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Abstract

The quality of raw milk is of paramount importance, as it directly affects the safety and nutritive value of dairy products. This research paper delves into the significant influence of various process parameters on the quality attributes of raw milk using an optimization-based approach. Through a comprehensive experimental design, this study systematically investigates the effects of parameters such as pH, temperature, and density. Utilizing a response surface methodology (RSM) coupled with statistical analysis, the paper evaluates the interplay between these process parameters and the resulting quality outcomes. By employing Box Behnken Design (BBD) experiments, the research establishes mathematical models that predict the impact of different parameter combinations on raw milk quality. These models provide valuable insights into the optimal conditions for achieving desired quality attributes while ensuring safety and stability. The findings underscore the intricate relationships between process parameters and raw milk quality, revealing opportunities for enhancing milk processing practices. Furthermore, the study highlights the potential for optimizing raw milk quality within practical constraints, guiding dairy industry professionals in making informed decisions to balance quality and efficiency. Ultimately, this research contributes to a deeper understanding of the complex dynamics governing raw milk quality and offers a framework for optimizing dairy processing methods to meet both consumer expectations and regulatory standards

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