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Future Food Analysis and Detection - 2nd Volume
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Department of Physical and Chemical Sciences, University of L’Aquila, Via Vetoio Coppito, 67100 L’Aquila, Italy
Department of Chemistry, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
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Future Food Analysis and Detection - 2nd Volume

Abstract submission deadline
30 June 2023
Manuscript submission deadline
30 September 2023
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Topic Information

Dear Colleagues,

The demographic increase, the demand for food, and the impact of production on the environment have always been some of the main issues which affect society on a global scale. Recently, a greater awareness of these issues has contributed to the search for food alternatives which can meet the needs of the world population. Science needs to keep up with the times, and new tools for the analysis of traditional and novel food products are needed. In particular, given the abovementioned premises, it is clear that developing new approaches for the authentication and characterization of novel foods, as well as the improvement of methods for the prevention of food fraud, are of utmost importance.

In the light of this, the present topic aims at collecting original research works, where new analytical/chemometric approaches in the field of food analysis are proposed. In particular, papers related to the authentication, characterization, and traceability of foods, describing novel approaches for food detection or to assess the geographical origin of commodities, as well as papers addressing the resolution of problems in the field of food analysis are very welcome. Moreover, given the close link between these subjects and chemometrics, the submission of articles discussing the development of new chemometric approaches that can be applied to the resolution of food analytical problems is also encouraged. Finally, to provide a complete overview of recent trends in this area, reviews in this framework will also be gladly received.

Dr. Alessandra Biancolillo
Prof. Dr. Federico Marini
Topic Editors

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.838 3.7 2011 14.9 Days 2300 CHF Submit
AppliedChem
appliedchem 		- - 2021 15.0 days * 1000 CHF Submit
Foods
foods 		5.561 4.1 2012 15.8 Days 2400 CHF Submit
Methods and Protocols
mps 		- 3.0 2018 26.3 Days 1600 CHF Submit
Molecules
molecules 		4.927 5.9 1996 13.4 Days 2300 CHF Submit

* Median value for all MDPI journals in the second half of 2022.


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Published Papers (1 paper)

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Article
E-Eye-Based Approach for Traceability and Annuality Compliance of Lentils
by
Martina Foschi
,
Valerio Di Maria
,
Angelo Antonio D’Archivio
,
Federico Marini
and
Alessandra Biancolillo
Appl. Sci. 2023, 13(3), 1433; https://doi.org/10.3390/app13031433 - 21 Jan 2023
Viewed by 438
Abstract
In recent years, thanks to their numerous nutritional benefits, legumes have been rediscovered and have attracted interest from many consumers. However, these products, the most valuable ones traditionally produced in smaller communities in particular, can be objects of fraud; this is the case [...] Read more.
In recent years, thanks to their numerous nutritional benefits, legumes have been rediscovered and have attracted interest from many consumers. However, these products, the most valuable ones traditionally produced in smaller communities in particular, can be objects of fraud; this is the case of Italian lentils, which, being a dry product, have a fairly long shelf life, but, due to the minimal visual changes that can affect them, it is possible that expired lentils may be sold alongside edible ones. The present work aims at creating a non-destructive method for classifying Italian lentils according to their harvest year and origin, and for discriminating between expired and edible ones. In order to achieve this goal, Red-Green-Blue (RGB) imaging, which could be considered as a sort of e-eye and represents a cutting-edge, rapid, and effective analytical method, was used in combination with a discriminant classifier (Sequential Preprocessing through ORThogonalization-Linear Discriminant Analysis, SPORT-LDA) to create novel testing models. The SPORT-LDA models built to discriminate the different geographical origins provided an average correct classification rate on the test set of about 88%, whereas an overall 90% accuracy was obtained (on the test samples) by the SPORT-LDA model built to recognize whether a sample was still within its expiry date or not. Full article
(This article belongs to the Topic Future Food Analysis and Detection - 2nd Volume)
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