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 175

Special Issue Editors


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Guest Editor
Instituto de Informática, Alameda Palmeiras, Quadra D, Campus Samambaia, Universidade Federal de Goiás, Goiânia 74690-900, GO, Brazil
Interests: machine learning; data science; food traceability; food adulteration

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Guest Editor
1. Department of Chemical Engineering and Analytical Chemistry, Faculty of Chemistry, University of Barcelona, Martí i Franquès 1-11, E-08028 Barcelona, Spain
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
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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

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

  • pattern recognition
  • multivariate analysis
  • machine learning
  • chemometrics
  • classification models
  • food safety
  • food traceability
  • food adulteration
  • food quality

Published Papers

This special issue is now open for submission.
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