Thanh bên bài viết
Chuyên mục
Hội nghị Khoa học Tự nhiên Đồng bằng sông Cửu Long
Trích dẫn bài báo
Nguyen, J. L., Nguyen, T. H. A., Trinh, V. D. K., Bui, M. H. B., Huynh, T. V., Nguyen, T. H. T., Le, H. M., & Tran, A. T. (2025). Classification of banana stages using microwave spectroscopy by machine learning. Tạp chí Khoa học Đại học Đồng Tháp, 14(04S), 174-188. https://doi.org/10.52714/dthu.14.04S.2025.1577
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Classification of banana stages using microwave spectroscopy by machine learning
Nội dung chính của bài viết
Tóm tắt
In the food industry, the need to assess and predict the ripening process of fruits plays an important role in optimizing storage and transportation strategies, ensuring the product quality when reaching consumers. The study proposes a method using microwave spectrum based on vector network analyzer combined with machine learning models. This method is used to evaluate among numerous machine learning models and predict the number of days required for an unripe banana to turn to semi ripe and then ripe banana. Data is collected through scattering parameters, including reflection parameter S11 and transmission parameter S21, in the frequency range from 1 GHz to 5 GHz. The S-parameters are processed, analyzed and extracted characteristic data and fed into the machine learning models to perform the comparison and prediction process.
Từ khóa
Máy phân tích mạng vectơ, Học máy, Dự đoán độ chín
Chi tiết bài viết
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Tài liệu tham khảo
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Bashir, S., Jabeen, A., Makroo, H. A., & Mehraj, F. (2020). Application of Computer Vision System in Fruit Quality Monitoring. In Sensor-Based Quality Assessment Systems for Fruits and Vegetables (pp. 267–290). Apple Academic Press. https://doi.org/10.1201/9781003084174-11
Clerjon, S., & Damez, J. L. (2007). Microwave sensing for meat and fish structure evaluation. Measurement Science and Technology, 18(4), 1038–1045. https://doi.org/10.1088/0957-0233/18/4/011
Ebrahimi, A., Scott, J., & Ghorbani, K. (2020). Microwave reflective biosensor for glucose level detection in aqueous solutions. Sensors and Actuators, A: Physical, 301, 111662. https://doi.org/10.1016/j.sna.2019.111662
Elmiladi, L. K., Hora, K. Y., Aaen, P. H., & Elsherbeni, A. Z. (2024). Wireless Monitoring of S-Parameters Measurement Using a Nano-VNA for Biomedical Applications. 2024 International Applied Computational Electromagnetics Society Symposium, ACES 2024, 1–2.
Ghavami, N., Sotiriou, I., & Kosmas, P. (2019). Experimental Investigation of Microwave Imaging as Means to Assess Fruit Quality. 13th European Conference on Antennas and Propagation, EuCAP 2019, 1–5.
Guo, W., Zhu, X., Liu, H., Yue, R., & Wang, S. (2010). Effects of milk concentration and freshness on microwave dielectric properties. Journal of Food Engineering, 99(3), 344–350. https://doi.org/10.1016/j.jfoodeng.2010.03.015
Kent, M., Knöchel, R., Daschner, F., Schimmer, O., Oehlenschläger, J., Mierke-Klemeyer, S., Kroeger, M., Barr, U. K., Floberg, P., Tejada, M., Huidobro, A., Nunes, L., Martins, A., Batista, I., & Cardoso, C. (2007). Intangible but not intractable: The prediction of fish “quality” variables using dielectric spectroscopy. Measurement Science and Technology, 18(4), 1029–1037. https://doi.org/10.1088/0957-0233/18/4/010
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Meng, Z., Wu, Z., & Gray, J. (2018). Microwave sensor technologies for food evaluation and analysis: Methods, challenges and solutions. Transactions of the Institute of Measurement and Control, 40(12), 3433–3448. https://doi.org/10.1177/0142331217721968
Muvianto, C. M. O., Yuniarto, K., Ariessaputra, S., Al Sasongko, S. M., Darmawan, B., & Syafaruddin, C. (2023). Microwave Non-destructive Technique using a Double-ring Resonator for Classification of Transparent Flesh and Yellow gum Mangosteens. E3S Web of Conferences, 465, 2063. https://doi.org/10.1051/e3sconf/202346502063
Naidu, G., Zuva, T., & Sibanda, E. M. (2023). A Review of Evaluation Metrics in Machine Learning Algorithms. Lecture Notes in Networks and Systems, 724 LNNS, 15–25. https://doi.org/10.1007/978-3-031-35314-7_2
Narendra, V. G., & Hareesh, K. S. (2010). Prospects of Computer Vision Automated Grading and Sorting Systems in Agricultural and Food Products for Quality Evaluation. International Journal of Computer Applications, 1(4), 1–12. https://doi.org/10.5120/111-226
Nelson, S. O. (2006). Agricultural applications of dielectric measurements. IEEE Transactions on Dielectrics and Electrical Insulation, 13(4), 688–702. https://doi.org/10.1109/TDEI.2006.1667726
Pacquit, A., Lau, K. T., McLaughlin, H., Frisby, J., Quilty, B., & Diamond, D. (2006). Development of a volatile amine sensor for the monitoring of fish spoilage. Talanta, 69(2 SPEC. ISS.), 515–520. https://doi.org/10.1016/j.talanta.2005.10.046
Rauf, H. T., Saleem, B. A., Lali, M. I. U., Khan, M. A., Sharif, M., & Bukhari, S. A. C. (2019). A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning. Data in Brief, 26, 104340. https://doi.org/10.1016/j.dib.2019.104340
Redzwan, S., Perez, M. D., Velander, J., & Augustine, R. (2018). Study of maturity fruit assessment using permittivity and microwave reflectivity measurements for quality classification. 2018 IEEE Conference on Antenna Measurements and Applications, CAMA 2018, 1–3. https://doi.org/10.1109/CAMA.2018.8530481
Schimmer, O., Daschner, F., & Knochel, R. (2008). UWB-sensors in food quality management - the way from the concept to market. Proceeedings of The 2008 IEEE International Conference on Ultra-Wideband, ICUWB 2008, 2, 141–144. https://doi.org/10.1109/ICUWB.2008.4653371
Tran, V. L., Doan, T. N. C., Ferrero, F., Huy, T. Le, & Le-Thanh, N. (2023). The Novel Combination of Nano Vector Network Analyzer and Machine Learning for Fruit Identification and Ripeness Grading. Sensors, 23(2), 952. https://doi.org/10.3390/s23020952
Venkatesh, M. S., & Raghavan, G. S. V. (2004). An overview of microwave processing and dielectric properties of agri-food materials. Biosystems Engineering, 88(1), 1–18. https://doi.org/10.1016/j.biosystemseng.2004.01.007
Bashir, S., Jabeen, A., Makroo, H. A., & Mehraj, F. (2020). Application of Computer Vision System in Fruit Quality Monitoring. In Sensor-Based Quality Assessment Systems for Fruits and Vegetables (pp. 267–290). Apple Academic Press. https://doi.org/10.1201/9781003084174-11
Clerjon, S., & Damez, J. L. (2007). Microwave sensing for meat and fish structure evaluation. Measurement Science and Technology, 18(4), 1038–1045. https://doi.org/10.1088/0957-0233/18/4/011
Ebrahimi, A., Scott, J., & Ghorbani, K. (2020). Microwave reflective biosensor for glucose level detection in aqueous solutions. Sensors and Actuators, A: Physical, 301, 111662. https://doi.org/10.1016/j.sna.2019.111662
Elmiladi, L. K., Hora, K. Y., Aaen, P. H., & Elsherbeni, A. Z. (2024). Wireless Monitoring of S-Parameters Measurement Using a Nano-VNA for Biomedical Applications. 2024 International Applied Computational Electromagnetics Society Symposium, ACES 2024, 1–2.
Ghavami, N., Sotiriou, I., & Kosmas, P. (2019). Experimental Investigation of Microwave Imaging as Means to Assess Fruit Quality. 13th European Conference on Antennas and Propagation, EuCAP 2019, 1–5.
Guo, W., Zhu, X., Liu, H., Yue, R., & Wang, S. (2010). Effects of milk concentration and freshness on microwave dielectric properties. Journal of Food Engineering, 99(3), 344–350. https://doi.org/10.1016/j.jfoodeng.2010.03.015
Kent, M., Knöchel, R., Daschner, F., Schimmer, O., Oehlenschläger, J., Mierke-Klemeyer, S., Kroeger, M., Barr, U. K., Floberg, P., Tejada, M., Huidobro, A., Nunes, L., Martins, A., Batista, I., & Cardoso, C. (2007). Intangible but not intractable: The prediction of fish “quality” variables using dielectric spectroscopy. Measurement Science and Technology, 18(4), 1029–1037. https://doi.org/10.1088/0957-0233/18/4/010
Leekul, P., Chivapreecha, S., & Krairiksh, M. (2016). Microwave sensor for tangerine classification based on coupled-patch antennas. International Journal of Electronics, 103(8), 1287–1300. https://doi.org/10.1080/00207217.2015.1092602
Meng, Z., Wu, Z., & Gray, J. (2018). Microwave sensor technologies for food evaluation and analysis: Methods, challenges and solutions. Transactions of the Institute of Measurement and Control, 40(12), 3433–3448. https://doi.org/10.1177/0142331217721968
Muvianto, C. M. O., Yuniarto, K., Ariessaputra, S., Al Sasongko, S. M., Darmawan, B., & Syafaruddin, C. (2023). Microwave Non-destructive Technique using a Double-ring Resonator for Classification of Transparent Flesh and Yellow gum Mangosteens. E3S Web of Conferences, 465, 2063. https://doi.org/10.1051/e3sconf/202346502063
Naidu, G., Zuva, T., & Sibanda, E. M. (2023). A Review of Evaluation Metrics in Machine Learning Algorithms. Lecture Notes in Networks and Systems, 724 LNNS, 15–25. https://doi.org/10.1007/978-3-031-35314-7_2
Narendra, V. G., & Hareesh, K. S. (2010). Prospects of Computer Vision Automated Grading and Sorting Systems in Agricultural and Food Products for Quality Evaluation. International Journal of Computer Applications, 1(4), 1–12. https://doi.org/10.5120/111-226
Nelson, S. O. (2006). Agricultural applications of dielectric measurements. IEEE Transactions on Dielectrics and Electrical Insulation, 13(4), 688–702. https://doi.org/10.1109/TDEI.2006.1667726
Pacquit, A., Lau, K. T., McLaughlin, H., Frisby, J., Quilty, B., & Diamond, D. (2006). Development of a volatile amine sensor for the monitoring of fish spoilage. Talanta, 69(2 SPEC. ISS.), 515–520. https://doi.org/10.1016/j.talanta.2005.10.046
Rauf, H. T., Saleem, B. A., Lali, M. I. U., Khan, M. A., Sharif, M., & Bukhari, S. A. C. (2019). A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning. Data in Brief, 26, 104340. https://doi.org/10.1016/j.dib.2019.104340
Redzwan, S., Perez, M. D., Velander, J., & Augustine, R. (2018). Study of maturity fruit assessment using permittivity and microwave reflectivity measurements for quality classification. 2018 IEEE Conference on Antenna Measurements and Applications, CAMA 2018, 1–3. https://doi.org/10.1109/CAMA.2018.8530481
Schimmer, O., Daschner, F., & Knochel, R. (2008). UWB-sensors in food quality management - the way from the concept to market. Proceeedings of The 2008 IEEE International Conference on Ultra-Wideband, ICUWB 2008, 2, 141–144. https://doi.org/10.1109/ICUWB.2008.4653371
Tran, V. L., Doan, T. N. C., Ferrero, F., Huy, T. Le, & Le-Thanh, N. (2023). The Novel Combination of Nano Vector Network Analyzer and Machine Learning for Fruit Identification and Ripeness Grading. Sensors, 23(2), 952. https://doi.org/10.3390/s23020952
Venkatesh, M. S., & Raghavan, G. S. V. (2004). An overview of microwave processing and dielectric properties of agri-food materials. Biosystems Engineering, 88(1), 1–18. https://doi.org/10.1016/j.biosystemseng.2004.01.007