Artificial Intelligence-Based Analysis of Ice Cream Melting Behavior Under Various Ingredients
Zhang Lai Bin, Zhen Bin It

TL;DR
This study uses AI and video analysis to evaluate how different stabilizers affect ice cream melting behavior, aiming to optimize texture, stability, and cost-effectiveness in formulations.
Contribution
It introduces a novel AI-based method for analyzing melting dynamics and compares the effectiveness of various stabilizers in ice cream.
Findings
All stabilizers helped maintain foam structure after melting.
Re-frozen samples showed increased sturdiness.
Some stabilizers provided better melting resistance and structural support.
Abstract
The stability of ice cream during melting is a critical factor for consumer's acceptance and product quality. With the commonly added stabilizer to improve texture, structure and slower melting as the factors to analyze. This report explores the effects of locust bean gum, guar gum, maltodextrin, and carrageenan on the melting behavior of homemade ice cream. The main objective was to assess how these additives influence melting resistance and to identify a more cost-effective recipe formulation. Ice cream samples incorporating each additive were prepared and subjected to melting tests under controlled conditions. Timelapse recordings were used to capture and analyze the progression of melting over time. Python and OpenCV is used for process and analysis. Observations revealed that all samples retained a foam-like structure even after melting, suggesting the stabilizers contributed to…
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Taxonomy
TopicsProteins in Food Systems · Microencapsulation and Drying Processes · Food composition and properties
