Prediction Modeling of Kulfi Freezing: A Regression Analysis Approach

Abstract

Kulfi is a popular frozen treat that originated on the Indian subcontinent. Its distinct tastes and texture are a result of the intricate interaction between various ingredients and freezing dynamics. The act of freezing has a crucial role in sculpting the crystalline composition and sensory characteristics of kulfi, hence impacting its overall quality. It becomes crucial to maintain the exact control over freezing conditions in order to maintain the tastes and creamy quality that characterize this traditional treat. Beyond culinary aesthetics, knowing and anticipating freezing dynamics is important in the larger context of frozen sweets. It is essential for maximizing production procedures, guaranteeing consistency in product quality and raising overall manufacturing efficiency in the food industry. A useful technique for figuring out the complex links controlling freezing behaviour is regression modelling. Regression modelling in the field of food science has been extensively studied, however there is still a significant study void in the area of kulfi freezing dynamics. Regression modelling is used in this work to specifically target the kulfi freezing domain in an effort to close this gap. The goal is to create a reliable regression model that, using the temperature dynamics of kulfi samples as a basis, can forecast refrigerator air temperature with accuracy. The created regression model exhibits excellent predicted accuracy (Mean residual of 0.0243℃) and offers a numerical framework for performance assessment (R² = 1, RMSE = 0.45837, MSE = 0.21011 and MAE = 0.20403). This research not only contributes to the wider field of food science but also elevates the artistic skill of kulfi production to new peaks by bridging the current research gap in regression modelling for kulfi freezing dynamics.