Integration of machine learning technologies in food flavor research: Current applications, challenges, and future perspectives

Authors

  • Jing Ling School of Computer Science, Guangzhou Maritime University, Guangzhou 510725, China
  • Dennis R. Heldman Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA

DOI:

https://doi.org/10.25165/ijabe.v18i3.9623

Keywords:

machine learning, food flavor, prediction, flavor perception

Abstract

Flavor characteristics significantly influence consumer food preferences and purchasing behaviors, constituting a vital research domain in food science. While traditional flavor assessment approaches rely primarily on sensory evaluations and instrumental analyses, they face inherent limitations in processing large-scale datasets and generating comprehensive insights. Advanced machine learning (ML) models have revolutionized flavor research through their high-precision predictive capabilities, effectively addressing conventional methodological constraints. These computational approaches enable sophisticated and efficient flavor analysis by integrating multiple data dimensions, including chemical composition (volatile and non-volatile compounds), sensory attributes (taste, aroma, texture), temporal dynamics (flavor release patterns), and consumer responses. ML models demonstrate remarkable capability in simultaneously processing diverse data types, such as gas chromatography-mass spectrometry results, sensory panel evaluations, and real-time flavor release measurements, to predict consumer preferences and optimize flavor formulations. This review examines state-of-the-art ML applications in flavor science, emphasizing crucial areas such as flavor database development, intelligent sensory detection, and food traceability. Through systematic analysis of contemporary ML algorithms, this study critically evaluates their potential and limitations in decoding complex flavor dynamics, providing valuable insights for both researchers and industry practitioners while identifying promising directions for future technological innovations in food flavor analysis and prediction. The comprehensive synthesis presented here represents a significant contribution to the field by establishing a theoretical framework for ML-driven flavor research and offering practical guidelines for the implementation of computational approaches in food flavor analysis. Keywords: machine learning, food flavor, prediction, flavor perception DOI: 10.25165/j.ijabe.20251803.9623 Citation: Ling J, Heldman D R. Integration of machine learning technologies in food flavor research: Current applications, challenges, and future perspectives. Int J Agric & Biol Eng, 2025; 18(3): 1–11.

Author Biographies

Jing Ling, School of Computer Science, Guangzhou Maritime University, Guangzhou 510725, China

Dr. Jing Ling is an Associate Professor at Guangzhou Maritime University and an Adjunct Master's Supervisor at Ningxia University. Her research focuses on intelligent detection technology for agricultural products. She has served as the Principal Investigator for projects funded by the National Natural Science Foundation of China (Grant No. 61663039) and the General Program of Ningxia Natural Science Foundation (Grant No. NZ1648). Her research interests include non-destructive testing methods for grain quality and intelligent sensing technologies. She has published over 20 academic papers, including 5 papers indexed by SCI, and holds 4 national invention patents.

Dennis R. Heldman, Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA

Dr. Dennis R. Heldman is an Emeritus Professor in the Department of Food Science and Technology at The Ohio State University. He received his Ph.D. in Engineering from Michigan State University. His research focuses on food process engineering, particularly in food safety engineering, process design, food freezing systems, and sustainability in food manufacturing. Throughout his distinguished career, Dr. Heldman has held significant positions in both academia and industry. He served as Professor at the University of Missouri and Michigan State University, and held industry positions including Vice President at Campbell Soup Company and Director of Research at the National Food Processors Association. He has authored or co-authored several influential books in food engineering, including "Principles of Food Processing" and "Food Engineering Principles," which have become standard references in food engineering education. His research contributions span over 200 scientific publications, significantly advancing the field of food process engineering. Prof. Heldman's exceptional contributions have been recognized with prestigious awards, including the Nicholas Appert Award and the Life Achievement Award from the Institute of Food Technologists (IFT). He is a Fellow of both IFT and the American Society of Agricultural and Biological Engineers (ASABE). His leadership roles include serving as President of IFT and editor of the Journal of Food Science and Food Technology.

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Published

2025-06-30

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Ling, J., & Heldman, D. R. (2025). Integration of machine learning technologies in food flavor research: Current applications, challenges, and future perspectives. International Journal of Agricultural and Biological Engineering, 18(3), 1–11. https://doi.org/10.25165/ijabe.v18i3.9623

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Vol. 18 No. 3 (2025): IJABE

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