AI-Driven Software Framework for Intelligent Optimization of Sugar Reduction Strategies in Confectionery Using Polyols and High-Intensity Sweeteners
Abstract
Growing consumer awareness regarding health and nutrition has increased the demand for reduced-sugar confectionery products.
However, sugar performs multiple functional roles in confectionery systems, including sweetness, bulking, texture formation,
crystallization control, and shelf stability, making its reduction a complex formulation challenge. This study proposes an AI-driven
software framework designed to intelligently optimize sugar reduction strategies in confectionery formulations using polyols and high-intensity sweeteners. The developed framework integrates machine learning algorithms, ingredient property databases, and predictive
modeling techniques to support researchers and product developers in designing optimized reduced-sugar formulations. The software architecture consists of modules for ingredient selection, sweetness equivalence prediction, physicochemical property estimation, and multi-objective optimization. Polyols such as sorbitol, xylitol, and maltitol are incorporated to provide bulking effects and desirablemouthfeel, while high-intensity sweeteners including steviol glycosides and sucralose are used to achieve the required sweetness intensity. A structured dataset comprising formulation ratios, sweetness intensity, water activity, texture parameters, and sensory evaluation scores is used to train supervised learning models for prediction and optimization. The framework applies multi-objective optimization algorithms to balance key formulation constraints including sweetness profile, caloric reduction, crystallization behavior, and storage stability. The proposed AI-enabled approach demonstrates significant potential in improving formulation efficiency and guiding intelligent sugar substitution strategies. This study highlights the interdisciplinary integration of software engineering, machine learning, and food product development for designing healthier next-generation confectionery products.
Keywords:
rtificial Intelligence, Sugar Reduction, Confectionery Optimization, Polyols, High-Intensity Sweeteners, Predictive Food FormulationPublished
Issue
Section
License
Copyright (c) 2026 International Journal on Emerging Research Areas

This work is licensed under a Creative Commons Attribution 4.0 International License.
All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Similar Articles
- Alan K George, Arpita Mary Mathew, Asin Mary Jacob, Elizabeth Antony, Shiney Thomas, Lung Cancer Subtype Classification Using Deep Learning Models , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Linsa Mathew, Jifith Joseph, George P Kurias, Gokul Krishna A U, Sharunmon R, TraceFusion: Precision AI for Missing and Wanted Person Detection , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Fabeela Ali Rawther, Raihana Rasaldeen, Stefi Marshal Fernandez, Irin Rose Jaison, Ria Mariam Mathews, A Survey on Automating Answer-Sheet Evaluation Using AI Techniques , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- Dr. Indu John, A Adithya, Alwin Rajan, Amal Biso George, Farhaan M Hussain, HEALTH GUARD-A Multiple Disease Prediction Model Based on Machine learning , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Selin Sam, Ameen Shouketh, Eby Jo, Jithin Russel, Joyal Anto, Muhammed Nihal K, Animal Detection Using Footprint , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Amrutha Suresh, Bibin Binu, Karthik Prakash, Nandana S, Thomas George, Deepa J, Campus Guide Robot , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- M Sreedharsh, S Saurav, Albin Joseph, Sravan Chandran , Lida K Kuriakose, Childhood Epilepsy Syndrome Classification through a Deep Learning Network with Clinical History Integration , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Avinash Krishnan, Belda Ben Thomas, Fr Siju John, Bava Kurian Varghese, Ajumon C Thampi, Computer Aided Carbon Footprint Estimation in Educational Institutions , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Merin Wilson, Muhammed Sajid N, Nandana L P, Nanda Santhosh, Rahul M, Mekha Jose, A Review on Deep Learning and IoT-Based Road Surface Damage Detection , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Goutham P Raj, Gregan George, Hadii Hasan, John Ashwin Delmon, V Pradeeba, COMPREHENSIVE VEHICLE SERVICES & E-COMMERCE PLATFORM WITH PRICE PREDICTION USING ML , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
You may also start an advanced similarity search for this article.
