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Introducing a Rapid and Practical Approach for Determining Fat Content in Cow Milk Using Image Processing | ||
| Biomechanism and Bioenergy Research | ||
| دوره 3، شماره 2 - شماره پیاپی 6، اسفند 2024، صفحه 61-74 اصل مقاله (500.31 K) | ||
| نوع مقاله: Original Research | ||
| شناسه دیجیتال (DOI): 10.22103/bbr.2024.23712.1087 | ||
| نویسندگان | ||
| Lena Beheshti Moghadam؛ Seyed Saeid Mohtasebi* ؛ Behzad Nouri؛ Mahmoud Omid؛ Seyed Morteza Mohtasebi | ||
| Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tehran, Karaj, Iran. | ||
| چکیده | ||
| Milk fat content serves as a crucial indicator of milk quality, holding significance for both producers and consumers. Therefore, the development of a swift and viable method for assessing this parameter could greatly enhance monitoring efforts. This study aimed to establish a correlation between milk fat content and milk color through image analysis techniques. Cow milk samples spanning a fat content range of 0.2% to 3.5% were analyzed under various lighting conditions, employing a fusion of image processing methods with artificial neural networks (ANNs) and particle swarm optimization (PSO) algorithms. Results demonstrated that the most optimal method, determined through comparative analysis against a reference sample, produced accurate estimations of milk fat content. Statistical evaluation revealed a high coefficient of determination (R2=0.99), accompanied by minimal mean absolute error (MAE=0.22) and mean squared error (MSE=0.05). Additionally, a comprehensive examination was conducted into the influence of water content on milk color across different levels of fat concentration. Findings from this investigation provided robust validation for the effectiveness of the proposed method, exhibiting attributes of reliability, efficiency, and cost-effectiveness in the realm of milk fat content assessment. | ||
| کلیدواژهها | ||
| Non-destructive Analysis؛ Artificial neural network؛ Color؛ Milk؛ Quality Assessment؛ Fat Content؛ Cow Milk | ||
| مراجع | ||
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Ali, A. H., Wei, W., Khalifa, S. A., Zhang, X., & Wang, X. (2021). Effect of pasteurisation, homogenisation and freeze‐drying on bovine and buffalo milk fat triacylglycerols profile. International Journal of Dairy Technology, 74(3), 472-488. https://doi.org/10.1111/1471-0307.12781
Ali, F. (2022). Nonthermal and thermal treatments impact the structure and microstructure of milk fat globule membrane. International Journal of Dairy Technology, 75(2), 338-347. https://doi.org/10.1111/1471-0307.12840
Amsaraj, R., Ambade, N. D., & Mutturi, S. (2021). Variable selection coupled to PLS2, ANN and SVM for simultaneous detection of multiple adulterants in milk using spectral data. International Dairy Journal, 123, 105172. https://doi.org/10.1016/j.idairyj.2021.105172
Asmare, M. H., Asirvadam, V. S., & Hani, A. F. M. (2015). Image enhancement based on contourlet transform. Signal, Image and Video Processing, 9, 1679-1690. https://doi.org/10.1007/s11760-014-0626-7
Attallah, B., Serir, A., & Chahir, Y. (2019). Feature extraction in palmprint recognition using spiral of moment skewness and kurtosis algorithm. Pattern Analysis and Applications, 22, 1197-1205. https://doi.org/10.1007/s10044-018-0712-5
Azimi-Saghin, B., Omid, M., Rezvani, F., & Arefi, M. (2023). An Algorithm to Extract the Defective Areas of Potato Tubers Infected with Black Scab Disease Using Fuzzy C Means Clustering for Automatic Grading. Biomechanism and Bioenergy Research, 2(1), 32-39. https://doi.org/10.22103/bbr.2023.21783.1046
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Borin, A., Ferrão, M. F., Mello, C., Cordi, L., Pataca, L. C., Durán, N., & Poppi, R. J. (2007). Quantification of Lactobacillus in fermented milk by multivariate image analysis with least-squares support-vector machines. Analytical and bioanalytical chemistry, 387, 1105-1112. https://doi.org/10.1007/s00216-006-0971-7
Cereceda, D., Medel-Vera, C., Ortiz, M., & Tramon, J. (2022). Roughness and condition prediction models for airfield pavements using digital image processing. Automation in Construction, 139, 104325. https://doi.org/10.1016/j.autcon.2022.104325
Chaudhary, A., Thakur, R., Kolhe, S., & Kamal, R. (2020). A particle swarm optimization based ensemble for vegetable crop disease recognition. Computers and Electronics in Agriculture, 178, 105747. https://doi.org/10.1016/j.compag.2020.105747
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Eid, S. M., El-Shamy, S., & Farag, M. A. (2022). Identification of milk quality and adulteration by surface-enhanced infrared absorption spectroscopy coupled to artificial neural networks using citrate-capped silver nanoislands. Microchimica Acta, 189(8), 301. https://doi.org/10.1007/s00604-022-05393-4
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Espejo-Carpio, F. J., Pérez-Gálvez, R., Guadix, A., & Guadix, E. M. (2018). Artificial neuronal networks (ANN) to model the hydrolysis of goat milk protein by subtilisin and trypsin. Journal of Dairy Research, 85(3), 339-346. https://doi.org/10.1017/S002202991800064X
Gallier, S., Gragson, D., Jiménez-Flores, R., & Everett, D. W. (2010). Surface characterization of bovine milk phospholipid monolayers by Langmuir isotherms and microscopic techniques. Journal of agricultural and food chemistry, 58(23), 12275-12285. https://doi.org/10.1021/jf102185a
Gholami, A., & Farshad, M. (2019). Fast hyperbolic Radon transform using chirp-z transform. Digital Signal Processing, 87, 34-42. https://doi.org/10.1016/j.dsp.2019.01.003
Ghosh, A., Seth, S. K., & Purkayastha, P. (2018). Undulation induced tuning of electron acceptance by edge-oxidized graphene oxide. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 204, 425-431. https://doi.org/10.1016/j.saa.2018.06.052
Gui, H., Xiang, J., Xing, T., Liu, J., Chu, Z., He, X., & Liu, C. (2022). Boundary element method with particle swarm optimization for solving potential problems. Advances in Engineering Software, 172, 103191. https://doi.org/10.1016/j.advengsoft.2022.103191
Han, B., Zhang, L., & Zhou, P. (2022). Comparison of milk fat globule membrane protein profile among bovine, goat and camel milk based on label free proteomic techniques. Food Research International, 162, 112097. https://doi.org/10.1016/j.foodres.2022.112097
Hornberg, A. (2017). Handbook of machine and computer vision: the guide for developers and users. John Wiley & Sons.
Hosainpour, A., Kheiralipour, K., Nadimi, M., & Paliwal, J. (2022). Quality assessment of dried white mulberry (Morus alba L.) using machine vision. Horticulturae, 8(11), 1011. https://doi.org/10.3390/horticulturae8111011
Kheiralipour, K., Nadimi, M., & Paliwal, J. (2022). Development of an intelligent imaging system for ripeness determination of wild pistachios. Sensors, 22(19), 7134. https://doi.org/10.3390/s22197134
Kheiralipour, K., & Nargesi, M. H. (2024). Classification of wheat flour levels in powdered spices using visual imaging. Journal of Agriculture and Food Research, 18, 101408. https://doi.org/10.1016/j.jafr.2024.101408
Kumar, R., Rao, P. S., Rana, S. S., & Ghosh, P. (2020). Comparative performance analysis of enzyme inactivation of soy milk by using RSM and ANN. Journal of Food Process Engineering, 43(11), e13530. https://doi.org/10.1111/jfpe.13530
Kumar, V., Chakravarty, A., Magotra, A., Patil, C., & Shivahre, P. (2019). Comparative study of ANN and conventional methods in forecasting first lactation milk yield in Murrah buffalo. Indian Journal of Animal Sciences, 89(11), 1262-1268. https://doi.org/10.56093/ijans.v89i11.95887
Li, S., Yang, Y., Chen, C., Li, L., Valencak, T. G., & Ren, D. (2021). Differences in milk fat globule membrane proteins among Murrah, Nili-Ravi and Mediterranean buffaloes revealed by a TMT proteomic approach. Food Research International, 139, 109847. https://doi.org/10.1016/j.foodres.2020.109847
McCarthy, K., Lopetcharat, K., & Drake, M. (2017). Milk fat threshold determination and the effect of milk fat content on consumer preference for fluid milk. Journal of Dairy Science, 100(3), 1702-1711. https://doi.org/10.3168/jds.2016-11417
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Ming, J. L. K., Anuar, M. S., How, M. S., Noor, S. B. M., Abdullah, Z., & Taip, F. S. (2021). Development of an artificial neural network utilizing particle swarm optimization for modeling the spray drying of coconut milk. Foods, 10(11), 2708. https://doi.org/10.3390/foods10112708
Moate, P., Jacobs, J., Hannah, M., Morris, G., Beauchemin, K., Hess, P. A., . . . Wales, W. (2018). Adaptation responses in milk fat yield and methane emissions of dairy cows when wheat was included in their diet for 16 weeks. Journal of Dairy Science, 101(8), 7117-7132. https://doi.org/10.3168/jds.2017-14334
Mu, S., Stieger, M., & Boesveldt, S. (2022). Olfactory discrimination of fat content in milks is facilitated by differences in volatile compound composition rather than odor intensity. Food Chemistry, 393, 133357. https://doi.org/10.1016/j.foodchem.2022.133357
Nargesi, M. H., & Kheiralipour, K. (2024). Ability of visible imaging and machine learning in detection of chickpea flour adulterant in original cinnamon and pepper powders. Heliyon, 10(16). https://doi.org/10.1016/j.heliyon.2024.e35944
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Rajeshkumar, G., Kumar, M. V., Kumar, K. S., Bhatia, S., Mashat, A., & Dadheech, P. (2023). An Improved Multi-Objective Particle Swarm Optimization Routing on MANET. Computer Systems Science & Engineering, 44(2), 1187-1200. https://doi.org/10.32604/csse.2023.026137
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Abdellatif, A. A., El Hamd, M. A., Salman, K. H., Abd-El-Rahim, A. M., El-Maghrabey, M., & Tawfeek, H. M. (2020). Integrative physicochemical and HPLC assessment studies for the inclusion of lornoxicam in buffalo's milk fat globules as a potential carrier delivery system for lipophilic drugs. Microchemical Journal, 152, 104321. https://doi.org/10.1016/j.microc.2019.104321
Ali, A. H., Wei, W., Khalifa, S. A., Zhang, X., & Wang, X. (2021). Effect of pasteurisation, homogenisation and freeze‐drying on bovine and buffalo milk fat triacylglycerols profile. International Journal of Dairy Technology, 74(3), 472-488. https://doi.org/10.1111/1471-0307.12781
Ali, F. (2022). Nonthermal and thermal treatments impact the structure and microstructure of milk fat globule membrane. International Journal of Dairy Technology, 75(2), 338-347. https://doi.org/10.1111/1471-0307.12840
Amsaraj, R., Ambade, N. D., & Mutturi, S. (2021). Variable selection coupled to PLS2, ANN and SVM for simultaneous detection of multiple adulterants in milk using spectral data. International Dairy Journal, 123, 105172. https://doi.org/10.1016/j.idairyj.2021.105172
Asmare, M. H., Asirvadam, V. S., & Hani, A. F. M. (2015). Image enhancement based on contourlet transform. Signal, Image and Video Processing, 9, 1679-1690. https://doi.org/10.1007/s11760-014-0626-7
Attallah, B., Serir, A., & Chahir, Y. (2019). Feature extraction in palmprint recognition using spiral of moment skewness and kurtosis algorithm. Pattern Analysis and Applications, 22, 1197-1205. https://doi.org/10.1007/s10044-018-0712-5
Azimi-Saghin, B., Omid, M., Rezvani, F., & Arefi, M. (2023). An Algorithm to Extract the Defective Areas of Potato Tubers Infected with Black Scab Disease Using Fuzzy C Means Clustering for Automatic Grading. Biomechanism and Bioenergy Research, 2(1), 32-39. https://doi.org/10.22103/bbr.2023.21783.1046
Berti, J., Grosso, N. R., Fernandez, H., Pramparo, M. C., & Gayol, M. F. (2018). Sensory quality of milk fat with low cholesterol content fractioned by molecular distillation. Journal of the Science of Food and Agriculture, 98(9), 3478-3484. https://doi.org/10.1002/jsfa.8866
Borin, A., Ferrão, M. F., Mello, C., Cordi, L., Pataca, L. C., Durán, N., & Poppi, R. J. (2007). Quantification of Lactobacillus in fermented milk by multivariate image analysis with least-squares support-vector machines. Analytical and bioanalytical chemistry, 387, 1105-1112. https://doi.org/10.1007/s00216-006-0971-7
Cereceda, D., Medel-Vera, C., Ortiz, M., & Tramon, J. (2022). Roughness and condition prediction models for airfield pavements using digital image processing. Automation in Construction, 139, 104325. https://doi.org/10.1016/j.autcon.2022.104325
Chaudhary, A., Thakur, R., Kolhe, S., & Kamal, R. (2020). A particle swarm optimization based ensemble for vegetable crop disease recognition. Computers and Electronics in Agriculture, 178, 105747. https://doi.org/10.1016/j.compag.2020.105747
Correa, K. d. P., Silva, M. E. T. d., Oliveira, D. R. B. d., Oliveira, A. F. d., Santos, I. J. B., Oliveira, E. B. d., & Coimbra, J. S. d. R. (2022). Influence of homogenization in the physicochemical quality of human milk and fat retention in gastric tubes. Journal of Human Lactation, 38(2), 309-322. https://doi.org/10.1177/08903344211031456
Dallago, G. M., de Figueiredo, D. M., de Resende Andrade, P. C., dos Santos, R. A., Lacroix, R., Santschi, D. E., & Lefebvre, D. M. (2019). Predicting first test day milk yield of dairy heifers. Computers and Electronics in Agriculture, 166, 105032. https://doi.org/10.1016/j.compag.2019.105032
Demir, B., Sayıncı, B., Çetin, N., Yaman, M., Çömlek, R., Aydın, Y., & Sutyemez, M. (2018). Elliptic Fourier based analysis and multivariate approaches for size and shape distinctions of walnut (Juglans regia L.) cultivars. Grasas y Aceites, 69(4), e271-e271. https://doi.org/10.3989/gya.0104181
Djaowé, G., Bitjoka, L., Boukar, O., Libouga, D. G., & Waldogo, B. (2013). Measurement of the rennet clotting time of milk by digital image sequences (2D+ t) processing. Journal of Food Engineering, 114(2), 235-241. https://doi.org/10.1016/j.jfoodeng.2012.07.024
Eid, S. M., El-Shamy, S., & Farag, M. A. (2022). Identification of milk quality and adulteration by surface-enhanced infrared absorption spectroscopy coupled to artificial neural networks using citrate-capped silver nanoislands. Microchimica Acta, 189(8), 301. https://doi.org/10.1007/s00604-022-05393-4
Ertugay, M. F., ŞENGÜL, M., & ŞENGÜL, M. (2004). Effect of ultrasound treatment on milk homogenisation and particle size distribution of fat. Turkish Journal of Veterinary & Animal Sciences, 28(2), 303-308.
Espejo-Carpio, F. J., Pérez-Gálvez, R., Guadix, A., & Guadix, E. M. (2018). Artificial neuronal networks (ANN) to model the hydrolysis of goat milk protein by subtilisin and trypsin. Journal of Dairy Research, 85(3), 339-346. https://doi.org/10.1017/S002202991800064X
Gallier, S., Gragson, D., Jiménez-Flores, R., & Everett, D. W. (2010). Surface characterization of bovine milk phospholipid monolayers by Langmuir isotherms and microscopic techniques. Journal of agricultural and food chemistry, 58(23), 12275-12285. https://doi.org/10.1021/jf102185a
Gholami, A., & Farshad, M. (2019). Fast hyperbolic Radon transform using chirp-z transform. Digital Signal Processing, 87, 34-42. https://doi.org/10.1016/j.dsp.2019.01.003
Ghosh, A., Seth, S. K., & Purkayastha, P. (2018). Undulation induced tuning of electron acceptance by edge-oxidized graphene oxide. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 204, 425-431. https://doi.org/10.1016/j.saa.2018.06.052
Gui, H., Xiang, J., Xing, T., Liu, J., Chu, Z., He, X., & Liu, C. (2022). Boundary element method with particle swarm optimization for solving potential problems. Advances in Engineering Software, 172, 103191. https://doi.org/10.1016/j.advengsoft.2022.103191
Han, B., Zhang, L., & Zhou, P. (2022). Comparison of milk fat globule membrane protein profile among bovine, goat and camel milk based on label free proteomic techniques. Food Research International, 162, 112097. https://doi.org/10.1016/j.foodres.2022.112097
Hornberg, A. (2017). Handbook of machine and computer vision: the guide for developers and users. John Wiley & Sons.
Hosainpour, A., Kheiralipour, K., Nadimi, M., & Paliwal, J. (2022). Quality assessment of dried white mulberry (Morus alba L.) using machine vision. Horticulturae, 8(11), 1011. https://doi.org/10.3390/horticulturae8111011
Kheiralipour, K., Nadimi, M., & Paliwal, J. (2022). Development of an intelligent imaging system for ripeness determination of wild pistachios. Sensors, 22(19), 7134. https://doi.org/10.3390/s22197134
Kheiralipour, K., & Nargesi, M. H. (2024). Classification of wheat flour levels in powdered spices using visual imaging. Journal of Agriculture and Food Research, 18, 101408. https://doi.org/10.1016/j.jafr.2024.101408
Kumar, R., Rao, P. S., Rana, S. S., & Ghosh, P. (2020). Comparative performance analysis of enzyme inactivation of soy milk by using RSM and ANN. Journal of Food Process Engineering, 43(11), e13530. https://doi.org/10.1111/jfpe.13530
Kumar, V., Chakravarty, A., Magotra, A., Patil, C., & Shivahre, P. (2019). Comparative study of ANN and conventional methods in forecasting first lactation milk yield in Murrah buffalo. Indian Journal of Animal Sciences, 89(11), 1262-1268. https://doi.org/10.56093/ijans.v89i11.95887
Li, S., Yang, Y., Chen, C., Li, L., Valencak, T. G., & Ren, D. (2021). Differences in milk fat globule membrane proteins among Murrah, Nili-Ravi and Mediterranean buffaloes revealed by a TMT proteomic approach. Food Research International, 139, 109847. https://doi.org/10.1016/j.foodres.2020.109847
McCarthy, K., Lopetcharat, K., & Drake, M. (2017). Milk fat threshold determination and the effect of milk fat content on consumer preference for fluid milk. Journal of Dairy Science, 100(3), 1702-1711. https://doi.org/10.3168/jds.2016-11417
Milovanovic, B., Tomovic, V., Djekic, I., Miocinovic, J., Solowiej, B. G., Lorenzo, J. M., . . . Tomasevic, I. (2021). Colour assessment of milk and milk products using computer vision system and colorimeter. International Dairy Journal, 120, 105084. https://doi.org/10.1016/j.idairyj.2021.105084
Ming, J. L. K., Anuar, M. S., How, M. S., Noor, S. B. M., Abdullah, Z., & Taip, F. S. (2021). Development of an artificial neural network utilizing particle swarm optimization for modeling the spray drying of coconut milk. Foods, 10(11), 2708. https://doi.org/10.3390/foods10112708
Moate, P., Jacobs, J., Hannah, M., Morris, G., Beauchemin, K., Hess, P. A., . . . Wales, W. (2018). Adaptation responses in milk fat yield and methane emissions of dairy cows when wheat was included in their diet for 16 weeks. Journal of Dairy Science, 101(8), 7117-7132. https://doi.org/10.3168/jds.2017-14334
Mu, S., Stieger, M., & Boesveldt, S. (2022). Olfactory discrimination of fat content in milks is facilitated by differences in volatile compound composition rather than odor intensity. Food Chemistry, 393, 133357. https://doi.org/10.1016/j.foodchem.2022.133357
Nargesi, M. H., & Kheiralipour, K. (2024). Ability of visible imaging and machine learning in detection of chickpea flour adulterant in original cinnamon and pepper powders. Heliyon, 10(16). https://doi.org/10.1016/j.heliyon.2024.e35944
Phillips, L. G., Mcgiff, M. L., Barbano, D. M., & Lawless, H. T. (1995). The influence of fat on the sensory properties, viscosity, and color of lowfat milk. Journal of Dairy Science, 78(6), 1258-1266. https://doi.org/10.3168/jds.S0022-0302(95)76746-7
Pluschke, A., Gilbert, M., Williams, B., van den Borne, J., Schols, H., & Gerrits, W. (2016). The effect of replacing lactose by starch on protein and fat digestion in milk-fed veal calves. animal, 10(8), 1296-1302. https://doi.org/10.1017/S1751731116000252
Ragni, L., Iaccheri, E., Cevoli, C., & Berardinelli, A. (2016). Spectral-sensitive pulsed photometry to predict the fat content of commercialized milk. Journal of Food Engineering, 171, 95-101. https://doi.org/10.1016/j.jfoodeng.2015.10.017
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