Authors : Amitesh Verma

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/syytxxpa

Scribd : https://tinyurl.com/3smkpbv2

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG1236

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The ability of Indian mothers to intuitively assess the ideal salt levels for different dishes and family members is truly remarkable. They take into account several variables, such as the current weather, the day of the week, the emotional state of the family, portion sizes, and the specific traits of the ingredients used. This research investigates the fundamental processes behind this intuitive approach, developing a detailed mathematical framework to capture the nuances of salt measurement. The measurement of salt in cooking is an intricate art form that intertwines cultural insights, personal experience, and instinctual knowledge. For Indian mothers, the ability to estimate salt without precise instruments is a time-honored culinary skill, passed down through generations. This study investigates the subtleties of this practice by utilizing mathematical modeling and machine learning techniques. By analyzing data collected from a group of Indian mothers, the research aims to quantify and reveal the patterns that guide their intuitive salt measurements. The findings illuminate the balance between precision and intuition in traditional cooking methods and propose avenues for integrating these age-old practices into the realm of modern culinary technology. The model is akin to machine learning algorithms, facilitating the passing down of culinary expertise to future generations. We develop a formula to determine the ideal salt quantity and offer visual aids, including graphs and tables, to demonstrate the relationship between various factors and the amount of salt needed.

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