Traditional Chemometrics and Innovative Machine Learning Techniques as Tools to Assess Food Quality, Safety and Traceability

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Food Process Engineering".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 2415

Special Issue Editors


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Guest Editor
“Nello Carrara” Applied Physics Institute of the National Research Council of Italy (CNR-IFAC), 73100 Lecce, Italy
Interests: spectroscopy; remote sensing; fluorescence; statistics; machine learning

Special Issue Information

Dear Colleagues,

The chemometric analysis in the field of food allows one to process a large number of data and responses, to extract information on the authentication of geographical or varietal origin, food quality, chemical composition, or even to trace the adulteration of commodities with high added value.

In recent years, machine learning techniques have also found a wide range of uses in the food sector, offering valuable support to classical chemometric techniques for data analysis, but also for the assessment of food quality, traceability and safety.

The application of AI has led to the development of techniques that are more reliable, objective, cost-effective, non-destructive and less time-consuming than traditional methods available in the industry.

This Special Issue aims to collect high quality manuscripts related to the implementation of machine learning techniques, coupled with classical chemometric strategies in the food industry, to highlight the potential and applications of these efficient and non-invasive techniques.

Dr. Maria Tufariello
Dr. Lorenzo Palombi
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 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. Processes is an international peer-reviewed open access monthly 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.

Keywords

  • chemometric analysis
  • machine learning techniques
  • artificial intelligence
  • food quality
  • safety
  • traceability

Published Papers (1 paper)

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Research

11 pages, 2234 KiB  
Article
Parameter Visualization of Benchtop Nuclear Magnetic Resonance Spectra toward Food Process Monitoring
by Koki Hara, Shunji Yamada, Eisuke Chikayama and Jun Kikuchi
Processes 2022, 10(7), 1264; https://doi.org/10.3390/pr10071264 - 27 Jun 2022
Cited by 2 | Viewed by 1765
Abstract
Low-cost and user-friendly benchtop low-field nuclear magnetic resonance (NMR) spectrometers are typically used to monitor food processes in the food industry. Because of excessive spectral overlap, it is difficult to characterize food mixtures using low-field NMR spectroscopy. In addition, for standard compounds, low-field [...] Read more.
Low-cost and user-friendly benchtop low-field nuclear magnetic resonance (NMR) spectrometers are typically used to monitor food processes in the food industry. Because of excessive spectral overlap, it is difficult to characterize food mixtures using low-field NMR spectroscopy. In addition, for standard compounds, low-field benchtop NMR data are typically unavailable compared to high-field NMR data, which have been accumulated and are reusable in public databases. This work focused on NMR parameter visualization of the chemical structure and mobility of mixtures and the use of high-field NMR data to analyze benchtop NMR data to characterize food process samples. We developed a tool to easily process benchtop NMR data and obtain chemical shifts and T2 relaxation times of peaks, as well as transform high-field NMR data into low-field NMR data. Line broadening and time–frequency analysis methods were adopted for data processing. This tool can visualize NMR parameters to characterize changes in the components and mobilities of food process samples using benchtop NMR data. In addition, assignment errors were smaller when the spectra of standard compounds were identified by transferring the high-field NMR data to low-field NMR data rather than directly using experimentally obtained low-field NMR spectra. Full article
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