Developing a Chinese Food Nutrient Data Analysis System for Precise Dietary Intake Management

  • Xiaowei Xu
  • Li Hou
  • Zhen Guo
  • Ju Wang
  • Jiao LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10968)


A big mount of dietary data can be recorded in the daily life with the development of Internet of Things (e.g., RFID-equipped food carriers and food vending machines). Via monitoring and analyzing of personal dietary, it can provide valuable information for disease diagnosis, body weight control, and dietary habit management. The big data analysis benefits for patients, dieters, nutritionists and individuals who concern their health. While various techniques have been used for dietary monitoring in clinical trials and user studies, they are not ready for daily use. Existing solutions either require tedious manual recording or may impede normal daily activities. In this paper, we designed a smart big data framework using RFID technology to analyze the nutrition intake from dietary every day. The framework is capable to record Chinese food dietary information efficiently and effectively. It is promising for individuals and dietarians to set up personalized nutrient plan in the future.


Big data Dietary record Nutrition analysis Chinese food 



This study was supported by the Key Laboratory of Medical Information Intelligent Technology Chinese Academy of Medical Sciences, The National Key Research and Development Program of China (Grant No. 2016YFC0901901), the National Population and Health Scientific Data Sharing Program of China, and the Knowledge Centre for Engineering Sciences and Technology (Medical Centre).


  1. 1.
    Saslow, L.R., Mason, A.E., Kim, S., Goldman, V., Ploutz-Snyder, R., Bayandorian, H., Daubenmier, J., Hecht, F.M., Moskowitz, J.T.: An online intervention comparing a very low-carbohydrate ketogenic diet and lifestyle recommendations versus a plate method diet in overweight individuals with type 2 diabetes: a randomized controlled trial. J. Med. Internet Res. 19(2), e36 (2017). PMID: 28193599CrossRefGoogle Scholar
  2. 2.
    Jospe, M.R., Fairbairn, K.A., Green, P., Perry, T.L.: Diet app use by sports dietitians: a survey in five countries. JMIR mHealth uHealth 3(1), e7 (2015)CrossRefGoogle Scholar
  3. 3.
    Desroches, S., Lapointe, A., Ratté, S., Gravel, K., Légaré, F., Turcotte, S.: Interventions to enhance adherence to dietary advice for preventing and managing chronic diseases in adults. Cochrane Database Syst. Rev. 2, CD008722 (2013). Medline: 23450587
  4. 4.
    Dhurandhar, N.V., Thomas, D.: The link between dietary sugar intake and cardiovascular disease mortality: an unresolved question. JAMA 313(9), 959–960 (2015). Medline: 25734737CrossRefGoogle Scholar
  5. 5.
    Mahabir, S., Baer, D.J., Giffen, C., Subar, A., Campbell, W., Hartman, T.J., et al.: Calorie intake misreporting by diet record and food frequency questionnaire compared to doubly labeled water among postmenopausal women. Eur. J. Clin. Nutr. 60(4), 561–565 (2006). Medline: 16391574CrossRefGoogle Scholar
  6. 6.
    Probst, Y.C., Tapsell, L.C.: Overview of computerized dietary assessment programs for research and practice in nutrition education. J. Nutr. Educ. Behav. 37(1), 20–26 (2005). Medline: 15745652CrossRefGoogle Scholar
  7. 7.
    Forster, H., Walsh, M.C., Gibney, M.J., Brennan, L., Gibney, E.R.: Personalised nutrition: the role of new dietary assessment methods. Proc. Nutr. Soc. 75(1), 96–105 (2016). Medline: 26032731CrossRefGoogle Scholar
  8. 8.
    Subar, A.F., Kipnis, V., Troiano, R.P., Midthune, D., Schoeller, D.A., Bingham, S., et al.: Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN study. Am. J. Epidemiol. 158(1), 1–13 (2003). Medline: 12835280CrossRefGoogle Scholar
  9. 9.
    Champagne, C.M., Baker, N.B., DeLany, J.P., Harsha, D.W., Bray, G.A.: Assessment of energy intake underreporting by doubly labeled water and observations on reported nutrient intakes in children. J. Am. Diet. Assoc. 98(4), 426–433 (1998). Medline: 9550166CrossRefGoogle Scholar
  10. 10.
    Gersovitz, M., Madden, J.P., Smiciklas-Wright, H.: Validity of the 24-hr. dietary recall and seven-day record for group comparisons. J. Am. Diet. Assoc. 73(1), 48–55 (1978). Medline: 659761Google Scholar
  11. 11.
    Australian Bureau of Statistics. Australian Health Survey: Users’ Guide, 2011–2013: Under-Reporting in Nutrition Surveys (2014). Accessed 09 Aug 2016. WebCite Cache ID 6jcWW3HQR
  12. 12.
    Moshfegh, A.J., Rhodes, D.G., Baer, D.J., Murayi, T., Clemens, J.C., Rumpler, W.V., et al.: The US department of agriculture automated multiple-pass method reduces bias in the collection of energy intakes. Am. J. Clin. Nutr. 88(2), 324–332 (2008). FREE Full text. Medline: 18689367CrossRefGoogle Scholar
  13. 13.
    Anton, S.D., LeBlanc, E., Allen, H.R., Karabetian, C., Sacks, F., Bray, G., et al.: Use of a computerized tracking system to monitor and provide feedback on dietary goals for calorie-restricted diets: the POUNDS LOST study. J. Diabetes Sci. Technol. 5, 1216–1225 (2012). FREE Full text. Medline: 23063049CrossRefGoogle Scholar
  14. 14.
    Springvloet, L., Lechner, L., Oenema, A.: Planned development and evaluation protocol of two versions of a web-based computer-tailored nutrition education intervention aimed at adults, including cognitive and environmental feedback. BMC Public Health 14, 47 (2014). FREE Full text. Medline: 24438381CrossRefGoogle Scholar
  15. 15.
    Charney, P., Peterson, S.J.: Practice paper of the academy of nutrition and dietetics abstract: critical thinking skills in nutrition assessment and diagnosis. J. Acad. Nutr. Diet. 113(11), 1545 (2013). Scholar
  16. 16.
    Daugherty, B.L., Schap, T.E., Ettienne-Gittens, R., Zhu, F.M., Bosch, M., Delp, E.J., et al.: Novel technologies for assessing dietary intake: evaluating the usability of a mobile telephone food record among adults and adolescents. J. Med. Internet Res. 14(2), e58 (2012). Medline: 22504018CrossRefGoogle Scholar
  17. 17.
    Raatz, S.K., Scheett, A.J., Johnson, L.K., Jahns, L.: Validity of electronic diet recording nutrient estimates compared to dietitian analysis of diet records: randomized controlled trial. J. Med. Internet Res. 17(1), e21 (2015). PMID: 25604640CrossRefGoogle Scholar
  18. 18.
    Roy, W.: An introduction to RFID technology. IEEE Pervasive Comput. 5(1), 25–33 (2010)MathSciNetGoogle Scholar
  19. 19.
    Shepard, S.: RFID: radio frequency identification. McGraw-Hill, New York (2005). ISBN 9780071442992Google Scholar
  20. 20.
    Chen, P.H., Liang, Y.H., Lin, T.C.: Using E-Plate to implement a custom dietary management system. In: International Symposium on Computer, Consumer and Control, pp. 978–981 (2014)Google Scholar
  21. 21.
    Chen, P.H., Liang, Y.H., Chou, W.C.: E-tag plate application for dietary management. In: International Symposium on Computer, Consumer and Control. pp. 223–226 (2014)Google Scholar
  22. 22.
    The Sovell Science and Technology Limited Company (2017). Accessed 7 Jul 2017. WebCite Cache ID 6tIHI2Guf (in Chinese)
  23. 23.
    Yang, Y.X., Wang, G.Y., Pan, X.C.: China food composition. Peking University Medical Press, Beijing (2009). ISBN 9787811167276Google Scholar
  24. 24.
    Yang, Y.X.: China food composition. Peking University Medical Press, Beijing (2004). ISBN 9787810716789Google Scholar
  25. 25.
    Matthews, R., Garrison, Y.: Agriculture handbook No. 102: Food yields summarized by different stages of preparation. USDA Agricultural Research Service, Washington, DC (1975)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xiaowei Xu
    • 1
  • Li Hou
    • 1
  • Zhen Guo
    • 1
  • Ju Wang
    • 2
  • Jiao Li
    • 1
    Email author
  1. 1.Institute of Medical InformationChinese Academy of Medical Sciences/Peking Union Medical CollegeBeijingChina
  2. 2.The University of Texas Health Science Center at HoustonHoustonUSA

Personalised recommendations