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A Novel Machine Learning–Based Approach for Characterising the Micromechanical Properties of Food Material During Drying

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Abstract

Plant-based food materials (PBFMs) such as fruits and vegetables contain various irregular cellular compartments. Like other engineering materials, the characterisation of micromechanical properties (MMPs) of PBFMs is intensely important for accurately estimating the functionality of dried food products. The application of a machine learning (ML)–based approach to characterise the MMPs is a promising idea. However, no intensive research in this regard has been attempted yet. Therefore, we proposed an ML-based modelling framework to characterise the MMPs of PBFMs during drying. A feed-forward artificial neural network (ANN) model with a backpropagation algorithm was developed and optimised with a genetic algorithm (GA)–based optimisation tool for characterising PBFMs, specifically carrots. Moreover, the accuracy of the ANN model was compared with a multiple nonlinear regression (MNLR) model. It was found that the developed network model agreed very well with the experimental data when predicting the elastic modulus, stiffness and hardness, with an accuracy of the goodness of fit (R2) values of 0.992, 0.993 and 0.802, respectively. It is expected that the developed model has incredible potential to characterise the MMPs of similar food products.

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Data Availability

All the weights and biases of the model and the MATLAB code developed by the author can be obtained from the corresponding author upon request.

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Acknowledgements

The authors would like to thank the Energy & Drying Research Group at the Queensland University of Technology for supporting the drying experiments. We would also like to acknowledge Mr Nishane Patel and the Central Analytical Research Facility at the Queensland University of Technology for their support with the nano-indentation experimentation.

Funding

This research work was partially funded by the Australian Research Council (ARC) Discovery Grant (Grant ID: DP180103009).

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Authors and Affiliations

  1. School of Mechanical, Medical and Process Engineering, Queensland University of Technology (QUT), 2 George St, Brisbane, QLD, 4000, Australia

    M. Imran. H. Khan & YuanTong Gu

  2. School of Computer Science, Queensland University of Technology (QUT), 2 George St, Brisbane, QLD, 4000, Australia

    Duval Longa

  3. Department of Biological Systems Engineering, Washington State University, P.O. Box 646120, Pullman, WA, 99164-6120, USA

    Shyam S. Sablani

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  1. M. Imran. H. Khan
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Contributions

Md Imran H. Khan, conceptualization, methodology, data analysis, writing–review and editing; Duval longa, methodology, writing–review and editing; Shyam S. Sablani, methodology, writing–review and editing; YuanTong Gu, conceptualization; funding acquisition, resources supervision, writing–review and editing.

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Correspondence to M. Imran. H. Khan.

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Khan, M.I.H., Longa, D., Sablani, S.S. et al. A Novel Machine Learning–Based Approach for Characterising the Micromechanical Properties of Food Material During Drying. Food Bioprocess Technol 16, 420–433 (2023). https://doi.org/10.1007/s11947-022-02945-7

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