The Construction of an AI electronic nose system for characterization of a coffee aroma map in Chiang Rai province
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Abstract
Chiang Rai province in northern Thailand is now known for its unique and distinct Arabica coffee aroma, thanks to the region's highland climate and topography. To add value and promote this aroma, a team of researchers developed an AI electronic noses system that detects and maps the coffee aroma from different areas of Chiang Rai. The system uses several gas sensor detectors that can detect gas in the range of 10-1000 ppm, covering 10 different gases from coffee beans. Detectors are connected to an Arduino ESP32 processor board and controlled by a Python program. The system responds to the sensor for measuring the coffee aroma and transmits data via the internet WIFI in the form of IOT protocol. It then sends data to store on a cloud web service and displays real-time coffee aroma maps on an online website. These maps can provide valuable insights into the distinct aroma of coffee beans grown in different areas of Chiang Rai. Moreover, the data collected can be used to develop AI electronic noses that can identify the unique aroma of coffee beans grown in the area. This will further enhance the collection of coffee aroma maps and add value to the coffee beans produced in Chiang Rai. The aroma maps can also help in the branding and marketing of the coffee beans, making it more recognizable and appealing to consumers worldwide.
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References
Barea-Ramos, J.D., Cascos, G., Mesías, M., Lozano, J. & Martín-Vertedor, D. (2022). Evaluation of the olfactory quality of roasted coffee beans using a digital nose. Sensors, 22, 8654.
Bhumiratana, N., & Adhikari, K. (2017). Sensory evaluation of foods. In K. Adhikari, L. Nollet, & F. Toldrá (Eds.), Handbook of sensory methods and analysis (pp. 1-22). CRC Press.
Bonah, E., Huang, X., Aheto, J.H. & Osae, R. (2020). Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review. J Food Sci Technol, 57, 1977-1990.
Borowik, P., Adamowicz, L., Tarakowski, R., Siwek, K. & Grzywacz, T. (2020). Odor detection using an E-nose with a reduced sensor array. Sensors, 20, 3542.
Chen, Y., Chen, H., Cui, D., Fang, X., Gao, J. & Liu, Y. (2022). Fast and non-destructive profiling of commercial coffee aroma under three conditions (beans, powder, and brews) using GC-IMS. Molecules, 27, 6262.
Di Natale, C., Paolesse, R., Macagnano, A., Mantini, A., D’Amico, A., Pennazza, G., & Bernabei, M. (2003). Electronic noses for environmental monitoring applications. Sensors and Actuators B: Chemical, 93(1), 231-236.
dos Santos, H.D. & Boffo, E.F. (2021). Coffee beyond the cup: analytical techniques used in chemical composition research—a review. Eur Food Res Technol, 247, 749-775.
Dung, T.T., Oh, Y., Choi, S.-J., Kim, I.-D., Oh, M.-K. & Kim, M. (2018). Applications and advances in bioelectronic noses for Odour sensing. Sensors, 18, 103.
Elhalis, H., Cox, J., Frank, D. & Zhao, J. (2021). The role of wet fermentation in enhancing coffee flavor, aroma and sensory quality. Eur Food Res Technol, 247, 485-498.
Farah, A. (2012). Coffee constituents. In M. Y. Yi-Chong (Ed.), Comprehensive natural products II: chemistry and biology (pp. 295-324). Elsevier.
Ferreira, S. R. S., de Oliveira, L. S., Rocha, M. A. M., Silva, M. A., & Fialho, E. M. P. (2016). Electronic noses in beverage analysis. In M. C. U. Araújo & L. C. C. Meisel (Eds.), Electronic noses and tongues in food science (pp. 69-87). Academic Press.
Gardner, J. W., & Bartlett, P. N. (1994). A brief history of electronic noses. Sensors and Actuators B: Chemical, 18(1), 210-211. ICO. (2021). Coffee Market Report - August 2021. International Coffee Organization.https://www.ico. org/documents/cy2020-21/ cmr-0821-e.pdf
Illy, A., & Viani, R. (2005). Espresso coffee: the science of quality (2nd ed.). Elsevier.
John, A.T., Murugappan, K., Nisbet, D.R. & Tricoli, A. (2021). An outlook of recent advances in chemiresistive sensor-based electronic nose systems for food quality and environmental monitoring. Sensors, 21, 2271.
Karakaya, D., Ulucan, O. & Turkan, M. (2020). Electronic nose and its applications: A survey. Int. J. Autom. Comput., 17, 179-209.
Lai, K. M., Kim, H. K., Kim, K. O., Kim, S. K., & Kim, J. H. (2020). Coffee roasting level classification using an electronic nose system. Journal of the Science of Food and Agriculture, 100(14), 5202-5209.
Lehane, D., & Olah, T. (2018). Coffee flavour: an overview. Flavour, 7(1), 1-12.
Misra, N.N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R. & Martynenko, A. (2022). IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet Things J, 9, 6305-6324.
Münchow, M., Alstrup, J., Steen, I. & Giacalone, D. (2020). Roasting conditions and coffee flavor: A multi-study empirical investigation. Beverages, 6, 29.
Park, C., Lee, J., Kim, Y., Park, J.G., Kim, H. & Hong, D. (2023). An enhanced AI-based network intrusion detection system using generative adversarial networks. IEEE Internet Things J, 10, 2330-2345.
Pereira, L.L., Marcate, J.P.P., Caliman, A.D.C., Guarçoni, R.C. & Moreli, A.P. (2021). Physical classification and sensory coffee analysis. Quality determinants in coffee production. 842, 373-405.
Rocchetti, G., Braceschi, G.P., Odello, L., Bertuzzi, T., Trevisan, M. & Lucini, L. (2020). Identification of markers of sensory quality in ground coffee: an untargeted metabolomics approach. Metabolomics, 16, 127.
Rocha, M. A. M., Filho, P. F., Silva, M. A., Rocha, A. G., Araujo, M. C. U., Lima, R. L. S., & Fialho, E. M. P. (2018). Electronic nose in coffee analysis: a review. Food Research International, 105, 380-389.
Seninde, D. R., & Chambers, E. IV. (2020). Coffee flavor: A review. Beverages, 6, 44.
Specialty Coffee Association of America (SCAA). (2021). Sensory evaluation. Retrieved. https://scanews.coffee/ section/research/sensory-evaluation/
Yamanaka, K., Nishimura, O., Yamazaki, T., & Toko, K. (2020). Coffee bean aroma detection using metal oxide gas sensor array and deep neural network. Sensors and Actuators B: Chemical, 309, 127719.