Hyperspectral Imaging and Other Nondestructive Methods for Analyzing Food Quality
A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".
Deadline for manuscript submissions: 22 November 2025 | Viewed by 529
Special Issue Editor
Special Issue Information
Dear Colleagues,
Hyperspectral imaging and other nondestructive methods are revolutionizing food quality analysis by enabling precise, real-time assessment without compromising product integrity. Hyperspectral imaging, which captures both spatial and spectral data, allows for detailed analysis of the chemical, physical, and biological properties of food.
Hyperspectral imaging and other nondestructive methods are particularly valuable for detecting contaminants, identifying damage or defects, detecting adulteration, distinguishing between varieties or quality grades, monitoring ripeness, assessing shelf life, and performing the quantitative analysis of food composition, all while preserving the sample for further use. Advanced machine learning and deep learning algorithms enhance the accuracy and efficiency of data interpretation, enabling rapid decision-making in quality control and supply chain management. By integrating these technologies, the food industry can ensure higher standards of safety, reduce waste, and meet consumer demands for transparency and sustainability.
Applications of hyperspectral imaging and nondestructive methods span across various stages of food production, from farm to table. They are used to optimize agricultural practices, improve processing efficiency, and ensure compliance with regulatory standards. This interdisciplinary approach not only enhances food quality but also drives innovation in the industry, paving the way for smarter, more sustainable food systems.
Dr. Jun Sun
Guest Editor
Manuscript Submission Information
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Keywords
- food quality
- nondestructive detection
- hyperspectral imaging
- spectral analysis
- sensory sensing
- chemometrics
- machine learning
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