Modernization of the milking system with a device for express analysis of milk quality

Milking systems used in Russia have the potential to be upgraded with devices for in-line control of milk quality parameters. Monitoring the composition of milk and tracking anomalies in the concentration of somatic cells in real time is especially important for rapid response to changes in the parameters of the physiological state of animals and timely intervention before low-quality milk enters the common reservoir. This paper provides an example of the modernization of the “Herringbone” milking system with the function of evaluating the quality of milk during milking. The milk quality express analysis device used to modernize the milking system is optical and does not affect the flow of milk in the milk hose of the milking system. The device allows for in-line analysis of the percentage concentration of fat and quantitative analysis of the concentration of somatic cells in milk with a threshold detection level of 900–1000 thousand cells / ml, analyzing a flow volume of up to 6 liters/min. In the study, the operability of the device to analyze raw cow′s milk with two different fat content parameters — 2.53% and 3.16% and a concentration of 1 × 10⁶ somatic cells per 1 ml was evaluated in two stages. As a result of the experiment, the average value ± standard deviation of fat content was (2.75 ± 0.16)% and (3.37 ± 0.20)%, and somatic cells were (0.096 ± 0.007) cu and (0.102 ± 0.006) cu, which corresponds to the range of 900–1000 thousand cells / ml. The errors of the average values of the measured fat content of milk amounted to 0.2–0.3% of the fat content of the measured milk. The maximum coefficient of variation for fat content measurements is 6%, and for qualitative analysis of somatic cells — 7%, which demonstrates the stability of the device and the success of the modernization of the milking system. In the future, the improvement of the system providing on-line monitoring of the milking process will continue.

1. Lobachevsky Ya.P., Dorokhov A.S. Digital technologies and robotic devices in the agriculture. Agricultural Machinery and Technologies. 2021; 15(4): 6‒10 (in Russian). https://doi.org/10.22314/2073-7599-2021-15-4-6-10

2. Tsench Yu.S. Scientific and Technological Potential as the Main Factor for Agricultural Mechanization Development. Agricultural Machinery and Technologies. 2022; 16(2): 4‒13 (in Russian). https://doi.org/10.22314/2073-7599-2022-16-2-4-13

3. Zolkin A.L., Matvienko E.V., Bityutsky A.S., Shamina S.V., Dragulenko V.V. Introduction of advanced information technologies in agriculture. E3S Web of Conferences. V International Scientific Forum on Computer and Energy Sciences (WFCES 2023). 2023; 419: 03002. https://doi.org/10.1051/e3sconf/202341903002

4. Zhuravleva L., Zarubina E., Ruchkin A., Simachkova N., Chupina I. Development of the agrarian and industrial complex of Russia through the use of new technologies. E3S Web of Conferences. International Scientific and Practical Conference “Ensuring the Technological Sovereignty of the Agro-Industrial Complex: Approaches, Problems, Solutions” (ETSAIC2023). 2023; 395: 05007. https://doi.org/10.1051/e3sconf/202339505007

5. Chernyakov M., Chemyakova M., Suleymanov Sh. The use of digital technologies in the agro-industrial complex. International Scientific and Practical Conference “Current Issues of Biology, Breeding, Technology and Processing of Agricultural Crops” (CIBTA2022). Conference Proceedings (To the 110th anniversary of V.S. Pustovoit All-Russian Research Institute of Oil Crops). 2023; 277: 020007-1–020007-6. https://doi.org/10.1063/5.0140164

6. Tsvetkova I.I., Vakhovskaya M.Yu. The use of digital technologies in agricultural management. II International Conference on Agriculture, Earth Remote Sensing and Environment (RSE-II-2023). Les Ulis. 2023; 392: 01028. https://doi.org/10.1051/e3sconf/202339201028

7. Burmistrov D.E. et al. Application of Optical Quality Control Technologies in the Dairy Industry: An Overview. Photonics. 2021; 8(12): 551. https://doi.org/10.3390/photonics8120551

8. Khakimov A.R., Pavkin D.Yu., Yurochka S.S., Astashev M.E., Dovlatov I.M. Development of an Algorithm for Rapid Herd Evaluation and Predicting Milk Yield of Mastitis Cows Based on Infrared Thermography. Applied Sciences. 2022; 12(13): 6621. https://doi.org/10.3390/app12136621

9. Baerinas M.N., Neverova O.P., Gorelik O.V., Gritsenko S.A., Rebezov M.B., Isaeva K.S. Dynamics of variation of dairy characteristics in cows when using the feed additive “VivAktiv”. Agrarian science. 2024; (5): 63–68 (in Russian). https://doi.org/10.32634/0869-8155-2024-382-5-63-68

10. Belookov A.A., Belookova O.V., Gorelik O.V., Rebezov M.B. The composition and properties of the milk of black-and-white cows of different genotypes. Agrarian science. 2023; (3): 62–69 (in Russian). https://doi.org/10.32634/0869-8155-2023-368-3-62-69

11. Kanev P.N., Gorelik O.V., Kharlap S.Yu., Gorelik A.S., Rebezov M.B. The conjugation of productive features of dairy cattle of the Holstein breed. Agrarian science. 2024; (3): 92–97 (in Russian). https://doi.org/10.32634/0869-8155-2024-380-3-92-97

12. He C., He H., Chang J., Chen B., Ma H., Booth M.J. Polarisation optics for biomedical and clinical applications: a review. Light: Science & Applications. 2021; 10: 194. https://doi.org/10.1038/s41377-021-00639-x

13. Ghosh N., Vitkin A.I. Tissue polarimetry: concepts, challenges, applications, and outlook. Journal of biomedical optics. 2011; 16(11): 110801. https://doi.org/10.1117/1.3652896

14. Ramella-Roman J.C., Saytashev I., Piccini M. A review of polarization-based imaging technologies for clinical and preclinical applications. Journal of Optics. 2020; 22(12): 123001. https://doi.org/10.1088/2040-8986/abbf8a

15. Li P. et al. Temperature dependent red luminescence from a distorted Mn4+ site in CaAl4O7: Mn4+. Optics Express. 2013; 21(16): 18943‒18948. https://doi.org/10.1364/OE.21.018943

16. Karoui R., De Baerdemaeker J. A review of the analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products. Food Chemistry. 2007; 102(3): 621‒640. https://doi.org/10.1016/j.foodchem.2006.05.042

17. Ageev A.I., Osiptsov A.N. Shear Flow of a Viscous Fluid over a Cavity with a Pulsating Gas Bubble. Doklady Physics. 2020; 65(7): 242‒245. https://doi.org/10.1134/S1028335820050031

18. Chue-Sang J., Bai Y., Stoff S., Straton D., Ramaswamy S.D., Ramella-Roman J.C. Use of combined polarization-sensitive optical coherence tomography and Mueller matrix imaging for the polarimetric characterization of excised biological tissue. Journal of Biomedical Optics. 2016; 21(7): 071109. https://doi.org/10.1117/1.JBO.21.7.071109

19. Khakimov A.R. et al. Effects of Milking System Operating Conditions on the Milk-Fat-Percentage Measuring Accuracy of an Inline Light-Scattering Sensor. Applied Sciences. 2023; 13(21): 11836. https://doi.org/10.3390/app132111836

20. Shkirin A.V., Astashev M.E., Ignatenko D.N., Suyazov N.V., Vedunova M.V., Gudkov S.V. Laser Scatterometric Device for Inline Measurement of Fat Percentage and the Concentration Level of Large-Scale Impurities in Milk. Applied Sciences. 2022; 12(24): 12517. https://doi.org/10.3390/app122412517

21. Shkirin A.V., Astashev M.E., Ignatenko D.N., Kozlov V.A., Gudkov S.V. Fluorescence-scatterometric technique for measuring the percentage content of dispersed components of emulsions in application to milk quality assessment. Kratkiye soobshcheniya po fizike FIAN. 2023; 50(5): 14‒24 (in Russian). https://www.elibrary.ru/kabwrz