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High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

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Quantifying techno-functional properties of ingredients from multiple crops using machine learning.

Anouk Lie-Piang¹, Jos Hageman², Iris Vreenegoor¹

¹Food Process Engineering, Wageningen University, P.O. Box 17, 6700 AA, Wageningen, the Netherlands.

Current Research in Food Science

|October 12, 2023

Summary

This summary is machine-generated.

Formulating food products requires understanding techno-functional properties, not just composition. This study shows models can predict these properties for ingredient blends from multiple crops like yellow pea and lupine, though with some accuracy trade-offs.

Keywords:

Food formulation Food ingredients Machine learning Mild fractionation Techno-functional properties

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Area of Science:

Food Science and Technology
Plant-based Ingredients
Ingredient Functionality

Background:

Minimally processed food ingredients contain complex compositions, necessitating formulation based on techno-functional properties.
Understanding ingredient functionality is crucial for developing novel food products from diverse plant sources.

Purpose of the Study:

To assess the feasibility of quantifying techno-functional properties (gelation, viscosity, emulsion stability, foaming capacity) of ingredient blends from multiple crops.
To compare predictive models for ingredient functionality based on single crops versus multi-crop blends.
To explore advanced modeling techniques for predicting ingredient behavior.

Main Methods:

Quantification of gelation, viscosity, emulsion stability, and foaming capacity for yellow pea and lupine ingredients.
Application of spline regression, random forest, and neural network models to predict techno-functional properties.
Selection of optimal models based on accuracy and physical feasibility of predictions.

Main Results:

A unified model was developed to predict each techno-functional property for both yellow pea and lupine blends.
Models using multi-crop data showed higher prediction errors compared to single-crop specific models.
Analysis indicated potential for reducing dataset sizes for certain properties without significant information loss.

Conclusions:

Techno-functional properties of ingredient blends can be modeled across different crops, offering a generalized approach to food formulation.
While a single model for multiple crops is achievable, it involves a compromise in predictive accuracy.
Further research can optimize data requirements for robust predictive modeling of plant-based ingredient functionality.