Real-Time Detection of Milk Adulteration with a Portable Multispectral Analysis Device: A Multispectral Sensor and Optimized Logistic Regression Approach

Year 2024, Volume: 10 Issue: 4, 968 - 980, 31.12.2024

Mahmut Durgun

https://doi.org/10.28979/jarnas.1569065

Abstract

This study presents the development of a portable, low-cost, and edge computing-based system for real-time milk adulteration detection. Utilizing an AS7265x multispectral sensor and Arduino Nano 33 BLE Sense microcontroller, this system employs an optimized logistic regression model to identify starch adulteration in milk samples with near-perfect accuracy. Unlike complex neural network models, the logistic regression model offers simplicity, low power consumption, and efficient operation on microcontrollers. The collected spectral data is processed in real-time, and results are transmitted via Bluetooth for immediate analysis. The system demonstrates high accuracy, portability, and cost-effectiveness, making it suitable for use in various stages of the milk supply chain, including farms, processing facilities, and retail points. Future work will explore the detection of other adulterants and the integration of cloud-based analytics to enhance monitoring capabilities. This study provides an innovative approach to ensuring milk quality and consumer safety.

Keywords

Milk adulteration, portable detection system, multispectral sensor, logistic regression

References

• F. Cimmino, A. Catapano, L. Petrella, I. Villano, R. Tudisco, G. Cavaliere, Role of milk micronutrients in human health, Frontiers in Bioscience-Landmark 28 (2) (2023) 41 16 pages.
• S. Das, B. Goswami, K. Biswas, Milk adulteration and detection: A review, Sensor Letters 14 (1) (2016) 4–18.
• M. Momtaz, S. Y. Bubli, M. S. Khan, Mechanisms and health aspects of food adulteration: A comprehensive review, Foods 12 (1) (2023) 199 25 pages.
• M. Kamal, R. Karoui, Analytical methods coupled with chemometric tools for determining the authenticity and detecting the adulteration of dairy products: A review, Trends in Food Science and Technology 46 (1) (2015) 27–48.
• M. M. Ferreira, L. Marins-Gonçalves, D. De Souza, An integrative review of analytical techniques used in food authentication: A detailed description for milk and dairy products, Food Chemistry 457 (2024) 140206 20 pages.
• O. Boukria, S. Boudalia, Z. F. Bhat, A. Hassoun, A. Aït-Kaddour, Evaluation of the adulteration of camel milk by non-camel milk using multispectral image, fluorescence and infrared spectroscopy, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 300 (2023) 122932 10 pages.
• S. Patari, P. Datta, P. S. Mahapatra, 3D Paper-based milk adulteration detection device, Scientific Reports 12 (1) (2022) 13657 14 pages.
• R. Nagraik, A. Sharma, D. Kumar, P. Chawla, A. P. Kumar, Milk adulterant detection: Conventional and biosensor based approaches: A review, Sensing and Bio-Sensing Research 33 (2021) 100433 9 pages.
• A. Poonia, A. Jha, R. Sharma, H. B. Singh, A. K. Rai, N. Sharma, Detection of adulteration in milk: A review, International Journal of Dairy Technology 70 (1) (2017) 23–42.
• T. Azad, S. Ahmed, Common milk adulteration and their detection techniques, International Journal of Food Contamination 3 (1) (2016) 1–9.
• A. Ravindran, F. P. Nesamani, D. Nirmal, A Study on the use of spectroscopic techniques to identify food adulteration, International Conference on Circuits and Systems in Digital Enterprise Technology (2018) 1–6.
• P. M. Santos, E. R. Pereira-Filho, L. E. Rodriguez-Saona, Rapid detection and quantification of milk adulteration using infrared microspectroscopy and chemometrics analysis, Food Chemistry 138 (1) (2013) 19–24.
• C. McVey, C. T. Elliott, A. Cannavan, S. D. Kelly, A. Petchkongkaew, S. A. Haughey, Portable spectroscopy for high throughput food authenticity screening: Advancements in technology and integration into digital traceability systems, Trends in Food Science and Technology 118 (2021) 777–790.
• L. Iram, M. Y. Sandhu, A. K. M. Z. Hossain, S. Khan, Portable real time microwave milk quality monitoring sensor, 9th International Conference on Computer and Communication Engineering (2023) 167–172.
• G. Durante, W. Becari, F. A. S. Lima, H. E. M. Peres, Electrical impedance sensor for real-time detection of bovine milk adulteration, IEEE Sensors Journal 16 (4) (2016) 861–865.
• R. Kodan, P. Parmar, S. Pathania, Internet of things for food sector: Status quo and projected potential, Food Reviews International 36 (6) (2020) 584–600.
• A. M. Aware, U. A. Kshirsagar, Design of milk analysis system for dairy farmers using embedded system, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering 5 (5) (2017) 11–16.
• K. Pugazhenthi, A. Sengamalam, B. Ganesan, Milk quality monitoring system using IoT, International Conference on Sustainable Computing and Smart Systems (2023) 1096–1098.
• R. C. Zhang, X. Yu, X. J. Liu, J. H. Zhai, Z. W. Ning, Study on mechanical automation with design of rapid milk detector based on freezing point, Advanced Materials Research 703 (2013) 282–286.
• M. K. Nieuwoudt, S. E. Holroyd, C. M. McGoverin, M. C. Simpson, D. E. Williams, Screening for adulterants in liquid milk using a portable Raman miniature spectrometer with immersion probe, Applied Spectroscopy 71 (2) (2017) 308–312.
• R. U. Mhapsekar, L. Abraham, N. O’Shea, S. Davy, Edge-AI implementation for milk adulteration detection, IEEE Global Conference on Artificial Intelligence and Internet of Things (2022) 108–113.
• A. A. Arrieta-Almario, M. S. Palencia-Luna, P. L. Arrieta-Torres, Determination of adulterant in milk through the use of a portable voltammetric electronic tongue, Revista Mexicana. Ingeniería. Química 17 (2018) 877–884.
• A. S. Sekhon, P. Unger, S. Sharma, B. Singh, X. Chen, G. M Ganjyal, M. Michael, Hyperspectral imaging of foodborne pathogens at colony and cellular levels for rapid identification in dairy products, Food Science and Nutrition 12 (1) (2024) 239–254.