Development of a Portable Low-Cost Multispectral Sensor Integrated with IoT and Machine Learning for Classifying Honey Types

Riki Muhammad; Isroni; Tri Pujian wisesa; Tri Siswandi  Syahputra

doi:10.23960/jemit.343

Authors

Riki Muhammad Department of Physics, Faculty of Science, Sumatera Institute of Technology, South Lampung, 32365
Isroni Department of Physics, Faculty of Science, Sumatera Institute of Technology, South Lampung, 32365
Tri Pujian wisesa Department of Physics, Faculty of Science, Sumatera Institute of Technology, South Lampung, 32365
Tri Siswandi Syahputra Department of Physics, Faculty of Science, Sumatera Institute of Technology, South Lampung, 32365

DOI:

https://doi.org/10.23960/jemit.343

Keywords:

AS7265X Sensor, Honey classification, Internet of Things, Machine Learning, Spectroscopy.

Abstract

Accurate honey type authentication is a significant challenge for small-scale producers, as conventional methods are often costly and impractical. This study aims to design and develop a low-cost honey classification prototype by integrating the AS7265X multispectral sensor with Internet of Things (IoT) technology and machine learning. Spectral data from 18 channels of various Indonesian honey types were acquired using the AS7265X sensor and analyzed exploratively using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The data were then normalized and used to train Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) classification models. An ESP32-based IoT system was developed for real-time monitoring and cloud data storage. The results demonstrate that AS7265X spectral data effectively differentiate honey types, with the ANN model achieving 94.05% accuracy, supported by a responsive IoT system (1–2 seconds) for monitoring and centralized storage. This prototype shows potential as a practical, rapid, accurate, and efficient honey authentication solution for various stakeholders.

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References

Aira, J., Olivares, T., & Delicado, F. M. (2022). SpectroGLY: A low-cost IoT-based ecosystem for the detection of glyphosate residues in waters. IEEE Transactions on Instrumentation and Measurement, 71, 1-10. https://doi.org/10.1109/TIM.2022.3196947

Al-Awadhi, M., & Deshmukh, R. (2023). Enhancing honey adulteration detection with optimal subspace wavelength reduction in Vis-NIR reflection spectroscopy. IEEE Access, 11, 144226-144243. https://doi.org/10.1109/ACCESS.2023.3343731

Biswas, A., & Chaudhari, S. R. (2024). Exploring the role of NIR spectroscopy in quantifying and verifying honey authenticity: A review. Food Chemistry, 445, 138712. https://doi.org/10.1016/j.foodchem.2024.138712

Chotimah, Saifullah, K., Laily, F. N., Puspita, M., Kombo, K. O., Hidayat, S. N., Sulistyani, E. T., Wahyono, & Triyana, K. (2024). Electronic nose-based monitoring of vacuum-packaged chicken meat freshness in room and refrigerated storage. Journal of Food Measurement and Characterization, 18(10), 8825-8842. https://doi.org/10.1007/s11694-024-02847-6

Da Silva, P. M., Gauche, C., Gonzaga, L. V., Costa, A. C. O., & Fett, R. (2016). Honey: Chemical composition, stability and authenticity. Food Chemistry, 196, 309-323. https://doi.org/10.1016/j.foodchem.2015.09.051

Durgun, Y. (2023). Classification of starch adulteration in milk using spectroscopic data and machine learning. Uluslararasi Muhendislik Arastirma ve Gelistirme Dergisi. https://doi.org/10.29137/umagd.1379171

Faal, S., Loghavi, M., & Kamgar, S. (2019). Physicochemical properties of Iranian ziziphus honey and emerging approach for predicting them using electronic nose. Measurement, 148, 106936. https://doi.org/10.1016/j.measurement.2019.106936

Goncalves, W. B., Teixeira, W. S. R., Cervantes, E. P., Mioni, M. de S. R., Sampaio, A. N. da C. E., Martins, O. A., Gruber, J., & Pereira, J. G. (2023). Application of an electronic nose as a new technology for rapid detection of adulteration in honey. Applied Sciences, 13(8), 4881. https://doi.org/10.3390/app13084881

Habibullah, M., Mohebian, M. R., Soolanayakanahally, R., & Bahar, A. N. (2020). Low-cost multispectral sensor array for determining leaf nitrogen status. Nitrogen, 1(1), Article 1. https://doi.org/10.3390/nitrogen1010007

Ihsan, N., Kombo, K. O., Kusuma, F. J., Syahputra, T. S., Puspita, M., Wahyono, & Triyana, K. (2025). Enhancing electronic nose performance for differentiating civet and non-civet roasted bean coffee using polynomial feature extraction methods. Flavour and Fragrance Journal, 40(2), 298-307. https://doi.org/10.1002/ffj.3826

Labsvards, K. D., Borisova, A., Kokina, K., Bertins, M., Viksna, A., & Rudovica, V. (2023). Multi-element profile characterization in monofloral honey. Proceedings, 92(1), Article 1. https://doi.org/10.3390/proceedings2023092031

Leon-Medina, J. X., Acosta-Opayome, D., Fuenmayor, C. A., Zuluaga-Dominguez, C. M., Anaya, M., & Tibaduiza, D. A. (2023). Intelligent electronic tongue system for the classification of genuine and false honeys. International Journal of Food Properties, 26(1), 327-343. https://doi.org/10.1080/10942912.2022.2161571

Mateo, F., Tarazona, A., & Mateo, E. M. (2021). Comparative study of several machine learning algorithms for classification of unifloral honeys. Foods, 10(7), Article 7. https://doi.org/10.3390/foods10071543

Mohamat, R. N., Noor, N. R. A. M., Yusof, Y. A., Sabri, S., & Zawawi, N. (2023). Differentiation of high-fructose corn syrup adulterated kelulut honey using physicochemical, rheological, and antibacterial parameters. Foods, 12(8), 1670. https://doi.org/10.3390/foods12081670

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

Noguera, M., Millan, B., Aquino, A., & Andujar, J. M. (2022). Methodology for olive fruit quality assessment by means of a low-cost multispectral device. Agronomy, 12(5), Article 5. https://doi.org/10.3390/agronomy12050979

Riswahyuli, Y., Rohman, A., Setyabudi, F. M. C. S., & Raharjo, S. (2020). Characterization of Indonesia wild honey and its potential for authentication and origin distinction. Food Research, 4(5), 1670-1680. https://doi.org/10.26656/fr.2017.4(5).105

Sagita, D., Mardjan, S. S., Suparlan, Purwandoko, P. B., & Widodo, S. (2024). Low-cost IoT-based multichannel spectral acquisition systems for roasted coffee beans evaluation: Case study of roasting degree classification using machine learning. Journal of Food Composition and Analysis, 133, 106478. https://doi.org/10.1016/j.jfca.2024.106478

Sagita, D., Widodo, S., Mardjan, S. S., Purwandoko, P. B., & Suparlan. (2025). Rapid identification of coffee species and origin using affordable multi-channel spectral sensor combined with machine learning. Food Research International, 211, 116501. https://doi.org/10.1016/j.foodres.2025.116501

Suhesti, E., Roni, Y., Yanti, R. N., Ningsih, A. T., & Hadinoto. (2023). Kualitas dan preferensi konsumen terhadap madu lebah Apis mellifera L. dan Apis dorsata F. Jurnal Penelitian Hasil Hutan, 41(2), Article 2. https://doi.org/10.55981/jphh.2023.766

Syahputra, T. S., Ihsan, N., Kombo, K. O., Faizah, K., Wahyono, Widada, J., & Triyana, K. (2025). Integration of low-cost multispectral sensors and electronic nose for enhanced fermentation monitoring in tempeh production. Journal of Food Measurement and Characterization, 19(5), 3687-3701. https://doi.org/10.1007/s11694-025-03217-6

Tsagkaris, A. S., Koulis, G. A., Danezis, G. P., & Martakos, I. (2021). Honey authenticity: Analytical techniques, state of the art and challenges. RSC Advances, 11(19), 11273-11294. https://doi.org/10.1039/D1RA00069A

Yang, B., Huang, X., Yan, X., Zhu, X., & Guo, W. (2020). A cost-effective on-site milk analyzer based on multispectral sensor. Computers and Electronics in Agriculture, 179, 105823. https://doi.org/10.1016/j.compag.2020.105823

Zhang, G., & Abdulla, W. (2022). On honey authentication and adulterant detection techniques. Food Control, 138, 108992. https://doi.org/10.1016/j.foodcont.2022.108992