Novel Applications of Machine Learning Combine with Chemometrics in Food Quality Assessment
A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 151
Special Issue Editors
Interests: machine learning; data science; food traceability; food adulteration
2. Research Institute in Food Nutrition and Food Safety, Universitat de Barcelona, Av. Prat de la Riba 171, Edifici Recerca (Gaudí), E-08921 Santa Coloma de Gramenet, Spain
Interests: food authentication; food characterization; food classification; food fraud identification; secondary metabolites; polyphenols; foodomics; bioactive compounds; liquid chromatography; mass spectrometry; high resolution mass spectrometry; ambient mass spectrometry; capillary electrophoresis; chemometrics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The classification of food samples based on their chemical composition can provide useful information for various purposes, such as determining geographical origin and authenticity, product characteristics, quality control for companies, preservation, and product differentiation. Analysis of ingredients like vitamins, minerals, enzymes, food additives, flavors, and colors can reveal insights into food quality and health impacts on consumers. One classification problem is the authentication of organic foods.
Certification labels indicating country of origin or food type add market value to products, making the authentication process relevant for preventing potential fraud. Since differences between food types and origins are not usually visible, systematic verification of authenticity and traceability relies on chemical analysis. Certification requires several steps that considerably increase product value. Methodologies for authenticating food type or origin are of great interest.
Dr. Rommel Melgaço Barbosa
Prof. Dr. Oscar Núñez
Guest Editors
Manuscript Submission Information
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Keywords
- pattern recognition
- multivariate analysis
- machine learning
- chemometrics
- classification models
- food safety
- food traceability
- food adulteration
- food quality
