Neural Network Chemical Compound Replacement for Plant-Based Foods
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Solution Overview
Problem
Current methods for developing plant-based food alternatives struggle to match the taste and texture of animal-based foods, relying on inefficient and time-consuming manual laboratory processes, and often require expensive or hard-to-find chemical compounds from animal sources.
Innovation Solution
A neural network-based model is trained on source chemical compounds and their flavors/odors to generate embeddings, allowing for the identification of alternative chemical compounds that recreate the sensory properties of target compounds, using a graph neural network and feed forward network to suggest plant-based substitutes for animal-derived ingredients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual laboratory processes are used to develop plant-based food alternatives, then ingredient combinations can be tested, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical laboratory processes with an artificial intelligence system that uses machine learning models to predict ingredient combinations and their sensory properties. The AI system processes chemical compound data and generates predictions about plant-based alternatives, eliminating the need for time-consuming manual experimentation and testing in laboratories.
2Object-affected harmful factors
If plant-based alternatives are developed to replace animal-based ingredients, then health and environmental benefits are achieved, but matching taste and texture is difficult
Solution Approach 1:
The patent uses the AI system to analyze and match multiple parameters of chemical compounds including sensory properties, chemical structure, and functional characteristics. The machine learning model processes these parameters to identify plant-based compounds that replicate the sensory profile of animal-based ingredients, achieving precise matching of taste and texture while maintaining health and environmental benefits.
3Reliability
If expensive or hard-to-find chemical compounds from animal sources are used, then functional properties can be achieved, but cost and availability become issues
Solution Approach 1:
The patent employs the AI system to create virtual copies or representations of animal-based chemical compounds by analyzing their molecular structures and sensory properties. The machine learning model then identifies plant-based compounds that serve as substitutes with similar functional properties, making the ingredients more available and easier to manufacture while maintaining consistent functional performance in food products.
Data Source
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AI summary
Techniques to suggest alternative chemical compounds that can be used to recreate or mimic a target flavor using artificial intelligence are disclosed. A neural network based model is trained on source chemical compounds and their corresponding flavors and odors. The neural network-based model learns compound embeddings of the source chemical compounds and a target chemical compound of a food item. From the compound embeddings, one or more chemical compounds that are closest to the target chemical compound may be determined by a distance metric. Each suggested chemical compound is an alternative that can be used to recreate functional features of the target chemical compound.