Acta Scientific Computer Sciences

Review Article Volume 4 Issue 7

Using Artificial Intelligence for Dynamically Selecting of White Cheese Production Parameters

Yasin Ozdemir1*, Salih Akyuz2, Omer Nuri Cam3 and Basri Kul3

1Ataturk Horticultural Central Research Institute, Food Technologies Department, Yalova, Turkey
2Akyuz Dairy Products Food Industry, Kutahya, Turkey
3Uludag University, Technical Sciences Vocational School, Bursa, Turkey

*Corresponding Author: Yasin Ozdemir, Ataturk Horticultural Central Research Institute, Food Technologies Department, Yalova, Turkey.

Received: May 21,2022; Published: June 02, 2022

Abstract

Artificial intelligence applications are increasing rapidly in many industries. But it is still in its infancy for food industry. Although there are industrial applications and scientific studies of machine learning on quality control of foods, there are no dynamic approaches that will enable quality control with artificial intelligence or more importantly, the selection and use of process parameters during food production. In this research, it is aimed to reveal the potential of dynamically determining the optimum parameters such as pasteurization norms, enzyme application time, enzyme amount and packaging pH in white cheese production by using artificial intelligence. In this way, it will be possible with future studies to make higher quality and more efficient productions by using artificial intelligence for white cheese production industry.


Keywords: Artificial Neural Networks; Cheese Quality; Cheese Production; Supervised Learning

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Citation

Citation: Yasin Ozdemir., et al. “Using Artificial Intelligence for Dynamically Selecting of White Cheese Production Parameters". Acta Scientific Computer Sciences 4.7 (2022): 18-23.

Copyright

Copyright: © 2022 Yasin Ozdemir., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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Acceptance rate35%
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