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Research & Reviews : Journal of Food Science & Technology Original Research

AI-Enabled Linear Regression Model for Spectroscopic Milk Adulteration Analysis

  1. Mrs.Vrishali M Patil Department of EXTC Engineering RAIT, D. Y. Patil Deemed to be University
  2. Dr. Savita Bhosale Department of Electroniocs Engineering,R.A.I.T, D. Y. Patil Deemed to be University

Abstract

Milk adulteration poses a serious threat to public health and quality assurance in the dairy industry. This requiring rapid, reliable, and non-destructive detection techniques. This study presents a linear regression-based analytical model for identifying and quantifying milk adulteration using spectroscopic data. Spectral measurements of milk samples, including both pure and adulterated variants were acquired using spectroscopic techniques at relevant wavelengths.Blending of other components in pure milk , is specifically called milk adulteration which is dangerous in high amounts to milk quality and consumer protection. The current study is presenting the linear regression-based detection of water adulteration in milk with Near-Infrared (NIR) spectroscopy. We tested a sample of 100-200 milk samples with different quantities of water or other additives added, where spectral absorbance patterns were used as predictors. Linear regression models were built to establish the relationship of the NIR spectral properties with proportions of adulteration. The results showed high predictive accuracy, measuring even small quantities of water and other common mixtures in milk precisely. The effective method explains the potential of regression-based statistical modeling as an efficient, cost-effective and practical solution to quality control in the Artificial Intelligence (AI) methods to improve the precision, accuracy and speed of adulteration detection.

Keywords

References (27)

  1. Nagraik R, Sharma A, Kumar D, Chawla P, Kumar AP. Milk adulterant detection: Conventional and biosensor based approaches: A review. Sensing and Bio-Sensing Research. 2021;33:100433. doi:10.1016/j.sbsr.2021.100433
  2. Hosseini E, Ghasemi JB, Daraei B, Asadi G, Adib N. Application of genetic algorithm and multivariate methods for the detection and measurement of milk‐surfactant adulteration by attenuated total reflection and near‐infrared spectroscopy. Journal of the Science of Food and Agriculture. 2020;101(7):2696-2703. doi:10.1002/jsfa.10894
  3. Handford CE, Campbell K, Elliott CT. Impacts of Milk Fraud on Food Safety and Nutrition with Special Emphasis on Developing Countries. Comprehensive Reviews in Food Science and Food Safety. 2015;15(1):130-142. doi:10.1111/1541-4337.12181
  4. Amar Nasir, Muhammad Umer Iqbal, Habib Ullah, Muhammad Kashif, Muhammad Zahid Farooq, Sikandar A, et al. Impact of Adulteration on Physico-chemical Characteristics of Marketed Raw Milk. Pak-Euro Journal of Medical and Life Sciences. 2022;5(2):511-518. doi:10.31580/pjmls.v5i2.2608
  5. Mhapsekar RU, Abraham L, O'Shea N, Davy S. Edge-AI Implementation for Milk Adulteration Detection. 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT). 2022:108-113. doi:10.1109/gcaiot57150.2022.10019173
  6. Farah JS, Cavalcanti RN, Guimarães JT, Balthazar CF, Coimbra PT, Pimentel TC, et al. Differential scanning calorimetry coupled with machine learning technique: An effective approach to determine the milk authenticity. Food Control. 2021;121:107585. doi:10.1016/j.foodcont.2020.107585
  7. Botelho BG, Reis N, Oliveira LS, Sena MM. Development and analytical validation of a screening method for simultaneous detection of five adulterants in raw milk using mid-infrared spectroscopy and PLS-DA. Food Chemistry. 2015;181:31-37. doi:10.1016/j.foodchem.2015.02.077
  8. Pal JB, Mistry A, Bandyopadhyay D, Biswas B, Bhattacharya S. Adulteration of Milk Identification Using Ionic Polymer Metal Composite as Sensor. IEEE Sensors Letters. 2023;7(3):1-3. doi:10.1109/lsens.2023.3247426
  9. Zhang W, Kasun LC, Wang QJ, Zheng Y, Lin Z. A Review of Machine Learning for Near-Infrared Spectroscopy. Sensors. 2022;22(24):9764. doi:10.3390/s22249764
  10. Galindo CJ, Levy G, Argov-Argaman N, Roth Z, Feldman Y. Bovine milk microwave dielectric response to pregnancy. 2021 13th International Conference on Electromagnetic Wave Interaction with Water and Moist Substances (ISEMA). 2021:1-4. doi:10.1109/isema49699.2021.9508329
  11. Khenwar M, Vishnoi S, Sisodia A. An Assessment of Milk Adulteration IoT Based Model to Identify the Quality of Milk using Lab View. 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART). 2022:868-873. doi:10.1109/smart55829.2022.10047364
  12. Dave A, Banwari D, Srivastava S, Sadistap S. Optical sensing system for detecting water adulteration in milk. 2016 IEEE Global Humanitarian Technology Conference (GHTC). 2016:634-639. doi:10.1109/ghtc.2016.7857345
  13. Wadalkar NM, Mudhalwadkar RP, Sulkekar AA. Development of Electrical Impedance Sensor System for Milk Adulteration (A1 and A2). 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). 2019:107-109. doi:10.1109/icoei.2019.8862733
  14. Ghayoumi M. Generative Adversarial Networks in Practice. 2023. doi:10.1201/9781003281344
  15. Kunes R, Bartos P, Iwasaka GK, Lang A, Hankovec T, Smutny L, et al. In-Line Technologies for the Analysis of Important Milk Parameters during the Milking Process: A Review. Agriculture. 2021;11(3):239. doi:10.3390/agriculture11030239
  16. Dave A, Banwari D, Mansinghani S, Srivastava S, Sadistap S. Ultrasonic sensing system for detecting water adulteration in milk. 2016 IEEE Region 10 Conference (TENCON). 2016:2228-2231. doi:10.1109/tencon.2016.7848424
  17. Natarajan S, Ponusamy V. A Review on Quantitative Adulteration Detection in Milk. 2021 Smart Technologies, Communication and Robotics (STCR). 2021:1-4. doi:10.1109/stcr51658.2021.9588800
  18. Alagumeenaakshi M, Ajitha S, Sathika J, Navaneethakrishnan R. Milk Adulteration Monitoring. 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). 2021:1-5. doi:10.1109/icaeca52838.2021.9675630
  19. Tapadar A, Adhikary A. High Sensitivity Milk Adulteration Detector Using Fractional Order Colpitts Oscillator. 2023 IEEE Conference on AgriFood Electronics (CAFE). 2023:85-88. doi:10.1109/cafe58535.2023.10292088
  20. Patari S, Mahapatra PS. A point of care sensor for milk adulteration detection. 2021 IEEE Sensors. 2021:1-4. doi:10.1109/sensors47087.2021.9639690
  21. Ghodinde KA, Chaskar UM. Quantification of Urea Adulteration with Impedance Spectroscopy in Cow Milk. 2021 6th International Conference for Convergence in Technology (I2CT). 2021:1-5. doi:10.1109/i2ct51068.2021.9418154
  22. Chakraborty M, Biswas K. Limit of Detection for Five Common Adulterants in Milk: A Study With Different Fat Percent. IEEE Sensors Journal. 2018;18(6):2395-2403. doi:10.1109/jsen.2018.2794764
  23. Pugazhenthi K, Sengamalam A, Ganesan B. Milk Quality Monitoring System using IoT. 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS). 2023:1096-1098. doi:10.1109/icscss57650.2023.10169262
  24. Salmaz U, Islam T. A Dynamic Milk Quality Capacitive Sensing System for Detection of Detergent. 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT). 2024. doi:10.1109/csnt60213.2024.10546082
  25. Bose A, Tapadar A, Kumar M, Adhikary A. Detection of Urea in Adulterated Milk With 3% Fat Using Disposable Paper Sensor. IEEE Sensors Letters. 2024;8(8):1-4. doi:10.1109/lsens.2024.3417283
  26. Deshpande A, Deshpande S, Dhande S. NIR Spectroscopy Based Milk Classification and Purity Prediction. 2021 IEEE Pune Section International Conference (PuneCon). 2021:1-5. doi:10.1109/punecon52575.2021.9686473
  27. Chakraborty M, Biswas K. Milk Tester: Simultaneous Detection of Fat Content and Adulteration. IEEE Transactions on Instrumentation and Measurement. 2020;69(5):2468-2476. doi:10.1109/tim.2019.2955796
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