Prediction of mangoes’ sweetness based on spectral data acquired from a low-cost multispectral sensor
Abstract
Recent studies have shown that low-cost multispectral sensors have attracted much interest in developing agricultural applications. This study evaluated the potential of using a low-cost multispectral sensor to predict the sweetness of mango fruit with high export values. A few spectral data preprocessing and wavelength selection algorithms were applied to develop an accurate prediction model. Experimental results showed that unprocessed spectral data of fourteen wavelengths selected by the “regression coefficients” algorithm were suitable for developing a partial least square regression model with a correlation coefficient of 0.703 and a root mean square error of 1.439 °Brix. These results were comparable to recent studies using the same multispectral sensor, confirming the potential use of low-cost multispectral sensors in developing applications and portable devices for fruit quality assessment.
Tóm tắt
Nhiều nghiên cứu gần đây cho thấy cảm biến đa phổ giá thành thấp được quan tâm nhiều trong việc phát triển các ứng dụng trong nông nghiệp. Nghiên cứu này đánh giá tiềm năng sử dụng cảm biến đa phổ giá thành thấp trong việc dự đoán độ ngọt của xoài, loại trái cây có giá trị xuất khẩu cao. Để phát triển được mô hình dự đoán chính xác, một số giải thuật tiền xử lý và lựa chọn bước sóng đã được áp dụng. Kết quả cho thấy dữ liệu phổ không qua tiền xử lý trích xuất từ mười bốn bước sóng được chọn bởi giải thuật “hệ số hồi quy” là phù hợp để xây dựng mô hình hồi quy bình phương tối thiểu từng phần có hệ số tương quan bằng 0,703 và sai số RMSE là 1,439 °Brix. Kết quả này có thể so sánh được với các nghiên cứu gần đây sử dụng cùng loại cảm biến đa phổ vì thế khẳng định tiềm năng sử dụng cảm biến đa phổ giá thành thấp trong việc phát triển ứng dụng và thiết bị cầm tay để đánh giá chất lượng trái cây.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Alós, E., Rodrigo, M. J., & Zacarias, L. (2019). Ripening and Senescence. In Postharvest Physiology and Biochemistry of Fruits and Vegetables (pp. 131–155). Elsevier. https://doi.org/10.1016/B978-0-12-813278-4.00007-5
Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra. Applied Spectroscopy, 43(5), 772–777. https://doi.org/10.1366/0003702894202201
Cayuela, J. A., & García, J. F. (2017). Sorting olive oil based on alpha-tocopherol and total tocopherol content using near-infra-red spectroscopy (NIRS) analysis. Journal of Food Engineering, 202, 79–88. https://doi.org/10.1016/j.jfoodeng.2017.01.015
Engel, J., Gerretzen, J., Szymańska, E., Jansen, J. J., Downey, G., Blanchet, L., & Buydens, L. M. C. (2013). Breaking with trends in pre-processing? TrAC Trends in Analytical Chemistry, 50, 96–106.
https://doi.org/10.1016/j.trac.2013.04.015
Flynn, K. C., Baath, G., Lee, T. O., Gowda, P., & Northup, B. (2023). Hyperspectral reflectance and machine learning to monitor legume biomass and nitrogen accumulation. Computers and Electronics in Agriculture, 211, 107991. https://doi.org/10.1016/j.compag.2023.107991
Gill, P. P. S., Jawandha, S. K., & Kaur, N. (2017). Transitions in mesocarp colour of mango fruits kept under variable temperatures. Journal of Food Science and Technology, 54(13), 4251–4256. https://doi.org/10.1007/s13197-017-2894-z
Golic, M., Walsh, K., & Lawson, P. (2003). Short-Wavelength Near-Infrared Spectra of Sucrose, Glucose, and Fructose with Respect to Sugar Concentration and Temperature. Applied Spectroscopy, 57(2), 139–145. https://doi.org/10.1366/000370203321535033
Huang, Y., Lu, R., & Chen, K. (2018). Assessment of tomato soluble solids content and pH by spatially-resolved and conventional Vis/NIR spectroscopy. Journal of Food Engineering, 236(May), 19–28. https://doi.org/10.1016/j.jfoodeng.2018.05.008
Li, H., Liang, Y., Xu, Q., & Cao, D. (2009). Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta, 648(1), 77–84. https://doi.org/10.1016/j.aca.2009.06.046
Li, X., Wang, Y., Basu, S., Kumbier, K., & Yu, B. (2019). A debiased MDI feature importance measure for random forests. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc.
Lu, R., Van Beers, R., Saeys, W., Li, C., & Cen, H. (2020). Measurement of optical properties of fruits and vegetables: A review. Postharvest Biology and Technology, 159, 111003. https://doi.org/10.1016/j.postharvbio.2019.111003
Maldonado-Celis, M. E., Yahia, E. M., Bedoya, R., Landázuri, P., Loango, N., Aguillón, J., Restrepo, B., & Guerrero Ospina, J. C. (2019). Chemical Composition of Mango (Mangifera indica L.) Fruit: Nutritional and Phytochemical Compounds. Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.01073
Malvandi, A., Kapoor, R., Feng, H., & Kamruzzaman, M. (2022). Non-destructive measurement and real-time monitoring of apple hardness during ultrasonic contact drying via portable NIR spectroscopy and machine learning. Infrared Physics & Technology, 122(February), 104077. https://doi.org/10.1016/j.infrared.2022.104077
Mishra, P., Roger, J. M., Rutledge, D. N., & Woltering, E. (2020). SPORT pre-processing can improve near-infrared quality prediction models for fresh fruits and agro-materials. Postharvest Biology and Technology, 168, 111271. https://doi.org/10.1016/j.postharvbio.2020.111271
Mohammed, M., Srinivasagan, R., Alzahrani, A., & Alqahtani, N. K. (2023). Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres. Sustainability (Switzerland), 15(17).
https://doi.org/10.3390/su151712871
Nghiệm, N. C., Lộc, N. P., Dũng, N. H. & Ngôn, N. C. (2021). Tổng quan về đánh giá chất lượng trái cây bằng phương pháp không phá hủy. Tạp chí Khoa học và Công nghệ Đại học Thái Nguyên, 226(11), 158–167. https://doi.org/10.34238/tnu-jst.4673
Nguyen, C.-N., Phan, Q.-T., Tran, N.-T., Fukuzawa, M., Nguyen, P.-L., & Nguyen, C.-N. (2020). Precise Sweetness Grading of Mangoes (Mangifera indica L.) Based on Random Forest Technique with Low-Cost Multispectral Sensors. IEEE Access, 8, 212371–212382. https://doi.org/10.1109/ACCESS.2020.3040062
Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., & Lammertyn, J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46(2), 99–118. https://doi.org/10.1016/j.postharvbio.2007.06.024
Noguera, M., Millan, B., & Andújar, J. M. (2022). New, Low-Cost, Hand-Held Multispectral Device for In-Field Fruit-Ripening Assessment. Agriculture, 13(1), 4. https://doi.org/10.3390/agriculture13010004
Nordey, T., Joas, J., Davrieux, F., Chillet, M., & Léchaudel, M. (2017). Robust NIRS models for non-destructive prediction of mango internal quality. Scientia Horticulturae, 216, 51–57. https://doi.org/10.1016/j.scienta.2016.12.023
Omar, A. F., Atan, H., & MatJafri, M. Z. (2012a). NIR Spectroscopic Properties of Aqueous Acids Solutions. Molecules, 17(6), 7440–7450. https://doi.org/10.3390/molecules17067440
Omar, A. F., Atan, H., & MatJafri, M. Z. (2012b). Peak Response Identification through Near-Infrared Spectroscopy Analysis on Aqueous Sucrose, Glucose, and Fructose Solution. Spectroscopy Letters, 45(3), 190–201. https://doi.org/10.1080/00387010.2011.604065
Posom, J., Klaprachan, J., Rattanasopa, K., Sirisomboon, P., Saengprachatanarug, K., & Wongpichet, S. (2020). Predicting Marian Plum Fruit Quality without Environmental Condition Impact by Handheld Visible–Near-Infrared Spectroscopy. ACS Omega, 5(43), 27909–27921. https://doi.org/10.1021/acsomega.0c03203
Rogers, M., Blanc-Talon, J., Urschler, M., & Delmas, P. (2023). Wavelength and texture feature selection for hyperspectral imaging: a systematic literature review. Journal of Food Measurement and Characterization, 17(6), 6039–6064.
https://doi.org/10.1007/s11694-023-02044-x
Rungpichayapichet, P., Mahayothee, B., Khuwijitjaru, P., Nagle, M., & Müller, J. (2015). Non-destructive determination of β-carotene content in mango by near-infrared spectroscopy compared with colorimetric measurements. Journal of Food Composition and Analysis, 38, 32–41.
https://doi.org/10.1016/j.jfca.2014.10.013
Luka, S. B., Mohammed Yunusa, B., Msurshima Vihikwagh, Q., Fanan Kuhwa, K., Hannah Oluwasegun, T., Ogalagu, R., Kenneth Yuguda, T., & Adnouni, M. (2024). Hyperspectral imaging systems for rapid assessment of moisture and chromaticity of foods undergoing drying: Principles, applications, challenges, and future trends. Computers and Electronics in Agriculture, 224(June), 109101. https://doi.org/10.1016/j.compag.2024.109101
Srinivasagan, R., Mohammed, M., & Alzahrani, A. (2023). TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits. Sensors, 23(16), 7081.
https://doi.org/10.3390/s23167081
Tran, N.-T., & Fukuzawa, M. (2020). A Portable Spectrometric System for Quantitative Prediction of the Soluble Solids Content of Apples with a Pre-calibrated Multispectral Sensor Chipset. Sensors, 20(20), 5883. https://doi.org/10.3390/s20205883
Tran, N.-T., Phan, Q.-T., Nguyen, C.-N., & Fukuzawa, M. (2021). Machine Learning-Based Classification of Apple Sweetness with Multispectral Sensor. 2021 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter), 23–27. https://doi.org/10.1109/SNPDWinter52325.2021.00014
Walsh, K. B., Blasco, J., Zude-Sasse, M., & Sun, X. (2020). Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use. Postharvest Biology and Technology, 168, 111246. https://doi.org/10.1016/j.postharvbio.2020.111246
Zhang, X., & Yang, J. (2024). Advanced chemometrics toward robust spectral analysis for fruit quality evaluation. Trends in Food Science & Technology, 150, 104612. https://doi.org/10.1016/j.tifs.2024.104612
Zhao, X., Peng, Y., Li, Y., Wang, Y., Li, Y., & Chen, Y. (2023). Intelligent micro flight sensing system for detecting the internal and external quality of apples on the tree. Computers and Electronics in Agriculture, 204(17), 107571. https://doi.org/10.1016/j.compag.2022.107571