Advances of Chemometrics and Artificial-Intelligence-Based Approaches to Food Analysis
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 4406
Special Issue Editors
Interests: computational intelligent systems; signal & image processing; system identification & control; energy forecasting systems; robotics; chemometrics; biomedicine; food safety/quality; traffic prediction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The quality and safety of food is an important issue for all of society, since it is at the basis of human health, social development and stability. Conventional methods for the detection of food quality are laborious, tedious, destructive, and time-consuming. Non-destructive methods are advancements in food quality evaluation that are useful for obtaining quantitative and qualitative data without destruction of the sample. These non-destructive methods include, among others, imaging-based (hyperspectral, multispectral, fluorescence, backscattering, magnetic resonance) approaches, spectroscopy-based (NIR, FTIR, Raman, terahertz) approaches, as well as electronic nose, electronic tongue, and dielectric-based approaches. Thus, the concept of food “sensing”-based analysis has attracted widespread attention and has found applicability to a variety of food “commodities” such as fruits and vegetables, meat, seafood, oil, dairy-based, and egg products. Although it can generally be grouped into prediction and classification problems, food analysis is associated with important problems such as quality detection (e.g., defects, spoilage, freshness, fatty acid composition), food contamination (e.g., detection of pathogens/parasites and harmful compounds), and authentication/adulteration issues.
In recent years, the combination of analytical tools and data-science-based algorithms has attracted the attention of researchers in the field of food analysis. However, to combat challenges in food integrity (i.e., quality, safety, authenticity), more sophisticated data-science tools need to be developed, tailored, or even integrated with innovative analytical tools to generate an effective analytical workflow, fully extract the underlying information, provide a better understanding of the results, and improve the overall performance of the analytical techniques for safeguarding food integrity.
This Special Issue thus aims to collect high-quality manuscripts related to the implementation of advanced artificial-intelligence-based techniques, coupled with classical chemometric strategies used in food analysis, to solve issues related to food integrity.
Dr. Vassilis Kodogiannis
Dr. Dedy Rahman Wijaya
Guest Editors
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Keywords
- spectroscopic techniques (UV-VIS, NIR, Raman, NMR, fluorescence, etc.)
- electronic nose and tongue
- imaging methods (digital, hyperspectral, etc.)
- advancement in data pre-processing methods (de-noising, etc.)
- advancement in feature selection and extraction methods
- machine learning techniques for food analysis
- multivariate methods for food analysis
- computational intelligence systems for food analysis (neural networks, fuzzy logic, genetic algorithms)
- data fusion in food analysis
- deep learning systems for food analysis
- advancement in ensemble systems
- application to food integrity (quality, safety, and authenticity)
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