Modernization Data Analysis and Visualization for Food Safety Research Outcomes
Source abstract
Abstract
Appropriate data collection and using reliable and accurate procedures are the first steps in conducting an experiment that will provide trustworthy outcomes. It is key to perform an assertive statistical analysis and data visualization for a correct interpretation and communication of results. A clear statistical summary and presentation of the data is critical for the reader to easily process and comprehend experimental results. Nowadays, there are a series of different tools to perform proper statistical analysis and create elaborate graphs that will help readers to understand the data, identify trends, detect outliers, evaluate statistical outputs, etc. However, researchers that are beginning to navigate experiments do not frequently encounter a guide that can provide basic principal concepts to begin their statistical analysis and data presentation. Therefore, the objective of this article is to provide a guide or manual to analyze and presents results focused on different types of common food safety experiments, including method comparisons, intervention studies, pathogen presence experiments, bio-mapping, statistical process control, and shelf life experiments. This review will provide information about data visualization options and statistical analysis approaches for different food safety experiments. In addition, basic concepts about descriptive statistics and possible solutions for issues related to microbiological measurements will be discussed.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Food Safety and Hygiene
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
International Center for Food Industry Excellence, Department of Animal and Food Sciences, Texas Tech University, Lubbock, TX 79409, USA
Department of Animal & Dairy Science, University of Georgia, Athens, GA 30602, USA
Independent Researcher, Quito 170528, Pichincha, Ecuador
Department of Animal & Dairy Sciences, University of Wisconsin Madison, Madison, WI 53706, USA
Funding
International Center for Food Industry Excellence (ICFIE) at Texas Tech University
References (100+)
Show source-provided references
1. White, J.C., and Haven, N. (2023, March 16). USDA NIFA Workshop on Toxic Elements in Food: Identification of Critical Knowledge Gaps, Available online: https://portal.ct.gov/-/media/caes/documents/publications/press_releases/2022/october-20/nifa-c2z-workshop-full-report_toxic-elements-in-food.pdf.
2. Foodborne illness acquired in the United States10.3201/eid1707.110019 ↗
3. Interagency Food Safety Analytics Collaboration (2022). Foodborne Illness Source Attribution Estimates for Salmonella, Escherichia coli O157, Listeria monocytogenes, and Campylobacter using Outbreak Surveillance Data, United States.
4. Foodborne pathogens10.3934/microbiol.2017.3.529 ↗
5. The impact of socioeconomic status on foodborne illness in high-income countries: A systematic review10.1017/S0950268814003847 ↗
6. Observations on the use of statistical methods in Food Science and Technology10.1016/j.foodres.2013.10.024 ↗
7. Descriptive Statistics and Normality Tests for Statistical Data10.4103/aca.ACA_157_18 ↗
8. Pampoukis, G., Lytou, A.E., Argyri, A.A., Panagou, E.Z., and Nychas, G.J.E. (2022). Recent Advances and Applications of Rapid Microbial Assessment from a Food Safety Perspective. Sensors, 22.10.3390/s22072800 ↗
9. Bland, M. (2015). Introduction to Medical Statistics, Oxford University Press. [4th ed.].
10. Big data and large sample size: A cautionary note on the potential for bias10.1111/cts.12178 ↗
11. Sundaram, K.R., Dwivedi, S.N., and Sreenivas, V. (2015). Medical Statistics: Principles and Methods, Medknow Publications and Media Pvt. Ltd.. [2nd ed.].
12. Chik, A.H.S., Schmidt, P.J., and Emelko, M.B. (2018). Learning Something From Nothing: The Critical Importance of Rethinking. Front. Microbiol., 9.10.3389/fmicb.2018.02304 ↗
13. Tropea, A. (2022). Microbial Contamination and Public Health: An Overview. Int. J. Environ. Res. Public Health, 19.10.3390/ijerph19127441 ↗
14. Particle and microorganism enumeration data: Enabling quantitative rigor and judicious interpretation10.1021/es902382a ↗
15. A review of conventional detection and enumeration methods for pathogenic bacteria in food10.1139/w04-080 ↗
16. Lund, B., Baird-Parker, T.C., and Gould, G.W. (2000). Microbiological Safety and Quality of Food, Springer Science & Business Media. [1st ed.].
17. Fitting a distribution to microbial counts: Making sense of zeroes10.1016/j.ijfoodmicro.2014.11.023 ↗
18. Schijven, J.F., and De-Roda-Husman, A.M. (2011). Applications of Quantitative Microbial Source Tracking and Quantitative Microbial Risk assessmentMicrobial Source Tracking: Methods, Applications & Case Studies, Springer.10.1007/978-1-4419-9386-1_24 ↗
19. Hierarchical Bayesian analysis of censored microbiological contamination data for use in risk assessment and mitigation10.1016/j.fm.2010.06.006 ↗
20. Log transformation and the effect on estimation, implication, and interpretation of mean and measurement uncertainty in microbial enumeration10.5740/jaoacint.18-0161 ↗
21. Spiral plate method for bacterial determination10.1128/am.25.2.244-252.1973 ↗
22. The Relevance of the Distribution of Microorganisms Within Batches of Food to the Control of Microbiological Hazards from Foods10.1111/j.1365-2672.1981.tb01251.x ↗
23. Gherezgihier, B.A., Mahmud, A., Samuel, M., and Tsighe, N. (2017). Methods and Application of Statistical Analysis in Food Technology. J. Acad. Ind. Res., 6.
24. Weiss, N.A. (2015). Introductory Statistics, Pearson. [10th ed.].
25. Chang, W. (2018). R Graphics Cookbook: Practical Recipes for Visualizing Data, O’Reilly Media. [2nd ed.].
26. Unwin, A. (2020). Why is Data Visualization Important? What is Important in Data Visualization?. Harvard Data Sci. Rev., 1–7.10.1162/99608f92.8ae4d525 ↗
27. Unwin, A. (2015). Graphical Data Analysis with R, Chapman & Hall/CRC.10.1201/b18305 ↗
28. Wilkinson, L. (2005). The Grammar of Graphics, Springer. [2nd ed.].
29. Wickman, H. (2009). Elegant Graphics for Data Analysis, Springer.
30. Freeman, J.V., Walters, S.J., and Campbell, M.J. (2008). How to Display Data, Blackwell Publishing. [1st ed.].
31. Aitken, M., Broadhurst, B., and Hladky, S. (2010). Mathematics for Biological Scientists, Taylor & Francis Group.10.4324/9780203833520 ↗
32. Jarvis, B. (2016). Statistical Aspects of Sampling of Microbiological Analysis. Statistical Aspects of the Microbiological Examination of Foods, Academic Press. [3rd ed.].10.1016/B978-0-12-803973-1.00005-X ↗
33. Bliss, C.I., and Fisher, R.A. (1953). Fitting the Negative Binomial Distribution to Biological Data. Biometrics, 9.10.2307/3001850 ↗
34. Navidi, W. (2011). Statistics for Scientist and EngineersStatistics for Engineers and Scientists, McGraw-Hill. [3rd ed.].
35. McDonald, J.H. (2014). Handbook of Biological Statistics, Sparky House Publishing. [3rd ed.].
36. Parametric methods outperformed non-parametric methods in comparisons of discrete numerical variables10.1186/1471-2288-11-44 ↗
37. Conover, W.J. (1999). Practical NonParametric Statistics, John Wiley & Sons Inc.. [3rd ed.].
38. Jarvis, B. (2016). Frequency distributions. Statistical Aspects of the Microbiological Examination of Foods, Academic Press. [3rd ed.].10.1016/B978-0-12-803973-1.00003-6 ↗
39. Types of biological variables10.21037/jtd.2017.05.75 ↗
40. Hawkins, D. (2019). Biomeasurement: A Student’s Guide to Biological Statistics, Oxford University Press. [4th ed.].10.1093/hesc/9780198807483.001.0001 ↗
41. Procedures for Handling Aerometric Data10.1080/00022470.1973.10469762 ↗
42. Estimation of Average Concentration in the Presence of Nondetectable Values10.1080/1047322X.1990.10389587 ↗
43. Applegate, S.F., Englishbey, A.K., Stephens, T.P., and Sanchez-plata, M.X. (2023). Development and Verification of a Poultry Management Tool to Quantify Salmonella from Live to Final Product Utilizing RT-PCR. Foods, 12.10.3390/foods12020419 ↗
44. Application of a Commercial Salmonella Real-Time PCR Assay for the Detection and Quantitation of Salmonella enterica in Poultry Ceca10.4315/JFP-21-285 ↗
45. Vargas, D.A., Betancourt-barszcz, G.K., Blandon, S.E., Applegate, S.F., Brashears, M.M., Miller, M.F., Gragg, S.E., and Sanchez-Plata, M.X. (2023). Rapid Quantitative Method Development for Beef and Pork Lymph Nodes Using BAX® System Real Time Salmonella Assay. Foods, 12.10.3390/foods12040822 ↗
46. Bueno López, R., Vargas, D.A., Jimenez, R.L., Casas, D.E., Miller, M.F., Brashears, M.M., and Sanchez-Plata, M.X. (2022). Quantitative Bio-Mapping of Salmonella and Indicator Organisms at Different Stages in a Commercial Pork Processing Facility. Foods, 11.10.3390/foods11172580 ↗
47. De Villena, J.F., Vargas, D.A., López, R.B., Chávez-Velado, D.R., Casas, D.E., Jiménez, R.L., and Sanchez-Plata, M.X. (2022). Bio-Mapping Indicators and Pathogen Loads in a Commercial Broiler Processing Facility Operating with High and Low Antimicrobial Intervention Levels. Foods, 11.10.3390/foods11060775 ↗
48. Applegate, S.F., Sánchez-Plata, M.X., Nightingale, K.K., Thompson, L., Stephens, T.P., and Brashears, M.M. (2023). Development, Verification, and Validation of a RT-PCR Based Methodology for Salmonella Quantification as a Tool for Integrated Food Safety Management in Poultry from Live Production to Final Product. Foods, 12.10.3390/foods12020419 ↗
49. Comparison of the SimPlate Total Plate Count Method with Petrifilm, Redigel, and Conventional Pour-Plate Methods for Enumerating Aerobic Microorganisms in Foods10.4315/0362-028X-61.1.14 ↗
50. Brown, L.N.P., Sanchez-Plata, M.X., Thompson, L., Singh, S., Echeverry, A., and Brashears, M.M. (2022). Integration of Regulatory Compliance Assessments, Microbial Bio-Mapping, and Novel Intervention Technologies for Food Safety Management in Controlled Environment Agriculture: Vertical Hydroponics Leafy Green Facility, Texas Tech University.
51. Hygiena (2023, March 16). HygienaTM MicroSnapTM vs 3MTM PetrifilmTM vs bioMérieux TEMPO® Correlation Objective. Available online: www.hygiena.com.
52. Comparison of an Automated Most-Probable-Number Technique with Traditional Plating Methods for Estimating Populations of Total Aerobes, Coliforms, and Escherichia coli Associated with Freshly Processed Broiler Chickens10.4315/0362-028X.JFP-11-024 ↗
53. Validation of the microsnap coliform and E. coli test system for enumeration and detection of coliforms and E. coli in a variety of foods10.5740/jaoacint.13-361 ↗
54. The validation of the microsnap total for enumeration of total viable count in a variety of foods10.5740/jaoacint.16-0016 ↗
55. Evaluation of the TEMPO most probable number technique for the enumeration of Enterobacteriaceae in food and dairy products
56. Vargas, D.A., Casas, D.E., Chávez-Velado, D.R., Jiménez, R.L., Betancourt-Barszcz, G.K., Randazzo, E., Lynn, D., Echeverry, A., Brashears, M.M., and Sánchez-Plata, M.X. (2021). In-plant intervention validation of a novel ozone generation technology (Bio-safe) compared to lactic acid in variety meats. Foods, 10.10.3390/foods10092106 ↗
57. A Comparison of the BAX System Method to the U.S. Food and Drug Administration’s Bacteriological Analytical Manual and International Organization for Standardization Reference Methods for the Detection of Salmonella in a Variety of Soy Ingredients10.4315/0362-028X.JFP-14-063 ↗
58. Comparison of the BAX for Screening/E.coli O157: H7 Method with Conventional Methods for Detection of Extremely Low Levels of Escherichia coli O157: H7 in Ground Beef10.1128/AEM.64.11.4390-4395.1998 ↗
59. Liu, T., Belk, K.E., and Zagmutt, F.J. (2020). Evaluation of Gene-Up and TEMPO AC for Determination of Shiga Toxin Producing Escherichia coli and Total Aerobic Microbial Populations from Microtally Sheets used to Sample Beef Carcasses and Hides, Colorado State University.
60. Comparison of the BAX R System with a multiplex PCR method for simultaneous detection and identification of Campylobacter jejuni and Campylobacter coli in environmental samples10.1016/S0168-1605(03)00073-4 ↗
61. Comparison of the BAX System PCR Method to Brazil’s Official Method for the Detection of Salmonella in Food, Water, and environmental samples10.4315/0362-028X-71.12.2442 ↗
62. In-Plant Validation Study of Harvest Process Controls in Two Beef Processing Plants in Honduras10.4315/0362-028X.JFP-18-395 ↗
63. Prevalence, seasonal variation, and antibiotic resistance pattern of enteric bacterial pathogens among hospitalized diarrheic children in suburban regions of central Kenya10.1186/s41182-016-0038-1 ↗
64. Casas, D.E., Vargas, D.A., Randazzo, E., Lynn, D., Echeverry, A., Brashears, M.M., Sanchez-Plata, M.X., and Miller, M.F. (2021). In-Plant Validation of Novel on-Site Ozone Generation Technology (Bio-Safe) Compared to Lactic Acid Beef Carcasses and Trim Using Natural Microbiota and Salmonella and E. coli O157:H7 Surrogate Enumeration. Foods, 10.10.3390/foods10051002 ↗
65. Dynamics of Microbial Shedding in Market Pigs during Fasting and the Influence of Alginate Hydrogel Bead Supplementation during Transportation10.3390/microbiolres12040065 ↗
66. Presence of Presumptive Shiga Toxin—Producing Escherichia coli and Salmonella on Sheep during Harvest in Honduras10.4315/0362-028X.JFP-19-424 ↗
67. Prevalence and characterization of foodborne pathogens from Australian dairy farm environments10.3168/jds.2014-8735 ↗
68. Rortana, C., Nguyen-viet, H., Tum, S., Unger, F., Boqvist, S., Dang-xuan, S., Koam, S., Grace, D., Osbjer, K., and Heng, T. (2021). Prevalence of Salmonella spp. and Staphylococcus aureus in Chicken Meat and Pork from Cambodian Markets. Pathogens, 10.10.3390/pathogens10050556 ↗
69. Low Prevalence of Human Pathogens on Fresh Produce on Farms and in Packing Facilities: A Systematic Review10.3389/fpubh.2018.00040 ↗
70. Seasonality and zoonotic foodborne pathogens in Canada: Relationships between climate and Campylobacter, E.coli and Salmonella in meat products10.1017/S0950268819000797 ↗
71. Antibacterial activity of decontamination treatments for pig carcasses10.1016/j.foodcont.2011.01.013 ↗
72. High-pressure processing and boiling water treatments for reducing Listeria monocytogenes, Escherichia coli O157: H7, Salmonella spp., and Staphylococcus aureus during beef jerky processing10.1016/j.foodcont.2013.11.002 ↗
73. Reduction potential of steam vacuum and high-pressure water treatment on microbes during beef meat processing10.1016/j.foodcont.2019.106728 ↗
74. Meat safety and quality: A biological approach10.1111/ijfs.14602 ↗
75. Pre- and post-harvest interventions to reduce pathogen contamination in the U.S. beef industry10.1016/j.meatsci.2014.06.026 ↗
76. Anti-Campylobacter Probiotics: Latest Mechanistic Insights10.1089/fpd.2022.0039 ↗
77. The diversity of beef safety: A global reason to strengthen our current systems10.1016/j.meatsci.2017.03.015 ↗
78. Zdolec, N., Kotsiri, A., Houf, K., Alvarez-Ordóñez, A., Blagojevic, B., Karabasil, N., Salines, M., and Antic, D. (2022). Systematic Review and Meta-Analysis of the Efficacy of Interventions Applied during Primary Processing to Reduce Microbial Contamination on Pig Carcasses. Foods, 11.10.3390/foods11142110 ↗
79. Muriana, P.M., Eager, J., Wellings, B., Morgan, B., Nelson, J., and Kushwaha, K. (2019). Evaluation of antimicrobial interventions against E. Coli O157:H7 on the Surface of Raw Beef to Reduce Bacterial Translocation during Blade Tenderization. Foods, 8.10.20944/preprints201901.0139.v1 ↗
80. Vargas, D.A., Miller, M.F., Woerner, D.R., and Echeverry, A. (2021). Microbial growth study on pork loins as influenced by the application of different antimicrobials. Foods, 10.10.3390/foods10050968 ↗
81. Evaluating best practices for Campylobacter and Salmonella reduction in poultry processing plants10.3382/ps/pev328 ↗
82. Evaluation of antimicrobial activities of sequential spray applications of decontamination treatments on chicken carcasses10.5713/ajas.14.0708 ↗
83. Singh, M., and Thippareddi, H. (2023, March 16). Biomapping: An Effective Tool for Pathogen Control during Poultry Processing. Available online: https://extension.uga.edu/publications/detail.html?number=C1200&title=biomapping-an-effective-tool-for-pathogen-control-during-poultry-processing.
84. Dutta, V. (2023, March 16). The Importance of Leveraging Biomapping in Salmonella Control. Available online: https://www.foodqualityandsafety.com/article/the-importance-of-leveraging-biomapping-in-salmonella-control/.
85. Correlation between slaughter practices and the distribution of Salmonella and hygiene indicator bacteria on pig carcasses during slaughter10.1016/j.fm.2017.10.003 ↗
86. Process Mapping the Prevalence of Salmonella Contamination on Pork Carcass from Slaughter to Chilling: A Systematic Review Approach10.1089/fpd.2011.1040 ↗
87. Vargas, D.A., De Villena, J.F., Larios, V., Bueno, R., Ch, D.R., Casas, D.E., Jim, R.L., Blandon, S.E., and Sanchez-plata, M.X. (2023). Data-Mining Poultry Processing Bio-Mapping Counts of Management Decision Making. Foods, 12.10.3390/foods12040898 ↗
88. Casas, D.E., Manishimwe, R., Forgey, S.J., Hanlon, K.E., Miller, M.F., Brashears, M.M., and Sanchez-Plata, M.X. (2021). Biomapping of Microbial Indicators on Beef Subprimals Subjected to Spray or Dry Chilling over Prolonged Refrigerated Storage. Foods, 10.10.3390/foods10061403 ↗
89. Vargas, D.A., Rodríguez, K.M., Betancourt-Barszcz, G.K., Ajcet-Reyes, M.I., Dogan, O.B., Randazzo, E., Sánchez-Plata, M.X., Brashears, M.M., and Miller, M.F. (2022). Bio-Mapping of Microbial Indicators to Establish Statistical Process Control Parameters in a Commercial Beef Processing Facility. Foods, 11.10.3390/foods11081133 ↗
90. Visualizing samples with box plots10.1038/nmeth.2813 ↗
91. Kernel density estimation and its application10.1051/itmconf/20182300037 ↗
92. Papadochristopoulos, A., Kerry, J.P., Fegan, N., Burgess, C.M., and Duffy, G. (2021). Natural anti-microbials for enhanced microbial safety and shelf-life of processed packaged meat. Foods, 10.10.3390/foods10071598 ↗
93. Nicoli, M.C. (2012). Shelf Life Assessment of Food, CRC Press.10.1201/b11871 ↗
94. The most important attributes of beef sensory quality and production variables that can affect it: A review10.1016/j.livsci.2021.104573 ↗
95. United States Department of Agriculture (2023, March 16). Food Waste FAQs, Available online: https://www.usda.gov/foodwaste/faqs.
96. Vargas, D.A., Blandon, S.E., Sarasty, O., Osorio-Doblado, A.M., Miller, M.F., and Echeverry, A. (2022). Shelf-Life Evaluation of Pork Loins as Influenced by the Application of Different Antimicrobial Interventions. Foods, 11.10.3390/foods11213464 ↗
97. Shelf life of fresh meat products under LED or fluorescent lighting10.1016/j.meatsci.2016.02.032 ↗
98. The Relationship of Broiler Breast Color to Meat Quality and Shelf-Life10.1093/ps/77.2.361 ↗
99. Australian consumers’ attitudes to packaged fresh meat products with added microbial bioprotective cultures for shelf-life extension10.1016/j.meatsci.2023.109095 ↗
100. Effect of normal and modified atmosphere packaging on shelf life of roast chicken meat10.1111/jfs.12493 ↗