Application of Non-Invasive Spectral and Multi-Spectral Sensors 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: 20 September 2024 | Viewed by 125
Special Issue Editors
Interests: fluorescence spectroscopy; chemometrics; dairy products; infrared and fluorescence spectrometry; analytical chemistry; image analysis
Special Issues, Collections and Topics in MDPI journals
Interests: food sustainability; food waste valorization; food industry 4.0 technologies; emerging food trends; food processing and analytical techniques
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Food products must be safe and authentic when delivered to consumers a goal that requires the development of reliable and robust methods for the measurement of food quality, safety, and authenticity. In general, it is time-consuming, destructive, and challenging to investigate the stability of food components using traditional analytical techniques such as chemistry- and biochemistry-based approaches. Therefore, various types of sensors that can identify food components and determine the changes that took place during processing and storage have been developed over the last few years. Among the proposed promising solutions, spectroscopy-based analytical sensors have shown good potential, enabling noninvasive, quick, and easy measurements in a variety of spectral wavelength regions, including visible/ultraviolet, mid-infrared, near-infrared, Raman, and fluorescence. Each spectral range is associated with specific physical and chemical information, making it possible to monitor the quality of the food product during processing and/or storage, and to predict its final quality. Moreover, the combination of different spectral ranges can enhance the accuracy and robustness of sensors, leading to better precision in process monitoring and quality control.
The aim of this Special Issue is to gather together the latest research, trends, and developments in spectroscopy and multi-spectroscopy sensors combined with chemometrics, particularly artificial intelligence and other advanced data analysis techniques, to authenticate food, monitor food processing, and predict quality changes during storage as well as final product quality.
In this Special Issue, original papers and review articles are welcomed.
Prof. Dr. Abderrahmane Aït-Kaddour
Dr. Abdo Hassoun
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- sensors
- spectroscopy
- hyperspectral imaging
- multispectral imaging
- food products
- food quality
- food processing
- advanced data analysis techniques
