Detection and Classification of Urea Adulteration in Milk with Deep Neural Networks

Authors

  • Ketaki Ghodinde COEP Technological University, India
  • Uttam Chaskar COEP Technological University, India
Volume: 14 | Issue: 3 | Pages: 14319-14326 | June 2024 | https://doi.org/10.48084/etasr.7091

Abstract

Milk is a major food constituent. However, the existing discrepancy between milk demand and supply leads to adulteration, which can be dangerous since it causes detrimental effects on health implicating lethal diseases. Although classical methods for adulteration detection are very accurate, their implementation requires skilled technicians as well as expensive and sophisticated instruments. These reasons trigger the need for improved techniques in uncovering adulteration. Urea is a natural component in milk and accounts for a substantial share of adulteration in the non-protein content of milk. The current research proposes and employs a sensor system utilizing the Electrical Impedance Spectroscopy (EIS) method to determine the presence of urea. The classification system was developed using different machine learning algorithms. Three classifiers, Extreme Gradient Boosting (XGBoost), Extreme Learning Machines (ELM), and Deep Neural Networks (DNN) were considered for various levels of urea adulteration. Milk samples were assessed by deploying the developed EIS sensor assembly and the results derived were employed in the training of the machine learning algorithms. The estimated classifiers displayed promising outcomes, involving up to 98.33% classification accuracies, outshining frequently used existing learning approaches like logistic regression.

Keywords:

EIS sensor, milk adulteration, deep neural network, urea detection, classification, sensor system development, deep neural networks

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References

A. F. S. Silva and F. R. P. Rocha, "A novel approach to detect milk adulteration based on the determination of protein content by smartphone-based digital image colorimetry," Food Control, vol. 115, Sep. 2020, Art. no. 107299.

T. Azad and S. Ahmed, "Common milk adulteration and their detection techniques," International Journal of Food Contamination, vol. 3, no. 1, Dec. 2016, Art. no. 22.

Y. Yang et al., "Fraud vulnerability in the Dutch milk supply chain: Assessments of farmers, processors and retailers," Food Control, vol. 95, pp. 308–317, Jan. 2019.

J. Qin et al., "Detection and quantification of adulterants in milk powder using a high-throughput Raman chemical imaging technique," Food Additives & Contaminants: Part A, vol. 34, no. 2, pp. 152–161, Feb. 2017.

M. Chakraborty and K. Biswas, "Limit of Detection for Five Common Adulterants in Milk: A Study With Different Fat Percent," IEEE Sensors Journal, vol. 18, no. 6, pp. 2395–2403, Mar. 2018.

R. Nagraik, A. Sharma, D. Kumar, P. Chawla, and A. P. Kumar, "Milk adulterant detection: Conventional and biosensor based approaches: A review," Sensing and Bio-Sensing Research, vol. 33, Aug. 2021, Art. no. 100433.

S. Tripathy, A. R. Ghole, K. Deep, S. R. K. Vanjari, and S. G. Singh, "A comprehensive approach for milk adulteration detection using inherent bio-physical properties as ‘Universal Markers’: Towards a miniaturized adulteration detection platform," Food Chemistry, vol. 217, pp. 756–765, Feb. 2017.

R. Sharma, Y. S. Rajput, and A. K. Barui, Detection of Adulterants in milk: A Laboratory Manual (Revised edition), NDRI, 2012.

K. M. Khan, H. Krishna, S. K. Majumder, and P. K. Gupta, "Detection of Urea Adulteration in Milk Using Near-Infrared Raman Spectroscopy," Food Analytical Methods, vol. 8, no. 1, pp. 93–102, Jan. 2015.

X. Dai et al., "Determination of Urea in Milk by Liquid Chromatography-Isotope Dilution Mass Spectrometry," Analytical Letters, vol. 45, no. 12, pp. 1557–1565, Aug. 2012.

X. Dai et al., "Accurate analysis of urea in milk and milk powder by isotope dilution gas chromatography–mass spectrometry," Journal of Chromatography B, vol. 878, no. 19, pp. 1634–1638, Jun. 2010.

M. Czauderna and J. Kowalczyk, "Easy and accurate determination of urea in milk, blood plasma, urine and selected diets of mammals by high-performance liquid chromatography with photodiode array detection preceded by pre-column derivatization," Chemia Analityczna, vol. 54, no. 5, pp. 919–937, 2009.

S. Sen, Z. Dundar, O. Uncu, and B. Ozen, "Potential of Fourier-transform infrared spectroscopy in adulteration detection and quality assessment in buffalo and goat milks," Microchemical Journal, vol. 166, Jul. 2021, Art. no. 106207.

S. R. Karunathilaka, B. J. Yakes, K. He, L. Bruckner, and M. M. Mossoba, "First use of handheld Raman spectroscopic devices and on-board chemometric analysis for the detection of milk powder adulteration," Food Control, vol. 92, pp. 137–146, Oct. 2018.

T. de Oliveira Mendes, B. L. S. Porto, M. J. V. Bell, I. T. Perrone, and M. A. L. de Oliveira, "Capillary zone electrophoresis for fatty acids with chemometrics for the determination of milk adulteration by whey addition," Food Chemistry, vol. 213, pp. 647–653, Dec. 2016.

Ν. Kamil et al., "Investigating the Quality of Milk using Spectrometry Technique and Scattering Theory," Engineering, Technology & Applied Science Research, vol. 11, no. 3, pp. 7111–7117, Jun. 2021.

D. Worku, M. Sharma, P. Kumar, and B. Koteswararao, "Detection of Adulteration in milk using capacitor sensor with especially focusing on Electrical properties of the milk.," in 7th International Electronic Conference on Sensors and Applications, Nov. 2020, pp. 1–10.

A. M. Lopes, J. A. T. Machado, E. Ramalho, and V. Silva, "Milk Characterization Using Electrical Impedance Spectroscopy and Fractional Models," Food Analytical Methods, vol. 11, no. 3, pp. 901–912, Mar. 2018.

H. Bouzidi, L. Otmani, R. Doufnoune, L. Zerroual, and D. Benachour, "Influence of Membrane Type on Some Electrical Properties of a Single Microbial Fuel Cell," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8492–8499, Jun. 2022.

M. Ghasemi-Varnamkhasti, N. Ghatreh-Samani, M. Naderi-Boldaji, M. Forina, and M. Bonyadian, "Development of two dielectric sensors coupled with computational techniques for detecting milk adulteration," Computers and Electronics in Agriculture, vol. 140, pp. 266–278, Aug. 2017.

K. A. Ghodinde and U. M. Chaskar, "Quantification of Urea Adulteration with Impedance Spectroscopy in Cow Milk," in 6th International Conference for Convergence in Technology, Maharashtra, India, Apr. 2021, pp. 1–5.

H. Chen, C. Tan, Z. Lin, and T. Wu, "Detection of melamine adulteration in milk by near-infrared spectroscopy and one-class partial least squares," Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 173, pp. 832–836, Feb. 2017.

J. K. F. Torres et al., "Technological aspects of lactose-hydrolyzed milk powder," Food Research International, vol. 101, pp. 45–53, Nov. 2017.

S. N. Jha, P. Jaiswal, M. K. Grewal, M. Gupta, and R. Bhardwaj, "Detection of Adulterants and Contaminants in Liquid Foods—A Review," Critical Reviews in Food Science and Nutrition, vol. 56, no. 10, pp. 1662–1684, Jul. 2016.

B. T. Pham, D. Tien Bui, I. Prakash, and M. B. Dholakia, "Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS," CATENA, vol. 149, pp. 52–63, Feb. 2017.

J. S. Farah et al., "Differential scanning calorimetry coupled with machine learning technique: An effective approach to determine the milk authenticity," Food Control, vol. 121, Mar. 2021, Art. no. 107585.

S. Kimbahune, S. M. Ghouse, M. B. S, S. Shinde, and A. K. Jha, "Hyperspectral sensing based analysis for determining milk adulteration," in SPIE Commerical Sensing and Imaging, Baltimore, MD, USA, Apr. 2016, vol. 9860, pp. 44–51.

S. Tripathy, M. S. Reddy, S. R. K. Vanjari, S. Jana, and S. G. Singh, "A Step Towards Miniaturized Milk Adulteration Detection System: Smartphone-Based Accurate pH Sensing Using Electrospun Halochromic Nanofibers," Food Analytical Methods, vol. 12, no. 2, pp. 612–624, Feb. 2019.

H. A. Neto, W. L. F. Tavares, D. C. S. Z. Ribeiro, R. C. O. Alves, L. M. Fonseca, and S. V. A. Campos, "On the utilization of deep and ensemble learning to detect milk adulteration," BioData Mining, vol. 12, no. 1, Jul. 2019, Art. no. 13.

G. Durante, W. Becari, F. A. S. Lima, and H. E. M. Peres, "Electrical Impedance Sensor for Real-Time Detection of Bovine Milk Adulteration," IEEE Sensors Journal, vol. 16, no. 4, pp. 861–865, Oct. 2016.

M. Grossi, C. Parolin, B. Vitali, and B. Ricco, "Electrical Impedance Spectroscopy (EIS) characterization of saline solutions with a low-cost portable measurement system," Engineering Science and Technology, an International Journal, vol. 22, no. 1, pp. 102–108, Feb. 2019.

C. Soares, J. A. Tenreiro Machado, A. M. Lopes, E. Vieira, and C. Delerue-Matos, "Electrochemical impedance spectroscopy characterization of beverages," Food Chemistry, vol. 302, Jan. 2020, Art. no. 125345.

C. Qin, Y. Zhang, F. Bao, C. Zhang, P. Liu, and P. Liu, "XGBoost Optimized by Adaptive Particle Swarm Optimization for Credit Scoring," Mathematical Problems in Engineering, vol. 2021, Mar. 2021, Art. no. e6655510.

A. Kehili, Κ. Dabbabi, and A. Cherif, "Early Detection of Parkinson’s and Alzheimer’s Diseases using the VOT_Mean Feature," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6912–6918, Apr. 2021.

G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, no. 1, pp. 489–501, Dec. 2006.

D. K. Singh and M. Shrivastava, "Evolutionary Algorithm-based Feature Selection for an Intrusion Detection System," Engineering, Technology & Applied Science Research, vol. 11, no. 3, pp. 7130–7134, Jun. 2021.

S. R. Gopi and M. Karthikeyan, "Effectiveness of Crop Recommendation and Yield Prediction using Hybrid Moth Flame Optimization with Machine Learning," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11360–11365, Aug. 2023.

H. Badem, A. Basturk, A. Caliskan, and M. E. Yuksel, "A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited–memory BFGS optimization algorithms," Neurocomputing, vol. 266, pp. 506–526, Nov. 2017.

N. K. Al-Shammari et al., "Cardiac Stroke Prediction Framework using Hybrid Optimization Algorithm under DNN," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7436–7441, Aug. 2021.

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How to Cite

[1]
K. Ghodinde and U. Chaskar, “Detection and Classification of Urea Adulteration in Milk with Deep Neural Networks”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14319–14326, Jun. 2024.

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