted to experimental data to determine kinetic parameters.

3.2. Objective:

The objectives of this study were (1) to determine the effect of chlorine dioxide gas on inactivation of L. innocua biofilm on stainless steel coupons, (2) to develop a kinetic model describing the inactivation of L. innocua biofilm as a function of chlorine dioxide gas concentration and exposure time.

3.3. Materials and method:

3.3.1. Materials preparation:

Tryptone soya broth (TSB) (CM0129; Oxoid Ltd., Basinstoke, Hampshire, England) was prepared by mixing the TSB powder and DI water at ratio 3:10 (w/v) in a flask.

TSA media was prepared by mixing TSB media and agar (BP1423-500; Fisher

Scientific Co., Fair Lawn, New Jersey, USA) at ratio 100:15 (v/w). 0.85% saline was prepared by dissolving sodium chloride (S641-500; Fisher Scientific Co., Fair Lawn,

New Jersey, USA) in water at a ratio 0.85:100 (w/v). Sparkleen detergent solution was used to washing and cleaning the glassware and coupons for experiment and was prepared by adding a spoon of Sparkleen detergent powder (04-320-4; Fisher

Scientific Co., Pittsburgh, PA, USA) into 1 L of DI water. 70% ethanol which is used

42 for cleaning and sanitizing was prepared by diluting 96% ethanol (VXC-128; Volu-

Sol, Salt lake City, UT, USA) with DI water. Crystal violet powder (C0775-25G;

Sigma-Aldrich Co., St Louis, MO, USA) was used to prepare the crystal violet solution that was used for staining by mixing 1 g of crystal violet powder and 100 ml of DI water. 35% sodium chlorite solution and 6 N hydrochloric acid which were used for generating chlorine dioxide gas were prepared by dissolving the sodium chlorite (RDCS0440-500B1; Ricca Chemical Comp., Arlington, TX, USA) in DI water and diluting 12 N hydrochloric acid (7647-01-0; Fisher Scientific Co., Fair

Lawn, New Jersey, USA) with DI water at ratio 1:1 (v/v) respectively. TSA, TSB, and 0.85% saline were autoclaved at 121°C for 25 min prior to use.

3.3.2. Methods:

3.3.2.1. Stainless steel coupon handling:

Stainless steel #304 coupons were obtained from Food Science Department, The Ohio

State University (Columbus, OH, USA). Each coupon was soaked in Sparkleen detergent solution overnight. After soaking, the coupons was brushed carefully and then rinsed with 20 ml of DI water. The coupons were then exposed to UV-light in biological hood under UV-light, and were autoclaved at 121°C for 25 min.

3.3.2.2. Bacteria stains and inoculum preparation:

The L. innocua ATCC 33090 strain was obtained from Food Science Department,

The Ohio State University (Columbus, OH, USA). Bacteria inoculum was prepared

43 by transferring a loop of L. innocua colony into 200 ml sterile TSB media in a flask and was incubated at agitate speed of 150 rpm at 37°C for 20 hours in a water bath.

The bacteria concentration reached to about 3.5×109 CFU/ml. The concentration of bacteria inoculum was enumerated by plate count on TSA plates after serial dilutions using 0.85% saline. Bacteria stock solution was prepared by mixing the bacteria inoculum and 100% glycerol by 1:1 (v/v) in a 2.5 ml centrifuge tube. Bacteria stock was placed in -80°C freezer for future use.

3.3.2.3. Inoculation of bacteria on stainless steel coupon:

Stainless steel coupons were placed in 60×15 mm diameter petri dishes (14315006;

Corning, New York, USA) with lids on. All petri dishes and coupons were wrapped with aluminum foil and were autoclaved for at 121°C for 25 minutes. After autoclaving, each petri dish was sprayed with 70% (v/v) ethanol. Then, petri dishes were filled with 1 ml sterile water at the bottom and the top surfaces of coupons were inoculated with 300 µl of bacteria inoculum. A tray of water was placed inside the incubator to create a moisture environment to prevent the inoculum from drying during incubation. Petri dishes containing inoculated coupons were incubated at 37°C for three hours for attachment of bacteria.

3.3.2.4. Biofilm incubation:

After three hour’s attachment, the coupons were removed from petri dishes and were rinsed with 20 ml of sterile 0.85% saline by using a micropipette to remove planktonic cells. When the coupon surface became dry, 300 µl sterile fresh TSB

44 media was added on the coupon surface containing attached bacteria. The petri dishes with coupons were returned back to the incubator to incubate the biofilm at 37°C for

24 hours. Then, the process of rinsing and adding media was repeated and petri dishes were incubated for another 24 hours. The total incubation time for biofilm growth was 51 hours.

3.3.2.5. Gaseous chlorine dioxide system and treatment:

Chlorine dioxide gas was generated inside a 43.6 L polypropylene treatment chamber

(1600 Case; Pelican Products, Inc., Torrance, CA, USA) according to the reaction below:

5NaClO2 + 4HCl →5NaCl + 4ClO2 + 2H2O

The chlorine dioxide gas concentration was calculated based on previous data collected in our laboratory and shown in Table 3.2 (Yang, 2015). A sample chamber of 4 L was placed inside the treatment chamber. The sample chamber can be opened from outside when the ClO2 concentration inside the treatment chamber reached to a constant value. It took 20 minutes to reach a constant ClO2 concentration (Gehringer,

2013). The ClO2 gas treatment system is shown in Fig. 3.1.

Figure 3.1. Distribution of the log number of bacteria in the biofilm formed on stainless steel

Table 3.2. Parameters to generate chlorine dioxide gas by sodium chlorite and hydrochloric acid Chlorine dioxide concentration 35% NaClO dioxide (ml) 6N HCl (ml) 2 (mg/L)

1.364 0.516 2

2.046 0.774 3

3.410 1.290 5

4.774 1.806 7

All the coupons were placed in biological hood for 15 min to dry the surface because chlorine dioxide gas has a high solubility in water. The coupons were placed inside

46 the sample chamber and the chamber was sealed. Chlorine dioxide gas was then generated by mixing 35% sodium chlorite (NaClO2) and 6N hydrochloric acid (HCl) with a stir bar in a 10 ml beaker placed on a stirrer in the treatment chamber. After adding the reactants into the beaker, the treatment chamber was sealed immediately.

After 20 min, a constant concentration of chlorine dioxide gas was reached. Then, the sample chamber was opened using a mechanism build outside the treatment chamber and the biofilm inside sample chamber was exposed to chlorine dioxide gas. After waiting for the desired exposure time, the treatment chamber was opened and the chlorine dioxide gas was purged from the chamber. The treated sample was removed for future analysis. For the control group, instead of exposing the coupons to chlorine dioxide gas, the coupons were placed in the biological hood for the similar time. All the treatment or control experiments were done at room temperature (25°C~28°C).

3.3.2.6. Microbial enumeration:

The biofilm was analyzed as described in 2.2.5. Each coupon (control and treated) was placed inside a 100 ml of beaker with 30 of ml sterile 0.85% saline and was sonicated for 20 min. After biofilm was removed from coupons to 0.85% saline, serial dilution (10-1~10-4) was prepared with 0.85% saline. Each dilution (including 100) was transferred into TSA plates and spreaded evenly with spreaders (08-100-11;

Fisher Scientific). All the plates were incubated at 37°C for 24 hours. After incubation, bacteria number was enumerated by counting the colonies in plates on which has 20~200 colonies.

47 3.3.2.7. Modeling and analysis of data:

The log-survival model (Equation 3.1) and Weibull model (Equation 3.2) were used to describe the inactivation of L. innocua biofilm by chlorine dioxide gas in this study. Both models at each concentration were fitted to log-reduction versus time data by using Matlab software. The parameters of both models were determined by minimizing the sum of the square of differences between the experimental and fitted data.

Log-survival model:

log !! = −�×� (Equation 3.1) !!

Where

N0 is the bacteria initial number (CFU/ml),

Nt is the number of the bacteria after treatment (CFU/ml), t is the treatment time (min), k is rate constant (min-1).

Weibull model:

log !! = �×�! (Equation 3.2) !!

Where

48 N0 is the bacteria initial number (CFU/ml),

Nt is the number of the bacteria after treatment (CFU/ml), t is the treatment time (min), b and n are chlorine dioxide gas concentration dependent constants.

Secondly, the data was fitted to the Weibull model by both solver method in Excel and Matlab coding at each concentration. After the chlorine dioxide dependent parameters b and n for Weibull model at each concentration was calculated, b and n were described as a function of chlorine dioxide concentration. Finally, the Weibull model was rewritten to include the effects of both chlorine dioxide gas concentration and treatment time.

3.4. Results and Discussion:

The distribution of log bacteria number in untreated biofilm was analyzed by JMP software to determine the repeatability of biofilm formation (Fig. 3.2). No outlier was detected in this distribution, which indicates that the log bacteria number had a normal distribution and the reproducibility of biofilm incubation was precise in a 95% interval.

49 6.6 6.8 7 7.2 7.4 7.6 7.8 8

Figure 3.2. Distribution of the log number of bacteria in the biofilm formed on stainless steel

The results of the connecting letters report in all pairs Tukey-Kramer test shows that all control groups have the same letter, which indicates that the bacteria numbers in all control groups are not significantly different (Table. 3.3). Therefore, the biofilm formation on each coupon was in a good repeatability.

Table 3.3. Connecting letters report of the control groups in each batch Chlorine dioxide concentration Letters Average of the bacteria numbers in (mg/L), exposure time (min) control groups in each batch (CFU/ml) 7,2 A 56100000 7,3 A 56100000 5,1 A 55200000 5,10 A 55200000 5,20 A 55200000 7,1 A 37050000 5,0.5 A 22500000 (Continued)

50 Table 3.3. (Continued) Chlorine dioxide concentration Letters Average of the bacteria numbers in (mg/L), exposure time (min) control groups in each batch (CFU/ml) 5,3 A 22500000 5,5 A 22500000 2,40 A 21150000 3,30 A 21150000 2,20 A 20400000 3,15 A 20400000 7,5 A 20400000 7,10 A 18000000 3,10 A 15525000 2,6 A 13725000 3,1 A 9900000 3,5 A 9900000 2,12 A 9450000 2,3 A 9450000 Levels not connected by same letter are significantly different.

Four chlorine dioxide gas concentrations of 2 mg/L, 3 mg/L, 5 mg/L, and 7 mg/L were used to treat 51-hour L. innocua biofilm grown for 51 hours. The treatment time at each concentration varied as short as 30 seconds at 7 mg/L to 40 minutes at 2 mg/L. Each group was repeated two to three times. For each batch, there are three treatment groups and one control group (biofilm was exposed to air instead of chlorine dioxide gas. Control group is used for the pre-ClO2 treatment bacteria number in the biofilm. The average of control group was used to normalize the bacteria number treated with ClO2 gas on the coupons. In order to minimize system error which was generated by the differences between inoculum in each batch, the log reduction of each treatment was calculated by using the log bacteria number of

51 control group in each batch to minus the log bacteria number after treatment in the same batch. The data collected is shown in Table 3.4.

Table 3.4. Raw data of biofilm inactivation

ClO2 concentration (mg/L) Exposure time (min) log (N/N0) 0 0.00 3 -0.29 6 -0.36 2 12 -0.53 20 -0.88 40 -1.25 0 0.00 1 -0.04 5 -0.46 3 10 -1.12 15 -1.22 30 -1.43 0 0.00 0.5 -0.18 1 -0.43 5 3 -0.46 5 -0.89 10 -1.27 20 -1.35 0 0.00 1 -1.06 2 -1.13 7 3 -1.35 5 -1.86 10 -2.01

52 The highest log reduction was found to be 2.01 when the biofilm is exposed to 7 mg/L chlorine dioxide gas for 10 min. To find out the relationship between treatment time and biofilm inactivation at each concentration, the data was fitted to log-survival model (Equation 3.1) at first.

Figure 3.3. L. innocua biofilm inactivation at each chlorine dioxide concentration and the data was fitted in linear model. Zero point is added at each line.

Figure 3.3 shows that log reduction increases with time at each concentration and as the gas concentration increases the rate constant increases. Furthermore, with the higher chlorine dioxide concentration, shorter inactivation times for biofilm is required.

53 From Figure 3.3, a tail effect was observed at longer exposure time. The tail effects were obvious at 3 mg/L, 5 mg/L, and 7 mg/L concentrations, but not obvious at 2 mg/L. Therefore, the first order kinetic model may not be sufficient to describe the biofilm inactivation at a higher chlorine dioxide concentration. To find out the suitability of log-survival model to describe the biofilm inactivation deeply, the parameters in log-survival model are described in Table 3.5:

Table 3.5. Parameter for log-survival model at each chlorine dioxide concentration Chlorine dioxide k R2 concentration (mg/L) 2 0.0351 0.8025

3 0.0598 0.6066

5 0.0861 0.3983

7 0.2651 -1.6816

Since R2 at each model fitting is low, log-survival model is not suitable to describe the biofilm inactivation. The Weibull model (equation 3.2) which will take the shoulder effect and tail effect into account may be more suitable describe the L. innocua biofilm inactivation by chlorine dioxide gas. The Weibull model fitting at each ClO2 gas concentration is shown in Fig 3.4:

54 0

-0.5

-1

Log reduction -1.5

-2

-2.5 0 5 10 15 20 25 30 35 40 Time (min)

2 mg/L 3 mg/L 5 mg/L 7 mg/L 2 mg/L 3 mg/L 5 mg/L 7 mg/L 2 mg/L 3 mg/L 5 mg/L 7 mg/L error bar error bar error bar error bar line line line line point point point point

Figure 3.4. L. innocua biofilm inactivation at each chlorine dioxide concentration and the data was fitted in Weibull model. Zero point is added at each line.

Fig 3.4 shows that Weibull model describes biofilm inactivation by ClO2 gas better regarding the tail effect from the observation. A downward concavity of the inactivation curve at each concentration, which happens when n<1, indicates that the increasing of the efficacy of chlorine dioxide gas treatment become less prominent at longer exposure time. The existence of tail effect may because there the bacteria are normal distributed in term of the resistance to chlorine dioxide gas and the bacteria are enclosed deep inside the biofilm is protected by the EPS and outer bacteria from

ClO2 treatment. The parameters b and n were different at each concentration Table

(3.5) and were exhibited a concentration dependence (Fig 3.5).

55 An F-test was performed to determine if the Weibull model is significant better than linear model but not improving the data fitting by simply adding another parameter.

The equation for F-test was shown below:

!"" !!"" ( ! !) !!!!! � = !"" (Equation 3.3) ( !) !!!!

Where,

RSS1, the residue sum of square of linear model,

RSS2, the residue sum of square of Weibull model, p1, number of parameters in linear model, p2, number of parameters in Weibull model, n, the number of data points to estimate parameter,

The result of F-test to compare the linear model and Weibull model at each ClO2 concentration is shown in Table 3.5.

Table 3.6. F-value to compare the goodness of fitting of linear model and Weibull model ClO2 concentration F-value F (p -p , n-p ) (mg/L) 2 1 2 2 37.84 5.54 3 6.63 5.54 5 23.35 4.54 7 91.36 5.54

56 From the F-test results showed in Table 3.6, the F-value calculated is higher than the critical value in F distribution at each concentration, which indicates that the Weibull model is significantly better than the linear model at describing the biofilm inactivation as a function of chlorine dioxide gas concentration and exposure time.

Table 3.7. Parameters in Weibull model for each ClO2 gas concentration to describe biofilm inactivation Concentration (mg/L) 2 3 5 7

b -0.1262 -0.2539 -0.3857 -1.0042

n 0.6235 0.5364 0.4459 0.3161

The parameter, b, showed an exponential dependence on concentration while parameter, n, decreased linearly with increasing concentration. The decreasing of n with the increasing of ClO2 concentration indicates that the inactivation of biofilm became less time dependent at higher ClO2 concentration, while the same trend of b indicates that the inactivation become more efficient at higher ClO2 concentration.

57 0.8

0.6

0.4

0.2

-0.2

-0.4

-0.6

-0.8

-1

-1.2 0 1 2 3 4 5 6 7 Chlorine dioxide concentration (mg/L) b curve n line b point n point

Figure 3.5. b and n in Weibull model described as a function of chlorine dioxide concentration

The R2 is Figure 3.5 is much higher than that in log-survival model, which indicate b and n in Weibull model are described perfectly as a function of ClO2 gas concentration in exponential and linear format. Yang (2015) found that the parameter n was independent constant when the Weibull model was used to describe the

Escherichia coli K12 cells inactivation by chlorine dioxide gas on spinach leaves. In this study n was described as chlorine dioxide gas concentration dependent constant.

58 The difference of description of n in Weibull model between this study and Yang’s study is due to the inoculation surfaces and the form of the bacteria (planktonic and biofilm) were different.

The concentration dependence of parameters b and c were incorporated into the

Weibull model to describe bacterial biofilm inactivation as a function of both ClO2 gas concentration and exposure time as shown in Equation 3.4.

! ! !!! (!!!!!!) log = −�!� ×� (Equation 3.4) !!

Where

Nt, the number of bacteria at time t

N0, the initial number of bacteria t, exposure time (min)

a1, a2, a3 and a4, model constants

The constants (a1, a2, a3, and a4) are independent only to the treatment time, and it may dependent on the bacteria, sanitizers, temperature, moisture etc., which are not variables in this study.

To determine a1, a2, a3, and a4, all the data was fitted into the revised Weibull model in Matlab software. The model constants are shown in Table.3.7. The global fitting results are presented in Fig. 3.5.

59 Table 3.8. Independent constants a1, a2, a3, and a4 in Weibull model Estimate p-Value

a1 0.055657395 0.0311588 a2 0.413550227 0.00000887

a3 -0.069307959 0.009800198

a4 0.783401056 0.0000313

The p-values presented in Table 3.8 are smaller than 0.05, which indicates that constants a2, a3, and a4 are significant in a 95% interval. The final model to describe the L. innocua biofilm inactivation by chlorine dioxide gas on stainless steel coupons:

log !! = −0.0557�!.!"#$!×�(!!.!"#$!!!.!"#$)(Equation 3.5) !!

The highest inactivation achieved in this study was 2.01-log, which is too low to meet the demand of industry sanitizing. Therefore, a higher chlorine dioxide concentration and a longer treatment time should be applied. Previous study indicated that there is a

3.8-log reduction on planktonic L. monocytogenes when treated with 2 mg/L chlorine dioxide gas for 10 min (Trinetta et al., 2012). However, another study indicate that the 0.2 mg/L chlorine dioxide gas treated L. monocytogenes biofilm for 10 min can reach 3.21-log reduction, which indicate the biofilm is much more sensitive than planktonic cell to chlorine dioxide gas (Vaid et al., 2010). The results reported by

Vaid et al. are not in agreement with the results obtained in this study. Chlorine dioxide treatment at 2 mg/L for 10 min produced 0.53-log reduction of L. innocua biofilm. Biofilm is expected to be more resistant than planktonic cells, which is widely accepted in biofilm studies.

-1

-2

-3 Log(N/N0) -4

40 35 9 30 8 25 7 20 6 15 5 4 10 3 5 Time (min) 2 Conc (mg/L)

Figure 3.6. The log-reduction of L. innocua biofilm by chlorine dioxide gas as a function of chlorine dioxide concentration and treatment time

L. innocua biofilm appears to be more sensitive to a higher concentration of chlorine dioxide gas. Furthermore, at each concentration performed in this study, the inactivation curve slope decreases at longer treatment time, which may attributed to increased resistance of remaining bacteria cells to chlorine dioxide gas or bacteria being embedded deeper in the biofilm to which the chlorine dioxide gas cannot access. Therefore, in order to reach a higher inactivation, the best strategy is to increase the chlorine dioxide gas concentration.

61 To reach a higher log reduction, the chlorine dioxide gas concentrations and treatment time that are predicted from the model are listed in the Table 3.9.

Table 3.9. Calculated value for chlorine dioxide gas treatment of L. innocua biofilm on stainless steel coupons calculated from Weibull model fit to the data to reach a 5- log reduction Chlorine dioxide gas concentration Treatment time (min)

(mg/L)

2 296

3 286

5 260

7 214

10 54

11 0.08

12 15411

From Table 3.7, 2 mg/L, 3mg/L, 5 mg/L, and 7 mg/L chlorine dioxide gas concentrations require too long treatment time beyond practical application to inactivate L. innocua biofilm. When the concentration extrapolated beyond collected data range such as 10 mg/L, the desired treatment time becomes 54 min, which is more feasible. As shown in the Table 3.8, further increase of concentration to 11

62 mg/L and 12 mg/L, the model predictions become unstable, the parameters need to be estimated by using data set expanding to higher concentrations and treatment times.

In this study, we use the ultrasound method to remove the biofilm, this prevent us from detecting the biofilm lower than 2.78-log. If a higher inactivation value needs to be detected, a larger stainless steel coupon, which can form more biofilm, should be applied to this study.

3.5. Conclusion:

This study investigates the inactivation of L. innocua biofilm by ClO2 gas. The result indicates that a biofilm containing 7.36±0.38 log unit bacteria were formed on 24×24 mm stainless steel surfaces after 51 hours of incubation. The L. innocua biofilm showed 2.01 log reduction by chlorine dioxide gas when the biofilm is exposed to 7 mg/L chlorine dioxide gas for 10 min. Therefore, a higher chlorine dioxide concentration and longer exposure times should be tested to increase the inactivation.

Both log-survival model and Weibull model fitted to describe the L. innocua biofilm inactivation by chlorine dioxide gas as a function of chlorine dioxide gas concentration and exposure time showed biofilm inactivation significantly better due to the tail effect observed at longer times. 5-log biofilm can be obtained beyond the data range investigated in this study, such as 54 min treatment at 10 mg/L ClO2 concentration.

3.6. Future work:

63 Future study can focus on treating the biofilm at a higher chlorine dioxide concentration for a longer time to reach a higher inactivation value and verify the

Weibull model built in this study. During the experiment, it was observed that a wet biofilm attached to stainless steel coupon treated with chlorine dioxide gas showed significantly higher reduction. Future study on stainless steel coupons by controlling the water activity during the treatment should be conducted. Since the chlorine dioxide gas has a high solubility in water, it is expected to dissolve in the water available in biofilm creating a high concentration of chlorine dioxide solution. The effect of gas should be differentiated from the effect of high concentration ClO2 aqueous solution.

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67 Appendix A. Raw data of the experiments

Table A.1. Raw data of L. innocua biofilm inactivation by chlorine dioxide gas on stainless steel log log ClO2 log log log Exposu averag (contro concentra Bacteria number Bacteria number average (contr (treat re time e l- tion (control)/(CFU/ml) (treated) (contro ol)- ed)- (min) (treated treated (mg/L) l) SD SD ) ) 1.2 6.0 2.2 7.5 9E 0E 8E 0E 2 3 6.98 0.24 6.69 0.37 0.29 +0 +0 +0 +0 7 6 6 6 1.2 6.0 1.2 2.4 2.7 5.7 9.3 9E 0E 0E 0E 9E 0E 0E 6 7.14 0.25 6.77 0.26 0.36 +0 +0 +0 +0 +0 +0 +0 7 6 7 7 6 6 6 1.2 6.0 2.7 2.0 3.5 9E 0E 6E 0E 2E 12 6.98 0.24 6.44 0.12 0.53 +0 +0 +0 +0 +0 7 6 6 6 6 2.5 1.5 2.3 3.0 2E 6E 4E 6E 20 7.31 0.15 6.43 0.08 0.88 +0 +0 +0 +0 7 7 6 6 2.4 1.8 7.2 1.6 0E 3E 0E 8E 40 7.33 0.08 6.08 0.26 1.25 +0 +0 +0 +0 7 7 5 6 9.0 1.0 7.8 1.0 0E 8E 0E 2E 3 1 7.00 0.06 6.95 0.08 0.04 +0 +0 +0 +0 6 7 6 7 9.0 1.0 3.0 3.9 0E 8E 0E 2E 5 7.00 0.06 6.54 0.08 0.46 +0 +0 +0 +0 6 7 6 6 9.0 1.0 2.4 1.8 4.8 6.0 2.1 1.5 0E 8E 0E 3E 0E 0E 0E 0E 10 7.19 0.20 6.07 0.31 1.12 +0 +0 +0 +0 +0 +0 +0 +0 6 7 7 7 5 5 6 6 2.5 1.5 1.2 1.1 2E 6E 9E 7E 15 7.31 0.15 6.09 0.03 1.22 +0 +0 +0 +0 7 7 6 6 2.4 1.8 6.3 9.3 0E 3E 0E 0E 30 7.33 0.08 5.89 0.12 1.43 +0 +0 +0 +0 7 7 5 5 2.5 1.9 9.9 1.9 1E 9E 0E 5E 5 0.5 7.35 0.07 7.17 0.21 0.18 +0 +0 +0 +0 7 7 6 7 (Continued)

68 Table A.1. (Continued) log log ClO2 log log log Exposu averag (contro concentra Bacteria number Bacteria number average (contr (treat re time e l- tion (control)/(CFU/ml) (treated) (contro ol)- ed)- (min) (treated treated (mg/L) l) SD SD ) ) 3.6 7.4 9.3 1.7 1.8 2.4 1.9 0E 4E 0E 4E 0E 0E 2E 1 7.74 0.33 7.31 0.07 0.43 +0 +0 +0 +0 +0 +0 +0 7 7 7 7 7 7 7 2.5 1.9 6.0 9.6 1E 9E 0E 0E 3 7.35 0.07 6.89 0.14 0.46 +0 +0 +0 +0 7 7 6 6 2.5 1.9 3.6 2.2 1E 9E 3E 2E 5 7.35 0.07 6.47 0.15 0.89 +0 +0 +0 +0 7 7 6 6 3.6 7.4 2.8 3.0 0E 4E 2E 6E 10 7.74 0.22 6.47 0.03 1.27 +0 +0 +0 +0 7 7 6 6 3.6 7.4 2.2 2.7 0E 4E 2E 0E 20 7.74 0.22 6.39 0.06 1.35 +0 +0 +0 +0 7 7 6 6 5.8 5.3 1.2 2.4 3.4 6.0 1.9 1.5 4E 8E 0E 0E 3E 0E 2E 3E 7 1 7.57 0.32 6.51 0.27 1.06 +0 +0 +0 +0 +0 +0 +0 +0 7 7 7 7 6 6 6 6 5.8 5.3 3.0 5.2 4E 8E 0E 8E 2 7.75 0.03 6.62 0.17 1.13 +0 +0 +0 +0 7 7 6 6 5.8 5.3 2.1 2.8 4E 8E 0E 8E 3 7.75 0.03 6.40 0.10 1.35 +0 +0 +0 +0 7 7 6 6 2.5 1.5 2.4 3.1 2E 6E 3E 5E 5 ` 7.31 0.15 5.45 0.08 1.86 +0 +0 +0 +0 7 7 5 5 1.2 2.4 1.4 2.0 0E 0E 7E 1E 10 7.26 0.21 5.24 0.10 2.01 +0 +0 +0 +0 7 7 5 5

69 Table A.2. Raw data of L. innocua biofilm formation on stainless steel, borosilicate glass and silicone rubber materials detected by plate count method Number of bacteria on each Average of bacteria Standard deviation of coupon (CFU/ml) number bacteria number

6.40E+08

5.10E+08 Stainless steel 4.23E+08 1.86E+08 2.40E+08

3.00E+08

5.30E+08

4.40E+08 Borosilicate 3.83E+08 1.25E+08 glass 3.00E+08

2.60E+08

8.40E+08

1.04E+09 Silicone rubber 8.13E+08 1.97E+08 5.60E+08

8.10E+08

70 Table A.3. Raw data of L. innocua biofilm formation on stainless steel, borosilicate glass and silicone rubber materials detected by crystal violet staining method

Crystal violet (OD 595) Average (OD) Average (OD)-SD

1.01

0.77

0.92 Stainless steel 0.85 0.10 0.84

0.75

0.82

0.83

0.69

0.75 Borosilicate glass 0.72 0.07 0.72

0.67

0.64

1.13

1.02

1.05 Silicone rubber 1.03 0.08 0.92

1.10

0.96

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