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AI-Enabled Optical Sensing for Smart and Precision Food Drying: Techniques, Applications and Future Directions

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Abstract

Recent developments in alternative drying techniques have significantly heightened interest in innovative technologies that improve the yield and quality of dried goods, enhance energy efficiency, and facilitate continuous monitoring of drying processes. Artificial intelligence (AI)-enabled optical sensing technologies have emerged as promising tools for smart and precise monitoring of food drying processes. Food industries can leverage AI-enabled optical sensing technologies to gain a comprehensive understanding of drying dynamics, optimize process parameters, identify potential issues, and ensure product consistency and quality. This review systematically discusses the application of selected optical sensing technologies, such as near-infrared (NIR) spectroscopy, hyperspectral imaging, and conventional imaging (i.e., computer vision) powered by AI. After covering the basics of optical sensing technologies for smart drying and an overview of different drying methods, it explores various optical sensing techniques for monitoring and quality control of drying processes. Additionally, the review addresses the limitations of these optical sensing technologies and their prospects in smart drying.

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Acknowledgements

This study was financially supported by the Center for Advanced Research in Drying (CARD), a U.S. National Science Foundation Industry University Cooperative Research Center. CARD is located at Worcester Polytechnic Institute and the University of Illinois at Urbana-Champaign (co-site).

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Authors and Affiliations

  1. The Grainger College of Engineering, Department of Agricultural and Biological Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA

    Marcus Vinicius da Silva Ferreira, Md Wadud Ahmed, Marciano Oliveira, Amir Malvandi & Mohammed Kamruzzaman

  2. PepsiCo R&D, Plano, TX, 75024, USA

    Sanjay Sarang & Sheyla Ramsay

  3. BASF Corporation US, Beachwood, OH, 44122, USA

    Xue Liu

  4. Department of Biological Systems Engineering, Washington State University, Pullman, WA, 99164, USA

    Youngsoo Lee

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  1. Marcus Vinicius da Silva Ferreira
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  2. Md Wadud Ahmed
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  6. Xue Liu
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  9. Mohammed Kamruzzaman
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Contributions

M. K. and M.V. d. S. F. conceptualized and designed the manuscript. M.V. d. S. F. wrote the majority of the manuscript text and prepared figures, M.W. A. and M. O. wrote some parts. All authors reviewed, edited and approved the manuscript.

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Correspondence to Mohammed Kamruzzaman.

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da Silva Ferreira, M.V., Ahmed, M.W., Oliveira, M. et al. AI-Enabled Optical Sensing for Smart and Precision Food Drying: Techniques, Applications and Future Directions. Food Eng Rev 17, 75–103 (2025). https://doi.org/10.1007/s12393-024-09388-0

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