Assessment of Milk Quality Optimization of Yogurt Fermentation

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Year : August 20, 2024 at 10:56 am | [if 1553 equals=””] Volume : [else] Volume :[/if 1553] | [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] : | Page : –

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Sejal Gilda1, Meghali Waghmode, Arpita Sable,

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  1. Student, Assistant Professor, Student Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Pune, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Pune, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Pune Maharashtra, Maharashtra India, India, India
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Abstract

nIn the creation of new products, yogurt makers need to prioritize consumer preferences to enhance their market share. An advanced prediction method will help in grasping the fundamental connection between consumer preferences and sensory attributes. This study introduces a new deep learning approach that employs an autoencoder to derive product features from expert-scored sensory attributes. These sensory features are then analyzed through support vector machine regression to align them with consumer preferences. Manual distance measurement is often prone to human error. This project aims to achieve precise and consistent measurements for short distances. The device can measure distances between 0.5 meters and 4 meters with an accuracy of 1 centimeter. This project employs ultrasonic sensors to determine distance by emitting ultrasonic waves at a frequency of 40 kHz. The circuit, which is controlled by an ATmega microcontroller, calculates the distance using the speed of sound at 25°C, factoring in the ambient temperature and the time taken for the waves to return. The measured distance is then displayed on an LCD module. The importance of this project lies in its capability to accurately measure distances to different obstacles. This device is applicable in numerous fields, including construction for distance measurement, robotics, car sensors for obstacle avoidance, and many other uses

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Keywords: Yogurt; Model-Train; Consumer Preference; Autoencoder; Support Vector Machine.

n[if 424 equals=”Regular Issue”][This article belongs to Research & Reviews : Journal of Dairy Science & Technology(rrjodst)]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Research & Reviews : Journal of Dairy Science & Technology(rrjodst)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article: Sejal Gilda1, Meghali Waghmode, Arpita Sable. Assessment of Milk Quality Optimization of Yogurt Fermentation. Research & Reviews : Journal of Dairy Science & Technology. August 20, 2024; ():-.

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How to cite this URL: Sejal Gilda1, Meghali Waghmode, Arpita Sable. Assessment of Milk Quality Optimization of Yogurt Fermentation. Research & Reviews : Journal of Dairy Science & Technology. August 20, 2024; ():-. Available from: https://journals.stmjournals.com/rrjodst/article=August 20, 2024/view=0

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References

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  1. S Louis and K Don,” Milk production estimates using feed-forward artificial neural networks”, Computers and Electronics in Agriculture, vol. 32, no. 1, pp. 21-30, 2023.
  2. Doran, P. Bioprocess Engineering Principles, 2nd ed.; Academic Press: London, UK, 2021. [Google Scholar]
  3. Stankowski, G. Ostojic, I. Senk, M. RakicSkokovic, S. Trivunovic, and D. Kucevic, “Dairy cow monitoring by RFID,” Scientia Agricola, vol. 69, no. 1, pp. 75–80, 2020
  4. Wang, Q.; Parsons, R.; Zhang, G. China’s dairy markets: Trends, disparities, and implications for trade. China Agri. Econ. Rev. 2010, 2, 356–371. [Google Scholar].
  5. Blanc, B., and Odet, G. 1981. Appearance flavor and texture aspects: Recent development. Azevedo et al., 2023 C.F. Azevedo, M.D.V. de Resende, F.F. E Silva, J.M.S.
  6. Viana, M.S.F. Valente, M.F.R. Resende Jr., P. Munoz Ridge, LASSO and Bayesian additivedominance genomic models BMC Genet., 16 (2023), p. 105 https://doi.org/10.1186/s12863-015-0264-2. Ghobakhloo, The future of manufacturing industry: A strategic roadmap toward industry 4.0, J. Manuf. Technol. Manag., vol.29, no.6, pp.910-936, 2021.
  7. Pohjanheimo, T.; Sandell, M. Explaining the liking for drinking yoghurt: The role of sensory quality, food choice motives, health concern and product information. Dairy J. 2009, 19, 459–466.
  8. Tomic, O.; Nilsen, A.; Martens, M.; Næs, T. Visualization of sensory profiling data for performance monitoring. LWT Food Sci. Technol. 2007, 40, 262–269.
  9. Fahad, S.A.; Yahya, A.E. Big Data Visualization: Allotting by R and Python with GUI Tools. In Proceedings of the International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Shah Alam, Malaysia, 11–12 July 2018; pp. 1–8.
  10. Wu, L.; Pu, H.; Sun, D.-W. Novel techniques for evaluating freshness quality attributes of fish: A review of recent developments. Trends Food Sci. Technol. 2019, 83, 259–273.
  11. Vidnerová, P.; Neruda, R. Evolving KERAS Architectures for Sensor Data Analysis. In Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, Czech Republic, 3–6 September 2017; pp. 109–112.
  12. Shawe-Taylor, J.; Bartlett, P.L.; Williamson, R.C.; Anthony, M. Structural risk minimization over data-dependent hierarchies. IEEE Trans. Inf. Theory 1998, 44, 1926–1940.
  13. Yang, Y.; Full, R.; Huiyov, C. SVR mathematical model and methods for sale prediction. Syst. Eng. Electron. 2007, 18, 18–769.
  14. Yenket, R.; Chambers, E., IV; Adhikari, K. A comparison of seven preference mapping techniques using four software programs. Sens. Stud. 2011, 26, 135–150.
  15. Murtagh, F.; Legendre, P. Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? Classif. 2014, 31, 274–295.
  16. Bi, K.; Zhang, D.; Song, Z.; Qiu, T.; Huang, Y. A PLSR Model for Consumer Preference Prediction of Yoghurt from Sensory Attributes Profiles. In Computer Aided Chemical Engineering; Elsevier: Amsterdam, The Netherlands, 2019; Volume 46, pp. 1477–1482.
  17. Wu, H.; Zhao, J. Deep convolutional neural network model based chemical process fault diagnosis. Chem. Eng. 2018, 115, 185–197.
  18. Cheng, F.; He, Q.P.; Zhao, J. A novel process monitoring approach based on variational recurrent autoencoder. Comput. Chem. Eng. 2019, 129, 106515.

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[if 424 not_equal=””][else]Ahead of Print[/if 424] Subscription Review Article

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Volume
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424]
Received July 22, 2024
Accepted August 1, 2024
Published August 20, 2024

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