|Paper title:||Software and Hardware Design Specifications for Quantifying Carbohydrate Contents in Food|
|Published in:||Issue 2, (Vol. 14) / 2020|
|Author(s):||GAMBO Ishaya, ALABI Oluwasegun T, OLUFOKUNBI Karen, MASSENON Rhodes|
|Abstract.||Analysis and detection of carbohydrate contents in food have been achieved in the past using different methods. However, these methods require pre-treatment and chemical reaction, which tamper with the sample composition, thereby making it unhealthy for consumption afterwards. A non- destructive method that will preserve the composition of the food sample– while giving an estimated result– is crucial. In this paper, a spectrometer was designed and built that can detect light spectrum penetrating through food samples. The goal is to assist diabetic patients and the general public to identify the amount of carbohydrate in their food for proper medical ration. The paper adopted the experimental research approach. An optical approach was employed using Near-Infrared LED to design a spectrometer that scans through food samples to obtain a spectrum using the TCD1304CD image sensor. The output was extracted using Arduino Uno Microcontroller which runs through a supervised machine learning algorithm, first to train the algorithm, and later to make the prediction using the algorithm. We trained the machine learning model using standard food samples with known composition. The system was tested using standard food samples which are not in the training dataset. The test result obtained was evaluated in comparison to the nutritional data of the testing samples. More so, it shows an average deviation of 46.87% of the expected values provided in the nutritional data of the sample.|
|Keywords:||Spectrometry, Near-Infrared Radiation, Carbohydrate, Machine Learning, Multiple Linear Regression, Feature Scaling, Diabetes Mellitus|
1. S. Chen, O. Sokolsky, J. Weimer and I. Lee, “Data-driven adaptive safety monitoring using virtual subjects in medical cyber-physical systems: a glucose control case study”, Journal of Computer Science and Engineering, 10(3), 75-84, 2016. https://doi.org/10.5626/JCSE.2016.10.3.75
2. E. Jovanov, A. Milenkovic, C. Otto and P. C. De Groen, “A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation”, Journal of NeuroEngineering and Rehabilitation, 2(1), 6, 2005. https://doi.org/10.1186/1743-0003-2-6
3. A. L. Bossen, H. Kim, K. N. Williams, A. E. Steinhoff and M. Strieker, “Emerging roles for telemedicine and smart technologies in dementia care”, Smart homecare technology and telehealth, 3, 49–57, 2015. https://doi.org/10.2147/SHTT.S59500
4. M. I. Pramanik, R. Y. Lau, H., Demirkan and M. A. K. Azad, Smart health: Big data-enabled health paradigm within smart cities. Expert Systems with Applications, 87, 370-383, 2017. https://doi.org/10.1016/j.eswa.2017.06.027
5. S. Das, A. Dey, A. Pal and N. Roy, “Applications of artificial intelligence in machine learning: review and prospect”, International Journal of Computer Applications, 115(9), 31-41. 2015.
6. E. Wiercigroch, E. Szafraniec, K. Czamara, M. Z. Pacia, K. Majzner, K. Kochan and K. Malek, “Raman and infrared spectroscopy of carbohydrates: A review”, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 185, 317-335, 2017. https://doi.org/10.1016/j.saa.2017.05.045
7. Q. Zou, K. Qu, Y. Luo, D. Yin, Y. Ju and H. Tang, “Predicting diabetes mellitus with machine learning techniques”, Frontiers in genetics, 9, 515-524, 2018. https://doi.org/10.3389/fgene.2018.00515
8. O. M. S. Las, principales causas de defunción. 2018.Internet..Consultado febrero 2019.. Disponible en: Disponible en: https://www. who. int/es/news-room/fact-sheets/detail/the-top-10-causes-of-death. 2018.
9. M. Callejas-Cuervo, J. P. C. Barrera and D. L. C. Rengifo, “Technological Platform for the Control of the Medication Supply to People with Diabetes”, In International Conference on Smart Technologies, Systems and Applications, 288-296, 2019. Springer, Cham.
10. A. Lonappan, G. Bindu, V. Thomas, J. Jacob, C. Rajasekaran and K. T. Mathew, “Diagnosis of diabetes mellitus using microwaves”, Journal of Electromagnetic Waves and Applications, 21(10), 1393-1401, 2007.
11. G. Matfin, (Ed.), Endocrine and Metabolic Medical Emergencies: A Clinician's Guide, 2018. John Wiley & Sons.
12. F. Chentli, S. Azzoug and S. Mahgoun, “Diabetes mellitus in elderly”, Indian journal of endocrinology and metabolism, 19(6), 744, 2015.
13. H. Wang, J. Peng, C. Xie, Y. Bao and Y. He, “Fruit quality evaluation using spectroscopy technology: a review”, Sensors, 15(5), 11889-11927, 2015.
14. B. N. Dar and S. A. Mir, (Eds.), Emerging Technologies for Shelf-life Enhancement of Fruits, 2020. CRC Press.
15. M. Herrero, A. Cifuentes, E. Ibáñez and M. D. del Castillo, “Advanced analysis of carbohydrates in foods”, Methods of Analysis of Food Components and Additives, 135-164, 2011.
16. Muhammad and Z. Yan, “Supervised Machine Learning Approaches: A survey”, ICTACT Journal on Soft Computing, 5(3), 946-952, 2015.
17. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas and I. Chouvarda, “Machine learning and data mining methods in diabetes research”, Computational and structural biotechnology journal, 15, 104-116, 2017.
18. N. BeMiller, “Carbohydrate analysis”, In Food Analysis, 147-177, 2010. Springer, Boston, MA.
19. N. BeMiller, “Carbohydrate analysis” In Food Analysis, 333-360. 2017. Springer, Cham.
20. C. F. O. R. Melo, L. C. Navarro, D. N. De Oliveira, T. M. Guerreiro, E. D. O. Lima, J. Delafiori and K. N. Morishita, “A machine learning application based in random forest for integrating mass spectrometry-based metabolomic data: A simple screening method for patients with zika virus”, Frontiers in bioengineering and biotechnology, 6, 31-40, 2018. https://doi.org/10.3389/fbioe.2018.00031
21. R. Goodacre, “Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rules”, Vibrational Spectroscopy, 32(1), 33-45, 2003. https://doi.org/10.1016/S0924-2031(03)00045-6
22. A. Nawrocka and J. Lamorska, “Determination of food quality by using spectroscopic methods”, In Advances in agrophysical research, 347-367, 2013. IntechOpen,
23. E. C. Y. Li‐Chan, A. A. Ismail, J. Sedman and F. R. Van de Voort, “Vibrational spectroscopy of food and food products”, Handbook of vibrational spectroscopy, 3630 – 3662, 2006. https://doi.org/10.1002/0470027320.s6501
24. Y. Zhang and X. Liu, “Machine learning techniques for mass spectrometry imaging data analysis and applications”, Journal of Bioanalysis, 10(8), 1-5, 2018. https://doi.org/10.4155/bio-2017-0281
25. B. G. Osborne, “Near‐infrared spectroscopy in food analysis”, Encyclopedia of analytical chemistry: applications, theory and instrumentation, 1-14, 2006. https://doi.org/10.1002/9780470027318.a1018
26. Dhikale, A. Shimpi, R. Raut ja and D. Ghogare, “Microcontroller Based Visible Light Spectrometer“, IOSR Journal of Computer Engineering, Recent Trends in Computer Technology & Communication 2k16" (RTCTC-2k16), 18-21, 2016.
27. A. Hasan, M. K. Hasan and M. A. Mottalib, “Linear regression-based feature selection for microarray data classification”, International journal of data mining and bioinformatics, 11(2), 167-179, 2015, https://doi.org/10.1504/IJDMB.2015.066776.
28. S. Rong and Z. Bao-wen, “The research of regression model in machine learning field”, In MATEC Web of Conferences, 176, 01033, 2018. EDP Sciences.
29. X. Wan, “Influence of feature scaling on convergence of gradient iterative algorithm”, In Journal of Physics: 21
Computer Science Section Conference Series, 1213(3), 032021, 2019. IOP Publishing.
30. R. Aishwarya and P. Gayathri, “A method for classification using machine learning technique for diabetes”, International Journal of Engineering and Technology (IJET), 5(3), 2903-2908, 2013.
31. Y. Lee, K. H. Kim, J. J. Kang, S. J. Choi, Y. S. Im, Y. D. Lee and Y. S. Lim, “Comparison and analysis of linear regression and artificial neural network”, International Journal of Applied Engineering Research, 12(20), 9820-9825, 2017.
32. S. R. Sankranti, “Stock Prediction Analysis by using Linear Regression Machine Learning Algorithm”, International Journal of Innovative Technology and Exploring Engineering, 9(4), 841-844, 2020.
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