Application of NIR and Other Non-Destructive Technologies in Food Analysis
A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".
Deadline for manuscript submissions: 10 August 2026 | Viewed by 21
Special Issue Editor
Special Issue Information
Dear Colleagues,
Near-infrared spectroscopy and other non-destructive methods have revolutionized food analysis by enabling precise, real-time assessment without damaging the sample. Near-infrared spectroscopy, which rapidly quantifies key components through deep penetration, enables detailed analysis of the chemical, physical, and biological properties of food.
Near-infrared spectroscopy and other non-destructive methods are particularly valuable for detecting contaminants and diseases, identifying adulteration, distinguishing between varieties or quality grades, monitoring moisture content and ripeness, and performing quantitative analysis of food composition. 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. NIRS could also be successfully coupled with imaging techniques to provide comprehensive spatial–spectral information. By integrating these technologies, the food industry can ensure higher safety standards and safeguard consumer health.
Applications of near-infrared spectroscopy and non-destructive methods extend across the entire food production chain. They are used to optimize agricultural practices, improve processing efficiency, and ensure compliance with regulatory standards, collectively contributing to enhanced food quality, industry innovation, and more intelligent, sustainable food systems.
Dr. Xin Zhou
Guest Editor
Manuscript Submission Information
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Keywords
- food quality
- non-destructive detection
- near-infrared spectroscopy
- hyperspectral imaging
- laser-induced breakdown spectroscopy
- Raman spectroscopy
- spectral analysis
- chemometrics
- machine learning
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