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Machine learning approach in predicting GlutoPeak test parameters from image data with AutoML and transfer learning.

Takehiro Murai¹, Yoshitaka Inoue², Assey Nambirige¹

¹Department of Food Science and Nutrition, College of Food, Agricultural and Natural Resource Sciences, University of Minnesota, 1334 Eckles Avenue, St. Paul, MN, 55108, USA.

|October 4, 2023

Summary

This study uses machine learning and image analysis to predict GlutoPeak test results for wheat gluten quality. This approach offers a faster, more cost-effective method for evaluating flour properties in the baking industry.

Keywords:

AutoML Convolutional neural network Gluten GlutoPeak test Machine learning Transfer learning

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Area of Science:

Agricultural Science
Computer Science
Food Science

Background:

The GlutoPeak test evaluates flour properties, specifically gluten strength and elasticity, crucial for baking.
Current methods for assessing gluten quality can be time-consuming and costly.
Developing efficient, automated methods for gluten property evaluation is essential for the baking industry.

Purpose of the Study:

To introduce a novel machine learning methodology for predicting GlutoPeak test parameters using image data.
To develop an efficient and cost-effective technique for quantifying wheat gluten properties.
To explore the application of deep learning and transfer learning for gluten quality assessment.

Main Methods:

Utilized AutoKeras for automated neural architecture search and hyperparameter tuning.
Employed convolutional neural network (CNN) models, specifically ResNet101, with transfer learning.
Trained models using image data to predict GlutoPeak test results.
Evaluated model performance using Adam and SGD optimizers for 2-class and 4-class predictions.

Main Results:

The ResNet101 model with the Adam optimizer achieved the highest accuracy of 0.5765 in 2-class prediction.
The ResNet101 model with the SGD optimizer reached an accuracy of 0.4362 in 4-class prediction.
Demonstrated the feasibility of predicting GlutoPeak parameters from image data using machine learning.

Conclusions:

Machine learning and deep learning techniques show significant potential for predicting GlutoPeak test parameters from image data.
This AI-driven approach offers a faster and more economical alternative for evaluating wheat gluten quality.
The findings pave the way for improved quality control in the wheat and baking industries.