Quantitative Microbiological Risk Assessment of Foodborne Salmonella in Bukavu City, Democratic Republic of Congo

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Okito, John M. Wagacha, Catherine W. Lukhoba, Franck P. Angbongbo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8640180/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Salmonella remains a primary cause of enteric disease. In the Democratic Republic of Congo (DRC), the lack of quantitative data on foodborne Salmonella exposure limits effective risk assessment and food safety management, underscoring the necessity for robust monitoring tools. Here, we applied a quantitative microbiological risk assessment (QMRA) to estimate the risk of Salmonella exposure through contaminated foods in Bukavu. Food samples were collected from multiple points of sale and analyzed using culture-based microbiological methods. The QMRA integrated Salmonella loads, exposure doses, dose–response modeling, and risk characterization to estimate Salmonella infection probabilities across varying consumption scenarios and exposure durations. High Salmonella loads were detected, with exposure doses increasing proportionally with food consumption, and Salmonella infection risk rising with increasing dose. This study provides the first QMRA-based estimates of foodborne Salmonella risk in the DRC and introduces a pathway framework that incorporates environmental conditions and cross-contamination at the food establishment level. These findings offer a practical evidence base to inform food safety monitoring and reduce the burden of foodborne disease in resource-limited settings. Health sciences/Diseases Earth and environmental sciences/Environmental sciences Biological sciences/Microbiology Health sciences/Risk factors Quantitative microbiological risk assessment foodborne Salmonella exposure dose-response modeling Bukavu Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The widespread consumption of ready-to-eat and street-vended foods under poor sanitation and hygiene conditions in the Democratic Republic of Congo (DRC) increases the risk of foodborne Salmonella infection 1 , 2 . There is high prevalence of Salmonella in human as well as animal, food, and water samples analyzed in DRC 3 , 4 . Despite the high prevalence of Salmonella in contaminated food samples in DRC, dose-response relationships and risk characterization of foodborne Salmonella infection remain poorly defined. The presence of Salmonella in food does not consistently result in infection, as contaminated products may be consumed without causing illnes 5 . The probability of infection depends on the ingested microbial dose and the duration of exposure 6 , 7 . Clinical Salmonella infection symptoms occurs when exposure exceeds a dose–response threshold; below this threshold, infection may not occur 8 . However, because Salmonella is an opportunistic pathogen, host-related factors such as immunodeficiency and malnutrition can increase Salmonella ability, enabling infection even at lower exposure doses 8 , 9 . Countries therefore require a robust quantitative microbiological risk assessment (QMRA) framework to provide a sound scientific basis for policy development and decision-making 6 , 10 . Implementing QMRA at national, subnational, or city levels enables accurate estimation of dietary risks to limit foodborne pathogen transmission. Such approaches can reduce pathogen exposure doses and lower the incidence of infectious diseases 5 , 7 . Beyond public health benefits, improved food safety may enhance product marketability in Bukavu City, with potential incomes and livelihoods at local and national levels 11 . This study has addressed a critical gap in food safety research in DRC by applying a QMRA to foods consumed in Bukavu City. It was guided by two central questions: i.“In the event food consumed in Bukavu City is contaminated with Salmonella species, what is the risk of contracting a Salmonella infection?”; ii. “How likely is a consumer of ready-to-eat food or street-vended food sold in Bukavu food establishments to get infected from exposure to Salmonella over a given period?”. This study therefore aimed to quantify Salmonella loads and exposure doses in food samples, estimate dose–response relationships associated with Salmonella infection, characterize the daily, weekly, monthly, and annual Salmonella infection risks based on consumption levels, and develop a pathway framework for routine Salmonella surveillance. The study revealed overlooked sources and drivers of foodborne infection risk in Bukavu City and provided evidence to support risk-based food safety policies and targeted interventions to reduce dietary exposure and disease transmission. Results Microbiological quality and Salmonella load in food samples The findings revealed widespread microbial contamination, affecting 90.4% of food samples (n = 415), while 9.6% presented no risk for human health. The rest of food samples posed low to high health risk for human consumption (Table 1 ). Table 1 Risk classification of food sampled according to the World Health Organization and the “ Office Congolais de Controle ” (OCC) standards. Risk classification Food samples n % Acceptable contamination, No risk 40 9.6 Acceptable contamination, Low risk 40 9.6 Polluted, Moderate risk 305 73.5 Highly contaminated, High risk, 30 7.2 Total 415 100 The average most probable number (MPN) values of Salmonella in 1 g and 100 g of food was as follows: beans (19 and 1900), beef (4.6 and 460), fish (23.2 and 2320), bread (25.8 and 2580), peanut (3.1 and 310), pork (34.4 and 3440), potato (22.4 and 2240), rice (34.2 and 3420), “ samosa” (36.4 and 3640), sausage (37.3 and 3730), ‘ ugali” (58.4 and 5840), and vegetables (31.3 and 3130) (Table 2 ). The Kruskal-Wallis test revealed no significant difference ( P > 0.01) in MPN values according to the type of food consumed. Table 2 Summary of Salmonella load in 12 food types sampled from Bukavu City, DRC. Quantity Min.* Mean Max.* Min. Mean Max. Min. Mean Max. Beans Beef Fish MPN*/1g 0.00E + 00 1.90E + 01 4.30E + 01 0.00E + 00 4.60E + 01 1.50E + 02 0.00E + 00 2.32E + 01 9.30E + 01 MPN/100g 0.00E + 00 1.90E + 03 4.30E + 03 0.00E + 00 4.60E + 03 1.50E + 04 0.00E + 00 2.32E + 03 9.30E + 03 Bread Peanut Pork MPN/1g 0.00E + 00 2.58E + 01 1.50E + 02 0.00E + 00 3.10E + 01 2.40E + 02 0.00E + 00 3.44E + 01 2.40E + 02 MPN/100g 0.00E + 00 2.58E + 03 1.50E + 04 0.00E + 00 3.10E + 03 2.40E + 04 0.00E + 00 3.44E + 03 2.40E + 04 Potato Rice Samosa a MPN/1g 0.00E + 00 2.24E + 01 9.30E + 01 0.00E + 00 3.42E + 01 1.50E + 02 0.00E + 00 3.64E + 01 2.40E + 02 MPN/100g 0.00E + 00 2.24E + 03 9.30E + 03 0.00E + 00 3.42E + 03 1.50E + 04 0.00E + 00 3.64E + 03 2.40E + 04 Sausage Ugali b Vegetable MPN/1g 0.00E + 00 3.73E + 01 2.40E + 02 0.00E + 00 5.84E + 01 2.40E + 02 0.00E + 00 3.13E + 01 1.50E + 02 MPN/100g 0.00E + 00 3.73E + 03 2.40E + 04 0.00E + 00 5.84E + 03 2.40E + 04 0.00E + 00 3.13E + 03 1.50E + 04 a “ Samosa” = popular pastry that is typically triangular in shape filled with spiced vegetables, potatoes, or meat, and is deep-fried until crispy. b “ Ugali ” = staple food in Eastern and Central Africa made from maize or cassava flour mixed with boiling water to form a thick porridge and is typically served with meat or vegetable stews. * MPN = Most Probable Number of microorganisms in 1g and 100g. Min.=minimum. Max.=maximum. Estimation of exposure dose and dose-response of Salmonella infection in food The average exposure dose of Salmonella in food varied from 21.7 MPN in 1 g of beans to 64032.3 MPN in 1000 g of Ugali (Table 3 ). Table 3 Estimated exposure dose of Salmonella in 12 food types sold in Bukavu City, DRC. Quantity Min Mean Max Min Mean Max Min Mean Max Min Mean Max Beans Beef Fish Sausage 1g 3.6 21.7 43 3.6 52.1 150 3.6 25.5 93 3.6 39.6 240 10g 36 216.7 430 36 521.4 1500 36 254.8 930 36 395.8 2400 25g 90 541.7 1075 90 1303.5 3750 90 637.1 2325 90 989.5 6000 100g 360 2166.7 4300 360 5214 15000 360 2548.4 9300 360 3958.1 24000 500g 1800 10833.3 21500 1800 26070 75000 1800 12741.9 46500 1800 19790.6 120000 1000 g 3600 21666.7 43000 3600 52140 150000 3600 25483.9 93000 3600 39581.3 240000 Bread Peanut Pork Ugali 1g 3.6 28.3 150 3.6 34 240 7.4 36.5 240 11 64 240 10g 36 282.8 1500 36 339.7 2400 74 365.1 2400 110 640.3 2400 25g 90 706.9 3750 90 849.2 6000 185 912.8 6000 275 1600.8 6000 100g 360 2827.7 15000 360 3396.8 24000 740 3651.3 24000 1100 6403.2 24000 500g 1800 14138.7 75000 1800 16983.9 120000 3700 18256.3 120000 5500 32016.1 120000 1000 g 3600 28277.4 150000 3600 33967.7 240000 7400 36512.5 240000 11000 64032.3 240000 Potato Rice Samosa Vegetable 1g 7.4 25.3 93 3.6 37.5 150 3.6 45.9 240 3.6 32.3 150 10g 74 253.4 930 36 375 1500 36 458.9 2400 36 322.7 1500 25g 185 633.5 2325 90 937.4 3750 90 1147.2 6000 90 806.8 3750 100g 740 2534 9300 360 3749.7 15000 360 4588.9 24000 360 3227.3 15000 500g 3700 12670 46500 1800 18748.4 75000 1800 22944.4 120000 1800 16136.4 75000 1000 g 7400 25340 93000 3600 37496.8 150000 3600 45888.9 240000 3600 32272.7 150000 The dose-response was positively correlated with the exposure dose (Figs. 1 and 2 ). An increase in Salmonella load corresponded to a higher dose-response probability of Salmonella infection. In 1 g, 10 g, and 25 g of food, the average dose-response probability was from 14.8% in beans to 98.3% in Ugali . From 100 g to 1000 g, the probability ranged from 99.3% to 100% in all types of food analyzed. The dose-response from exponential modeling (Fig. 1 ) was significantly different (Wilcox test = 13604, P ≤ 0.01) from the dose-response using beta-Poison modeling (Fig. 2 ). Risk characterization of Salmonella infection associated with contaminated food Based on the beta-Poison modeling, estimated daily and weekly risk of Salmonella infection from 1 g to 25 g of food varied from 46.6% in beans to 96.6% in rice. This estimation exceeded 99% over one month for all types of food (Fig. 3 a, 3 c, 3 e). Exponential modeling estimated the risk of Salmonella infection from 14.8% in 1 g of beans to 80% in other types of food on a daily and weekly basis, respectively. The monthly and annual risks are nearly 100% for all type of food (Fig. 3 b, 3 d, 3 f). No significant associations (Kruskall-Wallis, P > 0.01) were observed between Salmonella infection risk and types of food. Consuming 100 g to 1000 g of food was associated with Salmonella infection risk ranging from 90% to 100% under daily, weekly, monthly, and annual exposures scenarios using the beta-Poison modeling (Fig. 4 g, 4 i, 4 k). The exponential modeling estimated a constant Salmonella infection risk of 100% regardless of exposure period (Fig. 4 h, 4 j, 4 l). Conceptual pathway framework for food safety monitoring under Bukavu conditions Discussion The quantitative microbiological risk assessment (QMRA) highlighted critical gaps in food safety monitoring in Bukavu City. Most food samples were unsafe for human consumption because of high microbial loads, independent of type of food. High Salmonella contamination observed may likely be explained by the proximity of ready-to-eat food to inadequate sanitation infrastructure, exposure to dust and waste, and sub-optimal hygiene practices in food vending environments. In addition, insufficient cooking temperature, poor microbiological quality of water used in food preparation, inadequate personal hygiene of food handlers, and cross-contamination during food handling may have considerable influence on the Salmonella load in food 12 . The findings of this study are consistent with previous research conducted in the Democratic Republic of Congo (DRC). In Bukavu City, bacterial loads ranging from 2 x 10 5 to 1 x 10 6 CFU/10g in roasted street-vended fish from Bukavu markets 2 , reflecting poor hygiene during food processing. The similarity between these results and our findings indicates persistent overlooked sources of foodborne Salmonella with a high potential for causing illness. These observations highlight the need for innovative dietary risk mitigation strategies. Exposure dose of Salmonella in food estimated using the most probable Number (MPN) method, showed a dose-dependent increase in Salmonella infection risk. Dose–response analyses indicated that higher Salmonella loads in food considerably increased Salmonella infection probability, with consumption of just 1 g of contaminated food associated with risks of 15.1%–31.3%. Typical daily food consumption exceeding 25 g returned estimated infection risks above 70%, indicating substantial population exposure in Bukavu City. These estimates are consistent with the high burden of Salmonella -associated illness reported by local healthcare facilities 13 – 15 . Because many Salmonella infections remain clinically asymptomatic 5 , 8 , a substantial hidden burden likely exists in Bukavu City. The beta–Poisson modeling produced more realistic risk estimates than the exponential modeling, supporting its use for dietary risk assessment under Bukavu conditions. Given the absence of dose–response studies on microbial infection in DRC, our findings were compared to studies from other countries. The Salmonella loads observed in our study were lower than those reported in Brazil, where Salmonella Enteritidis concentration was up to 4.6 x 10 9 MPN/g in potatoes, with higher concentrations in sausage, cassava, and roasted beef, largely attributed to inadequate temperature control during storage and cross-contamination, and linked to the occurrence of salmonellosis outbreaks 16 . In the United States, a study reported that 1 x 10 3 CFU/g of Salmonella initiated infection in 50% of broiler chicken consumers 8 . Experimental studies in mice indicated lethal doses ranging from 1.7 × 10⁶ to 2.0 × 10⁹ cells of Salmonella Typhimurium 17 . For healthy people, Salmonella infectious doses between 10⁶ and 10⁸ CFU/100 mL have been reported 18 . Collectively, previous studies highlighted the high infectious potential of Salmonella species and serotypes, even at low concentrations, notably in individuals with a weak immune system such as infants, young children, immunodeficiency patients and the elderly 8 , 18 . At the time of this study, research in the DRC addressing exposure dose, dose–response relationships, and microbial risk characterization remained limited. This gap underscores the need for comprehensive studies that integrate key parameters into predictive models for foodborne Salmonella detection. Pilot applications of mathematical dose–response modeling, such as the present study, are therefore fundamental. A study supported that robust models should incorporate serotype-specific variability of Salmonella , environmental drivers of contamination, and diverse exposure scenarios to improve infection risk prediction 19 . Such approaches enable the development of locally informed theoretical and practical frameworks, while recognizing the non-zero infection risk associated with any Salmonella dose 20 , 21 . Without targeted control strategies, exposure dose and dose–response dynamics will continue to challenge foodborne disease prevention efforts. The characterization of foodborne Salmonella infection risk was estimated on daily, weekly, monthly, and annual bases. The Salmonella infection probability increased with dose–response and exposure frequency. Consumption of 1 g of contaminated food resulted in 73% daily or weekly risk, while regular consumption over one month decidedly increased risk. Consuming 100 g of contaminated food represented 100% of Salmonella infection risk regardless of exposure duration. This indicates the constant threat posed by any contact with contaminated food. Our findings on risk characterization of Salmonella infection aligned with previous studies. A study attributed illness cases to consumption of low proportions (0.3% and 0.6%) of Salmonella –contaminated products 19 . Similarly, another study linked environmental contamination to 10% of Salmonella infection risk 8 . Stathas et al. reported 6.6% of annual Salmonella infection probabilities from low-dose exposure (3.57 x 10 − 4 CFU/g) 6 . Collectively, these findings underscored the high infectivity of Salmonella regardless of quantity or type of food consumed. Using trend analyses linking environmental conditions and food handling practices with Salmonella load and infection risk, our study proposes a pathway framework for food safety monitoring in Bukavu City. The framework integrates environmental drivers, hygiene practices, and cross-contamination processes to predict contamination across food production and vending stages. Akil and Ahmad reported approximately 12% Salmonella infection during food production and along the food supply chain 8 . Stathas et al. emphasized the need for monitoring during food processing, storage, and preparation 6 . Ovuru et al . reported that the pathogen prevalence in slaughterhouses reflects poor hygiene 22 . Consistent with evidence of substantial Salmonella prevalence along food supply chains, the proposed framework prioritizes upstream monitoring at slaughterhouses, markets, fisheries, and food establishments to reduce contamination at its source. Our proposed pathway framework is comparable to the farm-to-fork quantitative model of Akil and Ahmad 8 but differs methodologically by incorporating qualitative variables. Designed for data-limited Bukavu City, our pathway framework provides a foundation for future dose–response relationship and microbial risk characterization studies in DRC. Complementary public health measures, including food-handler vaccination, may further enhance prevention 23 , 24 . These findings underscore the urgent need for integrated, risk-based monitoring frameworks that address environmental and operational drivers of foodborne contamination, given that environmental factors could soundly influence the spread of microorganisms 25 , 26 . Materials and methods Isolation and enumeration of Salmonella Salmonella strains analyzed in this study were isolated from 415 food samples collected from randomly selected food establishments, including restaurants and street-vended food outlets in Bukavu City, DRC (Fig. 1 ). Samples were transported in a cooler box to the Microbiology and Biotechnology Laboratory at the “ Université Officielle de Bukavu ” within four hours of sampling. For microbiological analysis, 25 g of each food sample were aseptically transferred into 225 mL of peptone water (PW) and homogenized for 1 minute. A 10-fold serial dilution up to 10 − 8 was then prepared by transferring 1 mL of the homogenate into 9 mL of PW. All dilutions were prepared in triplicate and incubated at 37°C for 24 h. From positive presumptive tubes, 1 mL was transferred into 10 mL of Tryptone Water Broth (TWB), a selective enrichment medium for Salmonella species and incubated at 37°C for 24 h. Subsequently, 1 mL of the enriched culture was streaked onto Hektoen Enteric Agar (HEA) and incubated at 37°C for 24 h. One to three presumptive Salmonella colonies were then sub-cultured onto Salmonella–Shigella (SS) agar and incubated at 37°C for 24 h. Plates showing no growth were re-incubated to confirm the absence of Salmonella species. Biochemical characterization included tests for citrate utilization, catalase and urease activities, indole production, glucose and lactose fermentation, hydrogen sulfide production, and motility, enabling discrimination of Salmonella from other enteric bacteria 16 , 27 . Among triplicate tubes, positive results were used to estimate the most probable number (MPN) of Salmonella per gram of food. MPN values were determined following the U.S. Food and Drug Administration Bacteriological Analytical Manual 28 , 29 , and associated occurrence probabilities were calculated using the U.S. Environmental Protection Agency online MPN Calculator (version 2.0). Quantitative microbiological risk assessment of foodborne Salmonella The Quantitative Microbiological Risk Assessment (QRMA) involved four main stages: hazard identification, exposure dose assessment, dose-response estimation, and risk characterization of Salmonella infection 19 , 30 . The hazard identification for risk assessment focused on Salmonella because of its high prevalence in DRC, established correlation with meat consumption in Bukavu City, and low minimum infective dose. The exposure dose assessment involved calculating the MPN of Salmonella in 1 g of food, then extrapolate it to predict the Salmonella dose in 10, 25, 100, 500 and 1000 g, applying the following equation : d = MPN in 1 g x Q. Where d represents the exposure dose of Salmonella. MPN means the most probable number of Salmonella cells in 1 g of food. Q represents the amount of contaminated food consumed. The dose-response estimation involved calculating the probability of Salmonella infection after ingesting a given dose of Salmonella . The exponential and beta-Poison models were applied to calculate the dose-response probability for Salmonella infection as follows: Exponential model: P inf = 1-exp (-rd) and beta-Poisson model: P inf = 1 – {1+ [d/N 50 (2 1/α – 1)]} –α . Where P inf represents the probability of infection from consuming one meal per day; d is the exposure dose or the number of viable Salmonella ingested; r is the probability of ingesting at least one Salmonella cell and getting infected; N 50 is the dose at which there is 50% probability of getting infected after consuming contaminated food; and α is a pathogen-specific parameter describing variability in susceptibility of a human against Salmonella . Each microbial species has distinct values for N 50 and α. For Salmonella species, r = 0.00752, α = 0.313, and N 50 = 23.600 5,9 . The risk characterization consisted of integrating results from the first three stages: the exposure dose of Salmonella measured at diverse quantities (1, 10, 25, 100, 500 and 1000 g) of food; the duration of exposure (daily, weekly, monthly, annual), and the dose-response probability estimated. Then the following equation was applied : Pt = 1 – (1 – P inf ) n . Where Pt represents the probability of infection after a given time according to the n number of exposures during the considered time. The risk characterization of Salmonella infection after consuming a contaminated food in Bukavu City was calculated as follows: Probability of infection from a single day with one exposure per day (n = 1): Pt = 1 – (1 – P inf ) 1 Probability of infection over one week with one exposure per day (n = 7): Pt = 1 – (1 – P inf ) 7 Probability of infection over one month with one exposure per day (n = 30): Pt = 1 – (1 – P inf ) 30 Probability of infection over one year with one exposure per day (n = 365): Pt = 1 – (1 – P inf ) 365 Designing pathway framework for food safety monitoring under Bukavu conditions A conceptual pathway framework for food safety monitoring was designed to assess the potential Salmonella contamination at each stage of the food handling, from production to consumption in food establishments. Salmonella occurrence was treated as a dependent variable, while environmental conditions and cross-contamination during food handling were considered as independent variables. Food contamination possibility by Salmonella was classified as present (“Yes”) when conditions favored the introduction or persistence of Salmonella and absent (“No”) when conditions inhibited or eliminated the pathogen. Data analysis Statistical analyses were performed using R software (version 4.4.0) 31 . Data normality was assessed with the Shapiro-Wilk test. As MPN values were not normally distributed, non-parametric tests were used. The Kruskal-Wallis test assessed the association of Salmonella load with the type of food. Wilcox test was applied to compare the result between exponential and beta-Poison modeling. The mean differences were considered significant at P ≤ 0.01. Bacteriological quality of food was assessed against the World Health Organization (WHO) and the “ Office Congolais de Contrôle ” (The National Office for Standardization in DRC) standards. Risk characterization assumed that individuals consumed food once a day. Dose-response models, using estimated exposure doses, calculated the probability of infection per gram of contaminated food. This probability was extrapolated to estimate the daily, weekly, monthly, and annual risks of Salmonella infection. Declarations Conflict of Interest Authors declare there is no conflict of interest concerning this publication. Funding The first author is a lecturer at “ Université Officielle de Bukavu ” in DRC whom PhD study was partially supported through a capacity building competitive training grant, the next generation of scientists funded by Carnegie Cooperation of New York through the Regional Universities Forum (RUFORUM) for capacity building in agriculture. Author Contribution Conceptualization: A.M.O. and J.M.W. Methodology: A.M.O., J.M.W. and F.P.A. Formal Analysis: A.M.O., J.M.W. and F.P.A. Investigation: A.M.O., F.P.A. and A.A.L. Resources: A.M.O. Data curation: A.M.O. and F.P.A. Writing original draft: A.M.O. Review and Editing: J.M.W., C.W.L, W.R.M., A.A.L., F.P.A, A.M.O. Supervision: J.M.W., C.W.L, W.R.M. and A.A.L. Funding acquisition: A.M.O. All authors have read and agreed to publish this manuscript. Acknowledgement We specially thank all students of “Université Officielle de Bukavu” who assisted during the fieldwork and sample collection. Data Availability Data generated in this study will be shared on request. References Tack, B. et al. Developing a clinical prediction model to modify empirical antibiotics for non-typhoidal Salmonella bloodstream infection in children under-five in the Democratic Republic of Congo. BMC Infect. Dis. 25 , 122. https://doi.org/10.1186/s12879-024-10319-x (2025). Ombeni, B. J. et al. 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Slaughterhouse facilities in developing nations: Sanitation and hygiene practices, microbial contaminants, and sustainable management system. Food Sci. Biotechnol. 33 , 519–537. https://doi.org/10.1007/s10068-023-01406-x (2024). Nazir, J. et al. Combatting Salmonella : A focus on antimicrobial resistance and the need for effective vaccination. BMC Infect. Dis. 25 (1), 84. https://doi.org/10.1186/s12879-025-10478-5 (2025). MacLennan, C. A. et al. Salmonella combination vaccines: Moving beyond typhoid. Open. Forum Infect. Dis. 10 , S41–S45. https://doi.org/10.1093/ofid/ofad041 (2023). Tack, B. et al. Direct association between rainfall and non-typhoidal Salmonella bloodstream infections in hospital-admitted children in DR Congo. Sci. Rep. 11 , 21617. https://doi.org/10.1038/s41598-021-01030-x (2021). Larsson, D. G., Flach, C. F. & Laxminarayan, R. Sewage surveillance of antibiotic resistance holds both opportunities and challenges. Nat. Rev. Microbiol. 21 , 213–214. https://doi.org/10.1038/s41579-022-00835-5 (2022). Rahman, M. A. et al. Isolation, identification, and antibiotic sensitivity pattern of Salmonella sp. from locally isolated egg samples. Am. J. Pure Appl. Bioscience . 1 (1), 1–11. https://doi.org/10.34104/ajpab.019.019111 (2019). Meléndez, K. F. et al. MicroMPN: Methods and software for high-throughput screening of microbe suppression in mixed populations. Microbiol. Spectr. 12 (3), e03578–e03523. https://doi.org/10.1128/spectrum.03578-23 (2024). FDA (Food and Drug Administration). Bacteriological Analytical Manual: Appendix 2 - Most Probable Number Determination from Serial Dilutions (8th ed.). (2023). https://www.fda.gov Chen, J., Karanth, S. & Pradhan, A. K. Quantitative microbial risk assessment for Salmonella : Inclusion of whole genome sequencing and genomic epidemiological studies, and advances in the bioinformatics pipeline. J. Agric. Food Res. 2 , 100045. https://doi.org/10.1016/j.jafr.2020.100045 (2020). R Core Team. A language and environment for statistical computing. R Foundation for Statistical Computing (2024). https://www.R-project.org/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 29 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor invited by journal 27 Jan, 2026 Editor assigned by journal 23 Jan, 2026 Submission checks completed at journal 23 Jan, 2026 First submitted to journal 19 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8640180","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":609746690,"identity":"ecd3ae1e-fa94-46b3-9587-74903474d108","order_by":0,"name":"Alain M. Okito","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACHsYGMM3G3nwASEnIEKOlEayHj+dYAkgLDxFaGCDWyEnkGED4hAA/z+H2Bx9zbOzZGHI+v7pRY8HDwH746AZ8WiR7GxsbZ25LS2xjOLvNOucY0GE8aWk38GkxOM/Y2My77XACG2PvNuMcNqAWCR4zorTYszHzPDPO+UeMlrONYC2MbWw8zI9z24jQItlzsHEm2C88bGbMuX0SPGyE/MLPk/7gw8dtNvby8x8//pzzrU6On/3wMbxakAGbBJgkVjkIMH8gRfUoGAWjYBSMHAAABFhGJDfM9TgAAAAASUVORK5CYII=","orcid":"","institution":"Université Officielle de Bukavu","correspondingAuthor":true,"prefix":"","firstName":"Alain","middleName":"M.","lastName":"Okito","suffix":""},{"id":609746692,"identity":"58d78165-4fb4-4c94-85fc-dab1449c1e24","order_by":1,"name":"John M. Wagacha","email":"","orcid":"","institution":"University of Nairobi","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"M.","lastName":"Wagacha","suffix":""},{"id":609746693,"identity":"3ec136d7-30e1-40c7-ae01-6dc0be19a893","order_by":2,"name":"Catherine W. Lukhoba","email":"","orcid":"","institution":"University of Nairobi","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"W.","lastName":"Lukhoba","suffix":""},{"id":609746694,"identity":"ab864188-f3bc-48cd-921c-a3d32d3648bf","order_by":3,"name":"Franck P. Angbongbo","email":"","orcid":"","institution":"Université Officielle de Bukavu","correspondingAuthor":false,"prefix":"","firstName":"Franck","middleName":"P.","lastName":"Angbongbo","suffix":""},{"id":609746696,"identity":"1ebfcc9d-4da6-438c-a547-ad6bf2713a68","order_by":4,"name":"Alex A. Lina","email":"","orcid":"","institution":"Université Officielle de Bukavu","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"A.","lastName":"Lina","suffix":""},{"id":609746697,"identity":"afd3cb66-1f39-4418-ab8b-ed088ce9bb9c","order_by":5,"name":"Wolfgang R. Mukabana","email":"","orcid":"","institution":"University of Nairobi","correspondingAuthor":false,"prefix":"","firstName":"Wolfgang","middleName":"R.","lastName":"Mukabana","suffix":""}],"badges":[],"createdAt":"2026-01-19 14:06:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8640180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8640180/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105207002,"identity":"49a6bed2-29fa-42ab-9a3f-f0b10a2eadd2","added_by":"auto","created_at":"2026-03-23 13:02:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176516,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of dose-response relationship with \u003cem\u003eSalmonella \u003c/em\u003einfection using exponential modelling (EM).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8640180/v1/18d5d09aaa1cf07d62278280.png"},{"id":105207001,"identity":"f4958803-3914-4330-b652-eb82c2e38900","added_by":"auto","created_at":"2026-03-23 13:02:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":175604,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of dose-response relationship with \u003cem\u003eSalmonella \u003c/em\u003einfection using beta-Poison modelling (BP).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8640180/v1/0168dbf0881114e02372e1ba.png"},{"id":105564068,"identity":"5e678ae6-168c-4022-957c-e1edde32459d","added_by":"auto","created_at":"2026-03-27 12:48:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":612682,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of risk characterization of \u003cem\u003eSalmonella\u003c/em\u003einfection based on daily, weekly, monthly, and annual consumption of 1 g, 10 g, and 25 g of food, using the beta-Poison and exponential model.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8640180/v1/f9383bd76b36d36adada8b48.png"},{"id":105207006,"identity":"10d1c466-a437-4537-a329-dacb270791f7","added_by":"auto","created_at":"2026-03-23 13:02:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":609559,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of risk characterization of \u003cem\u003eSalmonella\u003c/em\u003einfection based on daily, weekly, monthly, and annual consumption of 100 g, 500 g, and 1000 g of food, using the beta-Poison and exponential model.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8640180/v1/f009036382d6d2d950aa401a.png"},{"id":105207003,"identity":"4240d464-d9cf-499e-bf81-8eadff670656","added_by":"auto","created_at":"2026-03-23 13:02:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":204986,"visible":true,"origin":"","legend":"\u003cp\u003eFood safety monitoring pathway framework for tracking the food contamination risk associated with \u003cem\u003eSalmonella\u003c/em\u003e in ready-to-eat food sold in Bukavu’s food establishments, DRC.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8640180/v1/feadf889db2a0c3c628aa20c.png"},{"id":105207004,"identity":"cb8e92ed-4f7d-48da-ad08-bf6aec5796ec","added_by":"auto","created_at":"2026-03-23 13:02:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2557311,"visible":true,"origin":"","legend":"\u003cp\u003eSampling sites across three municipalities of Bukavu City where food samples were collected.\u003c/p\u003e\n\u003cp\u003eSource: This figure 6 was generated by the author using the global positioning system of each location, using QGIS software. The waypoints were collected using Kobo Collect tool.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8640180/v1/1bb82d2a737fdcfc77ade9a7.png"},{"id":105569454,"identity":"67f03411-2fd4-4981-a12f-dce2d5b49274","added_by":"auto","created_at":"2026-03-27 13:12:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4920260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8640180/v1/a1fb8852-8ab8-4e34-ad8a-b09d01cdf189.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative Microbiological Risk Assessment of Foodborne Salmonella in Bukavu City, Democratic Republic of Congo","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe widespread consumption of ready-to-eat and street-vended foods under poor sanitation and hygiene conditions in the Democratic Republic of Congo (DRC) increases the risk of foodborne \u003cem\u003eSalmonella\u003c/em\u003e infection\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. There is high prevalence of \u003cem\u003eSalmonella\u003c/em\u003e in human as well as animal, food, and water samples analyzed in DRC\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Despite the high prevalence of \u003cem\u003eSalmonella\u003c/em\u003e in contaminated food samples in DRC, dose-response relationships and risk characterization of foodborne \u003cem\u003eSalmonella\u003c/em\u003e infection remain poorly defined.\u003c/p\u003e \u003cp\u003eThe presence of \u003cem\u003eSalmonella\u003c/em\u003e in food does not consistently result in infection, as contaminated products may be consumed without causing illnes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The probability of infection depends on the ingested microbial dose and the duration of exposure\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Clinical \u003cem\u003eSalmonella\u003c/em\u003e infection symptoms occurs when exposure exceeds a dose\u0026ndash;response threshold; below this threshold, infection may not occur\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, because \u003cem\u003eSalmonella\u003c/em\u003e is an opportunistic pathogen, host-related factors such as immunodeficiency and malnutrition can increase \u003cem\u003eSalmonella\u003c/em\u003e ability, enabling infection even at lower exposure doses\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Countries therefore require a robust quantitative microbiological risk assessment (QMRA) framework to provide a sound scientific basis for policy development and decision-making\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Implementing QMRA at national, subnational, or city levels enables accurate estimation of dietary risks to limit foodborne pathogen transmission. Such approaches can reduce pathogen exposure doses and lower the incidence of infectious diseases\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Beyond public health benefits, improved food safety may enhance product marketability in Bukavu City, with potential incomes and livelihoods at local and national levels\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has addressed a critical gap in food safety research in DRC by applying a QMRA to foods consumed in Bukavu City. It was guided by two central questions: i.\u0026ldquo;In the event food consumed in Bukavu City is contaminated with \u003cem\u003eSalmonella\u003c/em\u003e species, what is the risk of contracting a \u003cem\u003eSalmonella\u003c/em\u003e infection?\u0026rdquo;; ii. \u0026ldquo;How likely is a consumer of ready-to-eat food or street-vended food sold in Bukavu food establishments to get infected from exposure to \u003cem\u003eSalmonella\u003c/em\u003e over a given period?\u0026rdquo;. This study therefore aimed to quantify \u003cem\u003eSalmonella\u003c/em\u003e loads and exposure doses in food samples, estimate dose\u0026ndash;response relationships associated with \u003cem\u003eSalmonella\u003c/em\u003e infection, characterize the daily, weekly, monthly, and annual \u003cem\u003eSalmonella\u003c/em\u003e infection risks based on consumption levels, and develop a pathway framework for routine \u003cem\u003eSalmonella\u003c/em\u003e surveillance. The study revealed overlooked sources and drivers of foodborne infection risk in Bukavu City and provided evidence to support risk-based food safety policies and targeted interventions to reduce dietary exposure and disease transmission.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eMicrobiological quality and\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003eload in food samples\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe findings revealed widespread microbial contamination, affecting 90.4% of food samples (n\u0026thinsp;=\u0026thinsp;415), while 9.6% presented no risk for human health. The rest of food samples posed low to high health risk for human consumption (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk classification of food sampled according to the World Health Organization and the \u0026ldquo;\u003cem\u003eOffice Congolais de Controle\u003c/em\u003e\u0026rdquo; (OCC) standards.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRisk classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFood samples\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003en %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceptable contamination, No risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceptable contamination, Low risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolluted, Moderate risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighly contaminated, High risk,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e415 100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe average most probable number (MPN) values of \u003cem\u003eSalmonella\u003c/em\u003e in 1 g and 100 g of food was as follows: beans (19 and 1900), beef (4.6 and 460), fish (23.2 and 2320), bread (25.8 and 2580), peanut (3.1 and 310), pork (34.4 and 3440), potato (22.4 and 2240), rice (34.2 and 3420), \u0026ldquo;\u003cem\u003esamosa\u0026rdquo;\u003c/em\u003e (36.4 and 3640), sausage (37.3 and 3730), \u0026lsquo;\u003cem\u003eugali\u0026rdquo;\u003c/em\u003e (58.4 and 5840), and vegetables (31.3 and 3130) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Kruskal-Wallis test revealed no significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.01) in MPN values according to the type of food consumed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of \u003cem\u003eSalmonella\u003c/em\u003e load in 12 food types sampled from Bukavu City, DRC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMin.* Mean Max.*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003eMin. Mean Max.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c14\" namest=\"c10\"\u003e \u003cp\u003eMin. Mean Max.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003eBeef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c14\" namest=\"c10\"\u003e \u003cp\u003eFish\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPN*/1g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.30E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e4.60E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.50E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e2.32E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e9.30E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPN/100g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.30E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e4.60E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.50E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e2.32E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e9.30E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBread\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003ePeanut\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c14\" namest=\"c10\"\u003e \u003cp\u003ePork\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPN/1g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.58E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.10E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e3.44E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPN/100g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.58E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.10E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e3.44E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c14\" namest=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSamosa\u003c/em\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPN/1g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.24E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.30E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.42E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.50E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e3.64E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPN/100g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.24E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.30E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.42E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.50E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e3.64E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSausage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eUgali\u003c/em\u003e \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c14\" namest=\"c10\"\u003e \u003cp\u003eVegetable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPN/1g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.73E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e5.84E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e3.13E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.50E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPN/100g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.73E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e5.84E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.00E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e3.13E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.50E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea \u003cem\u003e\u0026ldquo;\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eSamosa\u0026rdquo;\u003c/em\u003e = popular pastry that is typically triangular in shape filled with spiced vegetables, potatoes, or meat, and is deep-fried until crispy. \u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e \u0026ldquo;\u003cem\u003eUgali\u003c/em\u003e\u0026rdquo; = staple food in Eastern and Central Africa made from maize or cassava flour mixed with boiling water to form a thick porridge and is typically served with meat or vegetable stews. \u003cb\u003e*\u003c/b\u003eMPN\u0026thinsp;=\u0026thinsp;Most Probable Number of microorganisms in 1g and 100g. Min.=minimum. Max.=maximum.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEstimation of exposure dose and dose-response of\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003einfection in food\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe average exposure dose of \u003cem\u003eSalmonella\u003c/em\u003e in food varied from 21.7 MPN in 1 g of beans to 64032.3 MPN in 1000 g of \u003cem\u003eUgali\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated exposure dose of \u003cem\u003eSalmonella\u003c/em\u003e in 12 food types sold in Bukavu City, DRC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eBeef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eFish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eSausage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e521.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e254.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e395.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e541.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1303.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e637.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e989.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2166.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2548.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3958.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e24000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10833.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12741.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e19790.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e120000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21666.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e150000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25483.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e93000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e39581.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e240000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBread\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePeanut\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ePork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u003cem\u003eUgali\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e339.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e365.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e640.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e706.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e849.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e912.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1600.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2827.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3396.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3651.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6403.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e24000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14138.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16983.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e120000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18256.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e120000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32016.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e120000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28277.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33967.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e240000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36512.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e240000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e64032.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e240000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePotato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSamosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eVegetable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e458.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e322.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e633.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e937.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1147.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e806.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3749.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4588.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3227.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e15000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18748.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22944.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e120000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16136.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e75000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000 g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37496.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e150000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45888.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e240000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32272.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e150000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe dose-response was positively correlated with the exposure dose (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). An increase in \u003cem\u003eSalmonella\u003c/em\u003e load corresponded to a higher dose-response probability of \u003cem\u003eSalmonella\u003c/em\u003e infection. In 1 g, 10 g, and 25 g of food, the average dose-response probability was from 14.8% in beans to 98.3% in \u003cem\u003eUgali\u003c/em\u003e. From 100 g to 1000 g, the probability ranged from 99.3% to 100% in all types of food analyzed. The dose-response from exponential modeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was significantly different (Wilcox test\u0026thinsp;=\u0026thinsp;13604, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) from the dose-response using beta-Poison modeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk characterization of\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e \u003cb\u003einfection associated with contaminated food\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBased on the beta-Poison modeling, estimated daily and weekly risk of \u003cem\u003eSalmonella\u003c/em\u003e infection from 1 g to 25 g of food varied from 46.6% in beans to 96.6% in rice. This estimation exceeded 99% over one month for all types of food (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Exponential modeling estimated the risk of \u003cem\u003eSalmonella\u003c/em\u003e infection from 14.8% in 1 g of beans to 80% in other types of food on a daily and weekly basis, respectively. The monthly and annual risks are nearly 100% for all type of food (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). No significant associations (Kruskall-Wallis, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.01) were observed between \u003cem\u003eSalmonella\u003c/em\u003e infection risk and types of food.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsuming 100 g to 1000 g of food was associated with \u003cem\u003eSalmonella\u003c/em\u003e infection risk ranging from 90% to 100% under daily, weekly, monthly, and annual exposures scenarios using the beta-Poison modeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ek). The exponential modeling estimated a constant \u003cem\u003eSalmonella\u003c/em\u003e infection risk of 100% regardless of exposure period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003el).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eConceptual pathway framework for food safety monitoring under Bukavu conditions\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe quantitative microbiological risk assessment (QMRA) highlighted critical gaps in food safety monitoring in Bukavu City. Most food samples were unsafe for human consumption because of high microbial loads, independent of type of food. High \u003cem\u003eSalmonella\u003c/em\u003e contamination observed may likely be explained by the proximity of ready-to-eat food to inadequate sanitation infrastructure, exposure to dust and waste, and sub-optimal hygiene practices in food vending environments. In addition, insufficient cooking temperature, poor microbiological quality of water used in food preparation, inadequate personal hygiene of food handlers, and cross-contamination during food handling may have considerable influence on the \u003cem\u003eSalmonella\u003c/em\u003e load in food\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The findings of this study are consistent with previous research conducted in the Democratic Republic of Congo (DRC). In Bukavu City, bacterial loads ranging from 2 x 10\u003csup\u003e5\u003c/sup\u003e to 1 x 10\u003csup\u003e6\u003c/sup\u003e CFU/10g in roasted street-vended fish from Bukavu markets\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, reflecting poor hygiene during food processing. The similarity between these results and our findings indicates persistent overlooked sources of foodborne \u003cem\u003eSalmonella\u003c/em\u003e with a high potential for causing illness. These observations highlight the need for innovative dietary risk mitigation strategies.\u003c/p\u003e \u003cp\u003eExposure dose of \u003cem\u003eSalmonella\u003c/em\u003e in food estimated using the most probable Number (MPN) method, showed a dose-dependent increase in \u003cem\u003eSalmonella\u003c/em\u003e infection risk. Dose\u0026ndash;response analyses indicated that higher \u003cem\u003eSalmonella\u003c/em\u003e loads in food considerably increased \u003cem\u003eSalmonella\u003c/em\u003e infection probability, with consumption of just 1 g of contaminated food associated with risks of 15.1%\u0026ndash;31.3%. Typical daily food consumption exceeding 25 g returned estimated infection risks above 70%, indicating substantial population exposure in Bukavu City. These estimates are consistent with the high burden of \u003cem\u003eSalmonella\u003c/em\u003e-associated illness reported by local healthcare facilities\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Because many \u003cem\u003eSalmonella\u003c/em\u003e infections remain clinically asymptomatic\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, a substantial hidden burden likely exists in Bukavu City. The beta\u0026ndash;Poisson modeling produced more realistic risk estimates than the exponential modeling, supporting its use for dietary risk assessment under Bukavu conditions.\u003c/p\u003e \u003cp\u003eGiven the absence of dose\u0026ndash;response studies on microbial infection in DRC, our findings were compared to studies from other countries. The \u003cem\u003eSalmonella\u003c/em\u003e loads observed in our study were lower than those reported in Brazil, where \u003cem\u003eSalmonella\u003c/em\u003e Enteritidis concentration was up to 4.6 x 10\u003csup\u003e9\u003c/sup\u003e MPN/g in potatoes, with higher concentrations in sausage, cassava, and roasted beef, largely attributed to inadequate temperature control during storage and cross-contamination, and linked to the occurrence of salmonellosis outbreaks\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In the United States, a study reported that 1 x 10\u003csup\u003e3\u003c/sup\u003e CFU/g of \u003cem\u003eSalmonella\u003c/em\u003e initiated infection in 50% of broiler chicken consumers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Experimental studies in mice indicated lethal doses ranging from 1.7 \u0026times; 10⁶ to 2.0 \u0026times; 10⁹ cells of \u003cem\u003eSalmonella\u003c/em\u003e Typhimurium\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. For healthy people, \u003cem\u003eSalmonella\u003c/em\u003e infectious doses between 10⁶ and 10⁸ CFU/100 mL have been reported\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Collectively, previous studies highlighted the high infectious potential of \u003cem\u003eSalmonella\u003c/em\u003e species and serotypes, even at low concentrations, notably in individuals with a weak immune system such as infants, young children, immunodeficiency patients and the elderly\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the time of this study, research in the DRC addressing exposure dose, dose\u0026ndash;response relationships, and microbial risk characterization remained limited. This gap underscores the need for comprehensive studies that integrate key parameters into predictive models for foodborne \u003cem\u003eSalmonella\u003c/em\u003e detection. Pilot applications of mathematical dose\u0026ndash;response modeling, such as the present study, are therefore fundamental. A study supported that robust models should incorporate serotype-specific variability of \u003cem\u003eSalmonella\u003c/em\u003e, environmental drivers of contamination, and diverse exposure scenarios to improve infection risk prediction\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Such approaches enable the development of locally informed theoretical and practical frameworks, while recognizing the non-zero infection risk associated with any \u003cem\u003eSalmonella\u003c/em\u003e dose\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Without targeted control strategies, exposure dose and dose\u0026ndash;response dynamics will continue to challenge foodborne disease prevention efforts.\u003c/p\u003e \u003cp\u003eThe characterization of foodborne \u003cem\u003eSalmonella\u003c/em\u003e infection risk was estimated on daily, weekly, monthly, and annual bases. The \u003cem\u003eSalmonella\u003c/em\u003e infection probability increased with dose\u0026ndash;response and exposure frequency. Consumption of 1 g of contaminated food resulted in 73% daily or weekly risk, while regular consumption over one month decidedly increased risk. Consuming 100 g of contaminated food represented 100% of \u003cem\u003eSalmonella\u003c/em\u003e infection risk regardless of exposure duration. This indicates the constant threat posed by any contact with contaminated food. Our findings on risk characterization of \u003cem\u003eSalmonella\u003c/em\u003e infection aligned with previous studies. A study attributed illness cases to consumption of low proportions (0.3% and 0.6%) of \u003cem\u003eSalmonella\u003c/em\u003e\u0026ndash;contaminated products\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Similarly, another study linked environmental contamination to 10% of \u003cem\u003eSalmonella\u003c/em\u003e infection risk\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Stathas \u003cem\u003eet al.\u003c/em\u003e reported 6.6% of annual \u003cem\u003eSalmonella\u003c/em\u003e infection probabilities from low-dose exposure (3.57 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e CFU/g)\u003csup\u003e6\u003c/sup\u003e. Collectively, these findings underscored the high infectivity of \u003cem\u003eSalmonella\u003c/em\u003e regardless of quantity or type of food consumed.\u003c/p\u003e \u003cp\u003eUsing trend analyses linking environmental conditions and food handling practices with \u003cem\u003eSalmonella\u003c/em\u003e load and infection risk, our study proposes a pathway framework for food safety monitoring in Bukavu City. The framework integrates environmental drivers, hygiene practices, and cross-contamination processes to predict contamination across food production and vending stages. Akil and Ahmad reported approximately 12% \u003cem\u003eSalmonella\u003c/em\u003e infection during food production and along the food supply chain\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Stathas \u003cem\u003eet al.\u003c/em\u003e emphasized the need for monitoring during food processing, storage, and preparation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Ovuru \u003cem\u003eet al\u003c/em\u003e. reported that the pathogen prevalence in slaughterhouses reflects poor hygiene\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Consistent with evidence of substantial \u003cem\u003eSalmonella\u003c/em\u003e prevalence along food supply chains, the proposed framework prioritizes upstream monitoring at slaughterhouses, markets, fisheries, and food establishments to reduce contamination at its source. Our proposed pathway framework is comparable to the farm-to-fork quantitative model of Akil and Ahmad\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e but differs methodologically by incorporating qualitative variables. Designed for data-limited Bukavu City, our pathway framework provides a foundation for future dose\u0026ndash;response relationship and microbial risk characterization studies in DRC. Complementary public health measures, including food-handler vaccination, may further enhance prevention\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These findings underscore the urgent need for integrated, risk-based monitoring frameworks that address environmental and operational drivers of foodborne contamination, given that environmental factors could soundly influence the spread of microorganisms\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003eIsolation and enumeration of\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eSalmonella\u003c/em\u003e strains analyzed in this study were isolated from 415 food samples collected from randomly selected food establishments, including restaurants and street-vended food outlets in Bukavu City, DRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Samples were transported in a cooler box to the Microbiology and Biotechnology Laboratory at the \u0026ldquo;\u003cem\u003eUniversit\u0026eacute; Officielle de Bukavu\u003c/em\u003e\u0026rdquo; within four hours of sampling. For microbiological analysis, 25 g of each food sample were aseptically transferred into 225 mL of peptone water (PW) and homogenized for 1 minute. A 10-fold serial dilution up to 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e was then prepared by transferring 1 mL of the homogenate into 9 mL of PW. All dilutions were prepared in triplicate and incubated at 37\u0026deg;C for 24 h.\u003c/p\u003e \u003cp\u003eFrom positive presumptive tubes, 1 mL was transferred into 10 mL of Tryptone Water Broth (TWB), a selective enrichment medium for \u003cem\u003eSalmonella\u003c/em\u003e species and incubated at 37\u0026deg;C for 24 h. Subsequently, 1 mL of the enriched culture was streaked onto Hektoen Enteric Agar (HEA) and incubated at 37\u0026deg;C for 24 h. One to three presumptive \u003cem\u003eSalmonella\u003c/em\u003e colonies were then sub-cultured onto Salmonella\u0026ndash;Shigella (SS) agar and incubated at 37\u0026deg;C for 24 h. Plates showing no growth were re-incubated to confirm the absence of \u003cem\u003eSalmonella\u003c/em\u003e species. Biochemical characterization included tests for citrate utilization, catalase and urease activities, indole production, glucose and lactose fermentation, hydrogen sulfide production, and motility, enabling discrimination of \u003cem\u003eSalmonella\u003c/em\u003e from other enteric bacteria\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Among triplicate tubes, positive results were used to estimate the most probable number (MPN) of \u003cem\u003eSalmonella\u003c/em\u003e per gram of food. MPN values were determined following the U.S. Food and Drug Administration Bacteriological Analytical Manual\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and associated occurrence probabilities were calculated using the U.S. Environmental Protection Agency online MPN Calculator (version 2.0).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eQuantitative microbiological risk assessment of foodborne\u003c/b\u003e \u003cb\u003eSalmonella\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe Quantitative Microbiological Risk Assessment (QRMA) involved four main stages: hazard identification, exposure dose assessment, dose-response estimation, and risk characterization of \u003cem\u003eSalmonella\u003c/em\u003e infection\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe hazard identification for risk assessment focused on \u003cem\u003eSalmonella\u003c/em\u003e because of its high prevalence in DRC, established correlation with meat consumption in Bukavu City, and low minimum infective dose.\u003c/p\u003e \u003cp\u003eThe exposure dose assessment involved calculating the MPN of \u003cem\u003eSalmonella\u003c/em\u003e in 1 g of food, then extrapolate it to predict the \u003cem\u003eSalmonella\u003c/em\u003e dose in 10, 25, 100, 500 and 1000 g, applying the following equation : \u003cb\u003ed\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;MPN in 1 g x\u003c/b\u003e \u003cb\u003eQ.\u003c/b\u003e Where \u003cem\u003ed\u003c/em\u003e represents the exposure dose of \u003cem\u003eSalmonella. MPN\u003c/em\u003e means the most probable number of \u003cem\u003eSalmonella\u003c/em\u003e cells in 1 g of food. \u003cem\u003eQ\u003c/em\u003e represents the amount of contaminated food consumed.\u003c/p\u003e \u003cp\u003eThe dose-response estimation involved calculating the probability of \u003cem\u003eSalmonella\u003c/em\u003e infection after ingesting a given dose of \u003cem\u003eSalmonella\u003c/em\u003e. The exponential and beta-Poison models were applied to calculate the dose-response probability for \u003cem\u003eSalmonella\u003c/em\u003e infection as follows: Exponential model: \u003cb\u003eP\u003c/b\u003e\u003cb\u003einf\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;1-exp (-rd)\u003c/b\u003e and beta-Poisson model: \u003cb\u003eP\u003c/b\u003e\u003cb\u003einf\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;1 \u0026ndash; {1+ [d/N\u003c/b\u003e\u003csub\u003e\u003cb\u003e50\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(2\u003c/b\u003e\u003csup\u003e\u003cb\u003e1/α\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e\u0026ndash; 1)]}\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026ndash;α\u003c/b\u003e\u003c/sup\u003e. Where P\u003cem\u003einf\u003c/em\u003e represents the probability of infection from consuming one meal per day; \u003cem\u003ed\u003c/em\u003e is the exposure dose or the number of viable \u003cem\u003eSalmonella\u003c/em\u003e ingested; \u003cem\u003er\u003c/em\u003e is the probability of ingesting at least one \u003cem\u003eSalmonella\u003c/em\u003e cell and getting infected; \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e50\u003c/em\u003e\u003c/sub\u003e is the dose at which there is 50% probability of getting infected after consuming contaminated food; and \u003cem\u003eα\u003c/em\u003e is a pathogen-specific parameter describing variability in susceptibility of a human against \u003cem\u003eSalmonella\u003c/em\u003e. Each microbial species has distinct values for N\u003csub\u003e50\u003c/sub\u003e and α. For \u003cem\u003eSalmonella\u003c/em\u003e species, r\u0026thinsp;=\u0026thinsp;0.00752, α\u0026thinsp;=\u0026thinsp;0.313, and N\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;23.600\u003csup\u003e5,9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe risk characterization consisted of integrating results from the first three stages: the exposure dose of \u003cem\u003eSalmonella\u003c/em\u003e measured at diverse quantities (1, 10, 25, 100, 500 and 1000 g) of food; the duration of exposure (daily, weekly, monthly, annual), and the dose-response probability estimated. Then the following equation was applied : \u003cb\u003ePt\u0026thinsp;=\u0026thinsp;1 \u0026ndash; (1 \u0026ndash; P\u003c/b\u003e\u003cb\u003einf\u003c/b\u003e \u003cb\u003e)\u003c/b\u003e\u003csup\u003e\u003cb\u003en\u003c/b\u003e\u003c/sup\u003e. Where \u003cem\u003ePt\u003c/em\u003e represents the probability of infection after a given time according to the \u003cem\u003en\u003c/em\u003e number of exposures during the considered time. The risk characterization of \u003cem\u003eSalmonella\u003c/em\u003e infection after consuming a contaminated food in Bukavu City was calculated as follows:\u003c/p\u003e \u003cp\u003eProbability of infection from a single day with one exposure per day (n\u0026thinsp;=\u0026thinsp;1): Pt\u0026thinsp;=\u0026thinsp;1 \u0026ndash; (1 \u0026ndash; P\u003cem\u003einf\u003c/em\u003e )\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eProbability of infection over one week with one exposure per day (n\u0026thinsp;=\u0026thinsp;7): Pt\u0026thinsp;=\u0026thinsp;1 \u0026ndash; (1 \u0026ndash; P\u003cem\u003einf\u003c/em\u003e )\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eProbability of infection over one month with one exposure per day (n\u0026thinsp;=\u0026thinsp;30): Pt\u0026thinsp;=\u0026thinsp;1 \u0026ndash; (1 \u0026ndash; P\u003cem\u003einf\u003c/em\u003e )\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eProbability of infection over one year with one exposure per day (n\u0026thinsp;=\u0026thinsp;365): Pt\u0026thinsp;=\u0026thinsp;1 \u0026ndash; (1 \u0026ndash; P\u003cem\u003einf\u003c/em\u003e )\u003csup\u003e365\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eDesigning pathway framework for food safety monitoring under Bukavu conditions\u003c/h3\u003e\n\u003cp\u003eA conceptual pathway framework for food safety monitoring was designed to assess the potential \u003cem\u003eSalmonella\u003c/em\u003e contamination at each stage of the food handling, from production to consumption in food establishments. \u003cem\u003eSalmonella\u003c/em\u003e occurrence was treated as a dependent variable, while environmental conditions and cross-contamination during food handling were considered as independent variables. Food contamination possibility by \u003cem\u003eSalmonella\u003c/em\u003e was classified as present (\u0026ldquo;Yes\u0026rdquo;) when conditions favored the introduction or persistence of \u003cem\u003eSalmonella\u003c/em\u003e and absent (\u0026ldquo;No\u0026rdquo;) when conditions inhibited or eliminated the pathogen.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R software (version 4.4.0)\u003csup\u003e31\u003c/sup\u003e. Data normality was assessed with the Shapiro-Wilk test. As MPN values were not normally distributed, non-parametric tests were used. The Kruskal-Wallis test assessed the association of \u003cem\u003eSalmonella\u003c/em\u003e load with the type of food. Wilcox test was applied to compare the result between exponential and beta-Poison modeling. The mean differences were considered significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01. Bacteriological quality of food was assessed against the World Health Organization (WHO) and the \u0026ldquo;\u003cem\u003eOffice Congolais de Contr\u0026ocirc;le\u003c/em\u003e\u0026rdquo; (The National Office for Standardization in DRC) standards. Risk characterization assumed that individuals consumed food once a day. Dose-response models, using estimated exposure doses, calculated the probability of infection per gram of contaminated food. This probability was extrapolated to estimate the daily, weekly, monthly, and annual risks of \u003cem\u003eSalmonella\u003c/em\u003e infection.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eAuthors declare there is no conflict of interest concerning this publication.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe first author is a lecturer at \u0026ldquo;\u003cem\u003eUniversit\u0026eacute; Officielle de Bukavu\u003c/em\u003e\u0026rdquo; in DRC whom PhD study was partially supported through a capacity building competitive training grant, the next generation of scientists funded by Carnegie Cooperation of New York through the Regional Universities Forum (RUFORUM) for capacity building in agriculture.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: A.M.O. and J.M.W. Methodology: A.M.O., J.M.W. and F.P.A. Formal Analysis: A.M.O., J.M.W. and F.P.A. Investigation: A.M.O., F.P.A. and A.A.L. Resources: A.M.O. Data curation: A.M.O. and F.P.A. Writing original draft: A.M.O. Review and Editing: J.M.W., C.W.L, W.R.M., A.A.L., F.P.A, A.M.O. Supervision: J.M.W., C.W.L, W.R.M. and A.A.L. Funding acquisition: A.M.O. All authors have read and agreed to publish this manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe specially thank all students of \u0026ldquo;Universit\u0026eacute; Officielle de Bukavu\u0026rdquo; who assisted during the fieldwork and sample collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData generated in this study will be shared on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTack, B. et al. 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R Foundation for Statistical Computing (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Quantitative microbiological risk assessment, foodborne Salmonella exposure, dose-response modeling, Bukavu","lastPublishedDoi":"10.21203/rs.3.rs-8640180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8640180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eSalmonella\u003c/em\u003e remains a primary cause of enteric disease. In the Democratic Republic of Congo (DRC), the lack of quantitative data on foodborne \u003cem\u003eSalmonella\u003c/em\u003e exposure limits effective risk assessment and food safety management, underscoring the necessity for robust monitoring tools. Here, we applied a quantitative microbiological risk assessment (QMRA) to estimate the risk of \u003cem\u003eSalmonella\u003c/em\u003e exposure through contaminated foods in Bukavu. Food samples were collected from multiple points of sale and analyzed using culture-based microbiological methods. The QMRA integrated \u003cem\u003eSalmonella\u003c/em\u003e loads, exposure doses, dose\u0026ndash;response modeling, and risk characterization to estimate \u003cem\u003eSalmonella\u003c/em\u003e infection probabilities across varying consumption scenarios and exposure durations. High \u003cem\u003eSalmonella\u003c/em\u003e loads were detected, with exposure doses increasing proportionally with food consumption, and \u003cem\u003eSalmonella\u003c/em\u003e infection risk rising with increasing dose. This study provides the first QMRA-based estimates of foodborne \u003cem\u003eSalmonella\u003c/em\u003e risk in the DRC and introduces a pathway framework that incorporates environmental conditions and cross-contamination at the food establishment level. These findings offer a practical evidence base to inform food safety monitoring and reduce the burden of foodborne disease in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Quantitative Microbiological Risk Assessment of Foodborne Salmonella in Bukavu City, Democratic Republic of Congo","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-23 13:02:24","doi":"10.21203/rs.3.rs-8640180/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-30T01:25:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26378316454819339528398639113275294233","date":"2026-03-21T05:12:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-20T13:25:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165159151363818011026097537100062004915","date":"2026-03-20T09:51:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T04:57:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-28T04:14:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-23T07:40:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-23T07:37:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-19T13:13:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b55a0da8-0c54-48d5-9f3a-da98f524dfe9","owner":[],"postedDate":"March 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64886287,"name":"Health sciences/Diseases"},{"id":64886288,"name":"Earth and environmental sciences/Environmental sciences"},{"id":64886289,"name":"Biological sciences/Microbiology"},{"id":64886290,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-23T13:02:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-23 13:02:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8640180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8640180","identity":"rs-8640180","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}