Skip to main content

Food Classification for Inflammation Recognition Through Ingredient Label Analysis: A Real NLP Case Study

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1251))

Included in the following conference series:

Abstract

As of late, literature shows that food intolerances affect a large portion of the world population. Diagnosis and Prevention are essential to avoid possible adverse responses due to food ingestion. Concerning this point, consumers and industry players are also demanding tools useful to warn individuals about the composition of commercial products. In this scenario, Natural Language Processing (NLP) approaches can be very useful to classify foods into the right intolerance group given their ingredients. In this work, we evaluate and compare different deep and shallow learning techniques, such as Linear Support Vector Machine (Linear SVM), Random Forest, Dense Neural Networks (Dense NN), Convolutional Neural Networks (CNN), and Long short-term memory (LSTM) with different feature extraction techniques like Bag of Word (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and Word2Vec, in order to solve this task on real commercial products, aiming to create a baseline for future works and a software-product. In the end, interesting and noticeable results have been achieved and the baselines have been identified into the Linear SVM and the Dense NN with Bag of Words or with the combination of Bag of Words, TF-IDF and Word2Vec.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Altschul, A.S., Scherrer, D.L., Muñoz-Furlong, A., Sicherer, S.H.: Manufacturing and labeling issues for commercial products: relevance to food allergy. J. Allergy Clin. Immunol. 108(3), 468 (2001)

    Article  Google Scholar 

  2. De Silva, D., Geromi, M., Halken, S., Host, A., Panesar, S.S., Muraro, A., Werfel, T., Hoffmann-Sommergruber, K., Roberts, G., Cardona, V., et al.: Primary prevention of food allergy in children and adults: systematic review. Allergy 69(5), 581–589 (2014)

    Article  Google Scholar 

  3. Enriquez, F., Troyano, J., López-Solaz, T.: An approach to the use of word embeddings in an opinion classification task. Expert Syst. Appl. 66, 1–6 (2016)

    Article  Google Scholar 

  4. Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Advances in neural information processing systems, pp. 1019–1027 (2016)

    Google Scholar 

  5. Khan, W., Daud, A., Nasir, J.A., Amjad, T.: A survey on the state-of-the-art machine learning models in the context of NLP. Kuwait J. Sci. 43, 95–113 (2016)

    MathSciNet  Google Scholar 

  6. Khatua, A., Khatua, A., Cambria, E.: A tale of two epidemics: contextual Word2Vec for classifying twitter streams during outbreaks. Inf. Process. Manag. 56(1), 247–257 (2019)

    Article  Google Scholar 

  7. Loper, E., Bird, S.: NLTK: the natural language toolkit. In: Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics - Volume 1 (ETMTNLP 2002), Stroudsburg, PA, USA, pp. 63–70. Association for Computational Linguistics (2002)

    Google Scholar 

  8. Muraro, A., Werfel, T., Hoffmann-Sommergruber, K., Roberts, G., Beyer, K., Bindslev-Jensen, C., Cardona, V., Dubois, A., Dutoit, G., Eigenmann, P., et al.: EAACI food allergy and anaphylaxis guidelines: diagnosis and management of food allergy. Allergy 69(8), 1008–1025 (2014)

    Article  Google Scholar 

  9. Riloff, E.: Little words can make a big difference for text classification. In: SIGIR, vol. 95, pp. 130–136 (1995)

    Google Scholar 

  10. Sarma, P.K., Liang, Y., Sethares, W.A.: Domain adapted word embeddings for improved sentiment classification. arXiv preprint arXiv:1805.04576 (2018)

  11. Scuderi, A., Pecorino, B.: Protected designation of origin (PDO) and protected geographical indication (PGI) Italian citrus productions. Acta Hortic. 1065, 1911–1917 (2015)

    Article  Google Scholar 

  12. Sicherer, S.H.: Food Allergy: Practical Diagnosis and Management. CRC Press, Boca Raton (2016)

    Google Scholar 

  13. Simons, E., Weiss, C.C., Furlong, T.J., Sicherer, S.H.: Impact of ingredient labeling practices on food allergic consumers. Ann. Allergy Asthma Immunol. 95(5), 426–428 (2005)

    Article  Google Scholar 

  14. Speciani, A.F., Soriano, J., Speciani, M.C., Piuri, G.: Five great food clusters of specific IgG for 44 common food antigens. A new approach to the epidemiology of food allergy. Clin. Transl. Allergy 3(S3), P67 (2013)

    Article  Google Scholar 

  15. Toman, M., Tesar, R., Jezek, K.: Influence of word normalization on text classification. Proc. InSciT 4, 354–358 (2006)

    Google Scholar 

  16. Wu, Q., Ye, Y., Zhang, H., Ng, M.K., Ho, S.-S.: ForesTexter: an efficient random forest algorithm for imbalanced text categorization. Knowl.-Based Syst. 67, 105–116 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stefano Campese .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Campese, S., Pozza, D. (2021). Food Classification for Inflammation Recognition Through Ingredient Label Analysis: A Real NLP Case Study. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-55187-2_15

Download citation

Publish with us

Policies and ethics