NIR Sensing for Food Analysis and Process Control
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".
Deadline for manuscript submissions: 20 July 2026 | Viewed by 4
Special Issue Editor
Interests: near infrared spectroscopy; chemometrics; modelling; optimization; extraction; food quality
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
I can announce a Special Issue of Sensors entitled “NIR Sensing for Food Analysis and Process Control”.
In recent years, Near-Infrared Spectroscopy (NIR) has emerged as a powerful, non-destructive analytical technique within the food sector. This Special Issue will gather the latest advances in applying NIR for both comprehensive food analysis and dynamic process control. NIR technology offers significant advantages compared to traditional analytical methods including real-time and in situ measurements, making it appropriate for monitoring complex food matrices and ensuring quality and safety across the supply chain.
We invite innovative submissions focusing on the latest developments in and applications of NIR sensing, in combination with chemometric tools and machine learning, to enhance model robustness and predictive accuracy.
Topics of interest include, but are not limited to, on-line and at-line monitoring of fermentation, drying, and mixing processes; detection and quantification of quality parameters (e.g., moisture, protein, fat, sugar, volatile compounds); rapid screening for contaminants and adulterants; and the development of miniaturized, portable NIR devices for field and consumer applications. This Special Issue will highlight innovative research that translates fundamental NIR sensing principles into practical, scalable solutions for modern food production and quality assurance.
Dr. Tamara Jurina
Guest Editor
Manuscript Submission Information
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Keywords
- NIR spectroscopy
- process control
- food production
- quality control
- portable NIRs
- chemometrics
- machine learning
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