Innovative Research Award
Hye-Won Jung
Adelaide MRI, Australia
Hye-Won Jung is a researcher affiliated with AdelaideMRI whose scholarly activities encompass food safety, quality control, hyperspectral imaging, artificial intelligence, computer vision, and nondestructive analytical technologies. The available Scopus metrics indicate a developing research profile comprising ten indexed publications, 225 citations, and an h-index of 7. The research portfolio demonstrates interdisciplinary collaboration across food science, biomedical imaging, machine learning, and optical sensing, with contributions to rapid contaminant detection and advanced image analysis methods. These achievements provide a strong basis for recognition within the International Food Scientist Awards while reflecting continued engagement with innovative analytical technologies.[1]
Abstract
The research activities of Hye-Won Jung focus on the integration of advanced imaging technologies, artificial intelligence, and chemometric analysis for food quality assessment and biomedical image interpretation. Published studies demonstrate expertise in hyperspectral imaging, optical spectroscopy, deep learning, and computer vision for nondestructive inspection systems. Recent investigations further extend these analytical methods into automated clinical image interpretation using attention-gated deep learning frameworks, illustrating broad interdisciplinary capability across food safety and medical imaging.[2]
Keywords
Food Safety, Quality Control, Hyperspectral Imaging, Artificial Intelligence, Deep Learning, Chemometrics, Optical Imaging, Computer Vision
Introduction
Modern food safety increasingly depends on rapid, nondestructive analytical technologies capable of detecting contaminants while preserving sample integrity. Hye-Won Jung has contributed to this evolving field through research involving hyperspectral imaging, optical spectroscopy, chemometric modelling, and machine learning approaches that improve contaminant detection and product quality evaluation. The interdisciplinary nature of these investigations aligns with international priorities for safer food systems and intelligent analytical technologies.[3]
Research Profile
The research profile combines expertise in food science, biomedical engineering, computer vision, and artificial intelligence. Published work spans peer-reviewed journals including LWT, Comprehensive Reviews in Food Science and Food Safety, Pattern Recognition, and Applied Sciences. Research emphasizes practical analytical solutions that improve diagnostic accuracy, contaminant screening, and automated image interpretation while supporting scientific reproducibility and technological innovation.[2]
Research Contributions
- Development of SWIR hyperspectral imaging methods for rapid detection of aflatoxin B1 in almonds.
- Comprehensive reviews of optical imaging technologies for nondestructive mycotoxin detection.
- Machine learning and attention-gated deep learning applications in medical image analysis.
- Computer vision algorithms for particle tracking in synchrotron X-ray imaging.
- Interdisciplinary integration of food safety analytics with advanced computational methods.
Publications
- Advanced Clinical Analysis Based on Automated Cardiothoracic Ratio Estimation Using Attention-Gated Deep Learning. Applied Sciences, 2026. DOI: 10.3390/app16147019
- Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of Aflatoxin B1 in single kernel almonds. LWT, 2022. DOI: 10.1016/j.lwt.2021.112954
- Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts. Comprehensive Reviews in Food Science and Food Safety, 2021. DOI: 10.1111/1541-4337.12801
- Multiple particle tracking in time-lapse synchrotron X-ray images using discriminative appearance and neighbouring topology learning. Pattern Recognition, 2019. DOI: 10.1016/j.patcog.2019.05.007
- Multiple mucociliary transit marker tracking in synchrotron X-ray images using the global nearest neighbor method. IEEE EMBC, 2017. DOI: 10.1109/EMBC.2017.8037200
Research Impact
Based on the available Scopus profile, the researcher has accumulated 225 citations with an h-index of 7 across ten indexed publications. Citation performance reflects meaningful scholarly influence within interdisciplinary research involving food safety, imaging science, chemometrics, and computational analysis. The body of work has contributed to improved methodologies for contaminant detection, optical sensing, and intelligent image processing, supporting continued scientific advancement.[1]
Award Suitability
The available publication record demonstrates sustained contributions to innovative analytical technologies applicable to food safety and quality control. The integration of hyperspectral imaging, chemometric modelling, artificial intelligence, and optical sensing reflects research that addresses contemporary scientific and industrial challenges. These achievements indicate strong suitability for recognition under the Innovative Research Award category of the International Food Scientist Awards, particularly for interdisciplinary research advancing nondestructive food analysis and intelligent imaging technologies.[3]
Conclusion
Hye-Won Jung has established an interdisciplinary research portfolio spanning food science, biomedical imaging, computer vision, and artificial intelligence. Through peer-reviewed publications and measurable citation impact, the researcher has contributed to the advancement of nondestructive analytical technologies and intelligent image analysis. The documented scholarly achievements provide a well-supported academic profile suitable for international professional recognition.
External Links
References
- Elsevier. (n.d.). Scopus Author Details: Hye-Won Jung, Author ID 57037522300. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=57037522300
- Jung, H.-W., et al. (2026). Advanced Clinical Analysis Based on Automated Cardiothoracic Ratio Estimation Using Attention-Gated Deep Learning. Applied Sciences.
DOI: https://doi.org/10.3390/app16147019
- Jung, H.-W., et al. (2022). Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of Aflatoxin B1 in single kernel almonds. LWT. DOI: https://doi.org/10.1016/j.lwt.2021.112954
- Jung, H.-W., et al. (2021). Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts. Comprehensive Reviews in Food Science and Food Safety. DOI: https://doi.org/10.1111/1541-4337.12801
- Jung, H.-W., et al. (2019). Multiple particle tracking in time-lapse synchrotron X-ray images using discriminative appearance and neighbouring topology learning. Pattern Recognition.
DOI: https://doi.org/10.1016/j.patcog.2019.05.007