Approaches to the Algorithm of Analyzing the Results of Laboratory Testing of Micro- and Macronutrient Content of Bakery Products: Part 1
https://doi.org/10.35627/2219-5238/2022-30-4-44-53
Abstract
Introduction: Data on the chemical composition of food products are important for solving many problems in medical and social spheres. The development of mechanisms for updating existing databases of the chemical composition of foodstuffs, including the need to change approaches to obtaining primary data and develop algorithms of their processing, is in de-
mand.
Objective: To develop an algorithm of obtaining statistically correct values of average concentrations and variability of the main micro– and macronutrients in bakery products.
Materials and methods: To develop and test the algorithm, we used the results of testing bakery products obtained in 2020 within the Federal Project on Public Health Strengthening by the laboratories of the Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor).
Results: A good separating power was demonstrated by kmeans clustering into two groups by the fat content. An algorithm for generalization of data obtained from different laboratories is proposed due to impossibility to assess the whole aggregate of potential errors related to testing, laboratory personnel, data entry, etc. To assess the effectiveness of each stage and the algorithm as a whole, we used the value of the deviation of the resulting variability from the initial one. As a result of processing, this indicator ranged from 5 % for the carbohydrate content to 72 % for the fat content. For the contents of car - bohydrates, ash, dietary fiber, vitamin B1, sodium and moisture in both clusters, statistically significant differences were obtained between the processed and original data. This result and the comparability of the obtained values of the mean and variability with the reference ones may indicate the correctness of the algorithm. There were no statistically significant differences between the obtained values of fat and protein content, but the consistency of the order of values with the reference
ones was also recorded.
Conclusion: The developed algorithm made it possible to obtain up-to-date information about the chemical composition of bakery products. Further research should be aimed at testing and, if necessary, adjusting the algorithm for all major food groups.
Keywords
About the Authors
G. D. ShcherbakovRussian Federation
Grigory D. Shcherbakov, Head of the Department, postgraduate student
Department of Public Health Monitoring, Analysis and Forecasting
109240
2/14 Ustyinsky Driveway
Moscow
V. V. Bessonov
Russian Federation
Vladimir V. Bessonov, Dr. Sci. (Biol.), Head of the Laboratory
Laboratory of Food Chemistry
109240
2/14 Ustyinsky Driveway
Moscow
References
1. Healthy Diet. WHO Fact Sheets. Accessed April 23, 2022. https://www.who.int/news-room/fact-sheets/detail/healthy-diet
2. Popova A. Yu., Tutelyan V. A., Nikityuk D. B. On the new (2021) Norms of physiological requirements in energy and nutrients of various groups of the population of the Russian Federation. Voprosy Pitaniya. 2021; 90 (4 (536)): 6-19. (In Russ.) doi: 10.33029/0042-8833-2021-90-4-6-19
3. Samman N., Rossi M. C. 12th IFDC 2017 Special Issue – Challenges facing the establishment and management of a national food composition database in Argentina. J Food Compos Anal. 2019; 84 (132): 103292. doi: 10.1016/j.jfca.2019.103292
4. Silva M., Ribeiro M., Viegas O., et al. Exploring two food composition databases to estimate nutritional components of whole meals. J Food Compos Anal. 2021; 102: 104070. doi: 10.1016/j.jfca.2021.104070
5. Jeddi M. Z., Boon P. E., Cubadda F., et al. A vision on the ‘foodture’ role of dietary exposure sciences in the inter-play between food safety and nutrition. Trends Food Sci Technol. 2022; 120: 288-300. doi: 10.1016/j.tifs.2022.01.024
6. Bessonov V. V., Bogachuk M. N., Bokov D. O., et al. Databases of the chemical composition of foods in the era of digital nutrition science. Voprosy Pitaniya. 2020; 89 (4): 211-219. (In Russ.) doi: 10.24411/0042-8833-2020-10058
7. Scrimshaw N. S. INFOODS: the international network of food data systems. Am J Clin Nutr. 1997; 65 (4 Suppl): 1190S-1193S. doi: 10.1093/ajcn/65.4.1190S
8. FAO/INFOODS Guidelines for Converting Units, Denominators, and Expressions – Version 1.0. FAO, Rome; 2012.
9. FAO/INFOODS Guidelines for Checking Food Composition Data prior to the Publication of a User Table/Database – Version 1.0. FAO, Rome; 2012.
10. Machackova M., Giertlova A., Porubska J., Roe M., Ramos C., Finglas P. EuroFIR Guideline on calculation of nutrient content of foods for food business operators. Food Chem. 2017; 238: 35-41. doi: 10.1016/j.foodchem.2017.03.103
11. Schubert A., Holden J. M., Wolf W. R. Selenium content of a core group of foods based on a critical evaluation of published analytical data. J Am Diet Assoc. 1987;87 (3): 285-99.
12. West C. E., Poortvliet E. J. The carotenoid content of foods with special reference to developing countries (Report). 1993. Accessed April 23, 2022. https://agris.fao.org/agris-search/search.do?recordID=XF2016032908
13. Holden J. M., Lemar L. E., Exler J. Vitamin D in foods: development of the US Department of Agriculture database. Am J Clin Nutr. 2008;87(4): 1092S-1096S. doi: 10.1093/ajcn/87.4.1092S
14. Raffo A., La Malfa G., Fogliano V., Maiani G., Quaglia G. Seasonal variations in antioxidant components of cherry tomatoes (Lycopersicon esculentum cv. Naomi F1). J Food Compos Anal. 2006; 19 (1): 11-19. doi: 10.1016/j.jfca.2005.02.003
15. Luthria D. L., Pastor-Corrales M. A. Phenolic acids content of fifteen dry edible bean (Phaseolus vulgaris L.) varieties. J Food Compos Anal. 2006; 19 (2–3): 205-211. doi: 10.1016/j.jfca.2005.09.003
16. Sarac I., Butnariu M. Food pyramid – The principles of a balanced diet. Int J Nutr Pharmacol Neurol Dis. 2020; 5 (2): 24-31. doi: 10.14302/issn.2379-7835.ijn-20-3199
17. Lockyer S., Spiro A. The role of bread in the UK diet: An update. Nutr Bull. 2020; 45 (2): 133-164. doi: 10.1111/nbu.12435
18. Bati A. The role of bread in Hungarian diet today. Acta Ethnographica Hungarica. 2012; 57 (2): 253-261. URL: https://www.researchgate.net/publication/279038728_The_role_of_bread_in_Hungarian_diet_today
19. Xu X., Liu H., Li L., Yao M. A comparison of outlier detection techniques for high-dimensional data. Int J Comput Intell Syst. 2018; 11 (1): 652. doi: 10.2991/ijcis.11.1.50
20. Das D., Nayak M., Pani S. K. Missing value imputation – A review. Int J Comput Sci Eng. 2019; 7 (4): 548-558. doi: 10.26438/ijcse/v7i4.548558
21. El-Bakry M., Ali F., El-Kilany A., Mazen S. Fuzzy based techniques for handling missing values. Int J Adv Comput Sci Appl. 2021; 12 (3). doi: 10.14569/IJACSA.2021.0120306
22. Nadraga V., Smirnov V., Boiko O., Dereko V. Comparison of missing values handling techniques using MICE package tools of R software and logistic regression model. In: Babichev S., Lytvynenko V., Wójcik W., Vyshemyrskaya S., eds. Lecture Notes in Computational Intelligence and Decision Making. Springer, Cham; 2021; 1246: 39-50. doi: 10.1007/978-3-030-54215-3_3
23. Pekel A. Y., Çalık A., Alataş M. S., et al. Evaluation of correlations between nutrients, fatty acids, heavy metals, and deoxynivalenol in corn (Zea mays L.). J Appl Poult Res. 2019; 28 (1): 94-107. doi: 10.3382/japr/pfy023
24. Pollard S., Namazi H., Khaksar R. Big data applications in food safety and quality. In: Encyclopedia of Food Chemistry. Academic Press; 2019: 356-363. doi: 10.1016/b978-0-08-100596-5.21839-8
25. Rashid W., Gupta M. K. A perspective of missing value imputation approaches. In: Gao X. Z., Tiwari S., Trivedi M., Mishra K., eds. Advances in Computational Intelligence and Communication Technology. Springer, Singapore; 2021; 1086: 307-315. doi: 10.1007/978-981-15-1275-9_25
26. Amano S., Aizawa K., Ogawa M. Food category representatives: Extracting categories from meal names in food recordings and recipe data. 2015 IEEE International Conference on Multimedia Big Data; 2015: 48-55. doi: 10.1109/BigMM.2015.54
27. Anzawa M., Amano S., Yamakata Y., Yamasaki T., Aizawa K., Ogawa M. Generation of representative meal names for food recording data by using web search results. 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW); 2016: 1-6. doi: 10.1109/ICMEW.2016.7574745
28. Ahmed M., Seraj R., Syed Mohammed Shamsul Islam. The k-means algorithm: A comprehensive survey and performance evaluation. Electronics. 2020; 9 (8): 1295. doi: 10.3390/electronics9081295
29. Ruvuna F., Flores D., Mikrut B., De La Gana K., Fong S. Generalized lab norms for standardizing data from multiple laboratories. Drug Inf J. 2003; 37 (1): 61–79. doi: 10.1177/009286150303700109
30. Brunden M. N., Clark J. J., Sutter M. L. A general method of determining normal ranges applied to blood values for dogs. Am J Clin Pathol. 1970; 53 (3): 332-339. doi: 10.1093/ajcp/53.3.332
31. Herrera L. The precision of percentiles in establishing normal limits in medicine. J Lab Clin Med. 1958; 52 (1): 34-42.
32. Charrad M., Ghazzali N., Boiteau V., Niknafs A. NbClust: An R package for determining the relevant number of clusters in a data set. J Stat Softw. 2014; 61 (6): 1-36. doi: 10.18637/jss.v061.i06
Review
For citations:
Shcherbakov G.D., Bessonov V.V. Approaches to the Algorithm of Analyzing the Results of Laboratory Testing of Micro- and Macronutrient Content of Bakery Products: Part 1. Public Health and Life Environment – PH&LE. 2022;(4):44-53. (In Russ.) https://doi.org/10.35627/2219-5238/2022-30-4-44-53