Special Issue "Non-destructive Sensors and Machine Learning for Food Safety & Quality Inspection"
Deadline for manuscript submissions: 31 December 2020.
Interests: neural networks; fuzzy systems; genetic algorithms; hybrid systems; machine learning; image/signal processing; bio-signal analysis; chemometrics; control; non-invasive sensing systems; robotics
Special Issues and Collections in MDPI journals
Food is routinely screened to assess quality (such as physical appearance and organoleptic properties) and safety (absence of health threatening pathogens and chemical compounds). These tests are usually carried out in laboratory by skilled personnel, thus resulting in delayed response and high costs for the analysis. No longer restricted to detailed laboratory analyses or simplified implementation in industrial or commercial settings, non-invasive sensing methods can now accommodate non-destructive, comprehensive, high-resolution spectral and image analyses for real-world safety and quality inspection on rapid food-processing lines.
In this context, analytical techniques, such as spectroscopy (UV-Vis, NIR, Raman, NMR, fluorescence, ultrasound, etc.), electronic nose, electronic tongue, nano-sensors and imaging (digital, hyperspectral, multispectral) play a key role. These techniques offer the possibility of simultaneously determining a high number of compounds or features, the so-called “fingerprint,” analyzing samples in a non-destructive, easy, quick, and direct way with minimal sample preparation. The resulting datasets are usually high dimensional and complex, requiring methods of pattern recognition or predictive analysis to extract important information. This special issue welcomes applications, high-quality articles on the application of non-invasive methods and machine learning based techniques to analyse or monitor composition, adulteration, quality and authentication issues in a diverse range of food (such as meats, fish, fruits, vegetables, oil, wines and dairy) products.
Dr. Vassilis S. Kodogiannis
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 papers will be 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. Sensors 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 2000 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.
Topics of this special issue will include, but are not limited to:
- Spectroscopic techniques (UV-VIS, NIR, Raman, NMR, fluorescence, etc)
- Electronic nose and tongue
- Imaging methods (digital, hyperspectral, etc.)
- Fusion of multiple sensors applied to Food Analysis
- Machine learning techniques for Food Quality Inspection
- Deep learning for Automated Food Inspection
- Detection of food adulteration using Deep and Ensemble Learning
- Feature selection and extraction methods to improve classiffication tasks
- Food authentication, adulteration
- Food Quality evalution (incl. spoilage, freshness)
- Food composition