Low-cost Multispectral Acquisition Device Coupled with Machine Learning for Detecting Adulteration of Honey
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Abstract
Honey is a natural sweetener created by honeybees from the nectar of flowers. Honey's extensive health benefits have led to its widespread use across multiple industries. Honey adulteration with inferior substances undermines its quality, reducing natural nutrients and antioxidants, and diminishing its health benefits. This study aimed to study the possibility of detection of honey adulteration with a low-cost multispectral device coupled with machine learning. The adulterated honey came from deliberate adulteration with cane syrup in the 1 to 90% range. Spectral data was collected for pure honey and the adulterated honey samples at the wavelengths of 610, 680, 730, 760, 810, and 860 nm. The detection models for distinguishing pure and adulterated honey were developed by Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), C-Support Vector Machine (C-SVM), and K-Nearest Neighbors (KNN). All models achieved high accuracy between 0.91 and 0.98 and maintained balanced precision and recall metrics. This study serves as a guideline for developing a low-cost portable honey authentication device that is practical for real-world applications.
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References
Aykas, D. P., & Menevseoglu, A. (2021). A rapid method to detect green pea and peanut adulteration in pistachio by using portable FT-MIR and FT-NIR spectroscopy combined with chemometrics. Food Control, 121, Article 107670. https://doi.org/10.1016/j.foodcont.2020.107670
Bodor, Z., Majadi, M., Benedek, C., Zaukuu, J.-L. Z., Veresné Bálint, M., Csajbókné Csobod, É., & Kovacs, Z. (2023). Detection of low-level adulteration of Hungarian honey using near infrared spectroscopy. Chemosensors, 11(2), Article 89. https://doi.org/10.3390/chemosensors11020089
Calle, J. L. P., Punta-Sánchez, I., González-de-Peredo, A. V., Ruiz-Rodríguez, A., Ferreiro-González, M., & Palma, M. (2023). Rapid and automated method for detecting and quantifying adulterations in high-quality honey using vis-NIRs in combination with machine learning. Foods, 12(13), Article 2491. https://doi.org/10.3390/foods12132491
Caredda, M., Ciulu, M., Tilocca, F., Langasco, I., Núñez, O., Sentellas, S., Saurina, J., Pilo, M. I., Spano, N., & Sanna, G. (2024). Portable NIR spectroscopy to simultaneously trace honey botanical and geographical origins and detect syrup adulteration. Foods, 13(19), Article 3062. https://doi.org/10.3390/foods13193062
Cherigui, S., Chikhi, I., Dergal, F., Chaker, H., Belaid, B., Bujagić, I. M., & Muselli, A. (2024). Authentication of honey through chemometric methods based on FTIR spectroscopy and physicochemical parameters. Journal of Food Measurement and Characterization, 18(6), 4653-4664. https://doi.org/10.1007/s11694-024-02521-x
Guelpa, A., Marini, F., du Plessis, A., Slabbert, R., & Manley, M. (2017). Verification of authenticity and fraud detection in South African honey using NIR spectroscopy. Food Control, 73, 1388-1396. https://doi.org/10.1016/j.foodcont.2016.11.002
Kapse, S., Kedia, P., Kausley, S., & Rai, B. (2023). Nondestructive Evaluation of Banana Maturity Using NIR AS7263 Sensor. Journal of Nondestructive Evaluation, 42(2), 30. https://doi.org/10.1007/s10921-023-00943-z
Lapcharoensuk, R., Lunvongsa, T., Suntisakoonwong, P., Sitorus, A., & Boodnon, W. (2024). Low-cost multispectral sensor for detecting adulteration of onion powder with machine learning. In 2024 10th International Conference on Mechatronics and Robotics Engineering (ICMRE). IEEE. https://doi.org/10.1109/ICMRE60776.2024.10532183
Lapcharoensuk, R., & Moul, C. (2024). Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 318, Article 124480. https://doi.org/10.1016/j.saa.2024.124480
Naila, A., Flint, S. H., Sulaiman, A. Z., Ajit, A., & Weeds, Z. (2018). Classical and novel approaches to the analysis of honey and detection of adulterants. Food Control, 90, 152-165. https://doi.org/10.1016/j.foodcont.2018.02.027
Pampuri, A., Tugnolo, A., Giovenzana, V., Casson, A., Guidetti, R., & Beghi, R. (2021). Design of cost-effective LED based prototypes for the evaluation of grape (Vitis vinifera L.) ripeness. Computers and Electronics in Agriculture, 189, Article 106381. https://doi.org/10.1016/j.compag.2021.106381
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., & Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
Pornchaloempong, P., Sharma, S., Phanomsophon, T., Srisawat, K., Inta, W., Sirisomboon, P., Prinyawiwatkul, W., Nakawajana, N., Lapcharoensuk, R., & Teerachaichayut, S. (2022). Non-destructive quality evaluation of tropical fruit (Mango and Mangosteen) Purée using near-infrared spectroscopy combined with partial least squares regression. Agriculture, 12(12), Article 2060. https://doi.org/10.3390/agriculture12122060
Song, X., She, S., Xin, M., Chen, L., Li, Y., Vander Heyden, Y., Rogers, K. M., & Chen, L. (2020). Detection of adulteration in Chinese monofloral honey using 1H nuclear magnetic resonance and chemometrics. Journal of Food Composition and Analysis, 86, Article 103390. https://doi.org/10.1016/j.jfca.2019.103390
Sulistyo, S. B., Sudarmaji, A., Kuncoro, P. H., & Haryanti, P. (2023). Design and performance test of portable spectrometer using AS7265x multispectral sensor for detection of adulterated cane sugar in granulated coconut sugar. AIP Conference Proceedings, 2586, Article 060016. https://doi.org/10.1063/5.0106942
Valinger, D., Longin, L., Grbeš, F., Benković, M., Jurina, T., Kljusurić, J. G., & Tušek, A. J. (2021). Detection of honey adulteration–The potential of UV-VIS and NIR spectroscopy coupled with multivariate analysis. LWT, 145, Article 111316. https://doi.org/10.1016/j.lwt.2021.111316
Wang, M., Luo, D., Yang, Y., Nikitina, M. A., Zhang, X., & Xiao, X. (2022). NIR based wireless sensing approach for fruit monitoring. Results in Engineering, 14, Article 100403. https://doi.org/10.1016/j.rineng.2022.100403
Wang, Y., Zhang, K., Shi, S., Wang, Q., & Liu, S. (2023). Portable protein and fat detector in milk based on multi-spectral sensor and machine learning. Applied Sciences, 13(22), Article 12320. https://doi.org/10.3390/app132212320
Williams, P., Manley, M., & Antoniszyn, J. (2019). Near infrared technology: getting the best out of light. African Sun Media.
Xu, J., Liu, X., Wu, B., & Cao, Y. (2020). A comprehensive analysis of 13C isotope ratios data of authentic honey types produced in China using the EA-IRMS and LC-IRMS. Journal of Food Science and Technology, 57(4), 1216-1232. https://doi.org/10.1007/s13197-019-04153-2
