Machine Learning and Chemometrics Applied to Food Control: New Trends and Challenges

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 2356

Special Issue Editors


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Guest Editor
Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, 11510 Puerto Real, Cádiz, Spain
Interests: food control; food adulteration; machine learning; chemometrics; honey; berries; peppers; onions; ion mobility spectroscopy; headspace - mass spectrometry; electronic nose; NIRs; FT-IR; HPLC; extraction techniques; green chemistry
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, 11510 Puerto Real, Cádiz, Spain
Interests: agri-food analysis; optimization; method validation; bioactive compounds; green extraction techniques; liquid chromatography; mass spectrometry
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, 11510 Puerto Real, Cádiz, Spain
Interests: agrifood analysis; optimization; method validation; bioactive compounds; green extraction techniques; liquid chromatography; mass spectrometry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food adulteration and authentication is becoming a major issue worldwide as a consequence of growing global trade. Food fraud is not only an illegal activity but can also cause health problems in consumers. Recently, analytical methods (NIRs, FT-IR, electronic noses, among others) based on the analyses of untargeted compounds or food fingerprinting are becoming very popular for food quality control, as they are usually faster, more ecofriendly and non-destructive. New advances in computer systems and data analysis have improved the possibilities of these methodologies, which usually generate large amounts of data. Therefore, the use of chemometric and machine learning algorithms are of great interest for the automation of and for developing predictive models in food quality control processes.

This Special Issue of Foods offers the opportunity to publish high-quality multidisciplinary research and reviews related to the most recent developments in food analytical methods and data analysis (chemometrics and machine learning) for food control, covering optimization and experimental design, data pre-processing strategies and classification and quantification models.

Prof. Dr. Marta Ferreiro-González
Dr. Mercedes Vazquez Espinosa
Dr. Ana V. González-de-Peredo
Guest Editors

Manuscript Submission Information

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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

  • food control
  • adulteration
  • machine learning
  • chemometrics
  • food fingerprint
  • analytical methods
  • data handling
  • sprectroscopic techniques
  • extraction techniques
  • green chemistry

Published Papers (2 papers)

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Research

17 pages, 1336 KiB  
Article
Effectiveness of Cyclic Voltammetry in Evaluation of the Synergistic Effect of Phenolic and Amino Acids Compounds on Antioxidant Activity: Optimization of Electrochemical Parameters
by María José Jara-Palacios, Emilio Begines, Francisco J. Heredia, María Luisa Escudero-Gilete and Dolores Hernanz
Foods 2024, 13(6), 906; https://doi.org/10.3390/foods13060906 - 16 Mar 2024
Viewed by 628
Abstract
Antioxidant activity can be evaluated using cyclic voltammetry (CV). The aim of this work is to verify the efficacy of CV in evaluating the synergistic effect of bioactive compounds, such as phenolic and amino acid compounds, on antioxidant activity. Therefore, three types of [...] Read more.
Antioxidant activity can be evaluated using cyclic voltammetry (CV). The aim of this work is to verify the efficacy of CV in evaluating the synergistic effect of bioactive compounds, such as phenolic and amino acid compounds, on antioxidant activity. Therefore, three types of model solutions were prepared: individual model solution (phenol and amino acid), (b) binary model solutions (phenol-phenol and amino acid-amino acid) and (c) mixed phenol–amino acid solutions. Electrochemical measurement conditions were optimized for phenolic compounds (pH 3.0, 1.0 g/L and 100 mV/s) and for amino acids (pH 7.0, 2.0 g/L for amino acids and 100 mV/s), and, for each solution, the functional groups responsible of the anodic and cathodic peaks were established. The peak anodic potential (Epa) and the onset potential (Eon) were two parameters of great importance. The first one was used to classify the solutions according to their antioxidant potential. In general, all the binary and mixed solutions had lower values of Epa than the corresponding individual model solution, which indicates an improvement in the antioxidant potential. The second one was used to evaluate the synergistic effects of phenolic compounds and amino acids. Full article
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16 pages, 2274 KiB  
Article
Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach
by Marta Barea-Sepúlveda, José Luis P. Calle, Marta Ferreiro-González and Miguel Palma
Foods 2023, 12(18), 3362; https://doi.org/10.3390/foods12183362 - 7 Sep 2023
Cited by 1 | Viewed by 1298
Abstract
Petroleum-derived waxes are used in the food industry as additives to provide texture and as coatings for foodstuffs such as fruits and cheeses. Therefore, food waxes are subject to strict quality controls to comply with regulations. In this research, a combination of visible [...] Read more.
Petroleum-derived waxes are used in the food industry as additives to provide texture and as coatings for foodstuffs such as fruits and cheeses. Therefore, food waxes are subject to strict quality controls to comply with regulations. In this research, a combination of visible and near-infrared (Vis-NIR) spectroscopy with machine learning was employed to effectively characterize two commonly marketed petroleum waxes of food interest: macrocrystalline and microcrystalline. The present study employed unsupervised machine learning algorithms like hierarchical cluster analysis (HCA) and principal component analysis (PCA) to differentiate the wax samples based on their chemical composition. Furthermore, nonparametric supervised machine learning algorithms, such as support vector machines (SVMs) and random forest (RF), were applied to the spectroscopic data for precise classification. Results from the HCA and PCA demonstrated a clear trend of grouping the wax samples according to their chemical composition. In combination with five-fold cross-validation (CV), the SVM models accurately classified all samples as either macrocrystalline or microcrystalline wax during the test phase. Similar high-performance outcomes were observed with RF models along with five-fold CV, enabling the identification of specific wavelengths that facilitate discrimination between the wax types, which also made it possible to select the wavelengths that allow discrimination of the samples to build the characteristic spectralprint of each type of petroleum wax. This research underscores the effectiveness of the proposed analytical method in providing fast, environmentally friendly, and cost-effective quality control for waxes. The approach offers a promising alternative to existing techniques, making it a viable option for automated quality assessment of waxes in food industrial applications. Full article
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