Chemometric Analysis of Food: Spectroscopic Techniques, Authenticity Identification and Food Applications

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 2404

Special Issue Editors


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Guest Editor
1. Department of Life Science, School of Science, Atlantic Technological University, Ash Lane, Ballinode, F91 YW50 Sligo, Ireland
2. Cameron Forensic Medical Sciences, Centre for Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
Interests: mass spectrometry; method validation; toxicology; food safety; laboratory accreditation; food fraud; customs; analytical chemistry; forensic science
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Chemical-Biological Sciences Department, Autonomous University of Ciudad Juarez, Ciudad Juarez, Mexico
Interests: functional foods development; identification and quantification of bioactive compounds by spectroscopic and chromatographic techniques; studies of interactions between biomolecules

Special Issue Information

Dear Colleagues,

Special attention by consumers towards food quality/labelling of food products has soared in recent years. The authentication and traceability of food attracts more interest internationally due to increasing consumer awareness regarding health and nutrition. Consequently, in the food industry, traceability systems, although in place, require approaches to address the problem of food authenticity to ensure food product quality is adhered to. It is also vital to identify if intentional or unintentional food fraud has occurred. In addition, any alleged declarations on a label of a food product must always be checked to verify its accuracy. Mounting competitiveness in the global food sector has, in turn, led to the presence of a large variety of duplicate products on the global market, whose global quality characteristics are, nevertheless, not the same.  

There is a need for innovative analytical approaches in the food safety field to authenticate the food quality of products for consumers. The evaluation of spectroscopy tools is rapidly gaining ground at all levels in food authenticity analyses. Chemometrics can be deemed an essential approach for the differentiation of similar samples that require authentication by authorities. For instance, recent advances through the dissemination of ‘fingerprinting strategies’ have enabled the study of many factors that influence food identity. It is worth noting that in authenticity studies, there are a wide range of areas to consider, such as chemical constituents, freshness, toxic substances, microorganisms and adulteration monitoring. In addition, providing evidence of the traceability of food variety and origin is critical. 

This Special Issue invites authors to submit articles that focus on spectroscopy and chemometrics in an area of food authenticity that may focus on the topics below (or authenticity studies on additional topics not listed). 

  • Identification of products in accordance with the associated species;  
  • Farming system approaches (conventional or organic; intensive, semi-intensive, or extensive); 
  • Geographical origin (countries of source); 
  • Production methods (wild or farmed); 
  • Processing techniques (fresh or fresh/thawed). 

Dr. Geraldine Dowling
Prof. Dr. Emilio Alvarez-Parrilla
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

  • traceability
  • food authentication
  • chemometrics
  • fingerprinting
  • vibrational spectroscopy
  • absorption/fluorescence spectroscopy
  • nuclear magnetic resonance
  • hyperspectral imaging
  • geographical origin
  • food fraud

Published Papers (2 papers)

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Research

14 pages, 2059 KiB  
Article
Hyperspectral Imaging Using a Convolutional Neural Network with Transformer for the Soluble Solid Content and pH Prediction of Cherry Tomatoes
by Hengnian Qi, Hongyang Li, Liping Chen, Fengnong Chen, Jiahao Luo and Chu Zhang
Foods 2024, 13(2), 251; https://doi.org/10.3390/foods13020251 - 12 Jan 2024
Viewed by 997
Abstract
Cherry tomatoes are cultivated worldwide and favored by consumers of different ages. The soluble solid content (SSC) and pH are two of the most important quality attributes of cherry tomatoes. The rapid and non-destructive measurement of the SSC and pH of cherry tomatoes [...] Read more.
Cherry tomatoes are cultivated worldwide and favored by consumers of different ages. The soluble solid content (SSC) and pH are two of the most important quality attributes of cherry tomatoes. The rapid and non-destructive measurement of the SSC and pH of cherry tomatoes is of great significance to their production and consumption. In this research, hyperspectral imaging combined with a convolutional neural network with Transformer (CNN-Transformer) was utilized to analyze the SSC and pH of cherry tomatoes. Conventional machine learning and deep learning models were established for the determination of the SSC and pH. The findings demonstrated that CNN-Transformer yielded outstanding results in predicting the SSC, with the coefficient of determination of calibration (R2C), validation (R2V), and prediction (R2P) for the SSC being 0.83, 0.87, and 0.83, respectively. Relatively worse results were obtained for the pH value prediction, with R2C, R2V, and R2P values of 0.74, 0.68, and 0.60, respectively. Furthermore, the visualization of the CNN-Transformer model revealed the wavelength weight distributions, indicating that the 1380–1650 nm range served as the characteristic band for the SSC, while the spectral range at 945–1280 nm was the characteristic band for pH. In conclusion, integrating spectral information features with the attention mechanism of Transformer through a convolutional neural network can enhance the accuracy of predicting the SSC and pH for cherry tomatoes. Full article
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14 pages, 3343 KiB  
Article
A Novel Variable Selection Method Based on Ordered Predictors Selection and Successive Projections Algorithm for Predicting Gastrodin Content in Fresh Gastrodia elata Using Fourier Transform Near-Infrared Spectroscopy and Chemometrics
by Zhenjie Wang, Changzhou Zuo, Min Chen, Jin Song, Kang Tu, Weijie Lan, Chunyang Li and Leiqing Pan
Foods 2023, 12(24), 4435; https://doi.org/10.3390/foods12244435 - 11 Dec 2023
Viewed by 841
Abstract
Gastrodin is one of the most important biologically active components of Gastrodia elata, which has many health benefits as a dietary and health food supplement. However, gastrodin measurement traditionally relies on laboratory and sophisticated instruments. This research was aimed at developing a [...] Read more.
Gastrodin is one of the most important biologically active components of Gastrodia elata, which has many health benefits as a dietary and health food supplement. However, gastrodin measurement traditionally relies on laboratory and sophisticated instruments. This research was aimed at developing a rapid and non-destructive method based on Fourier transform near infrared (FT-NIR) to predict gastrodin content in fresh Gastrodia elata. Auto-ordered predictors selection (autoOPS) and successive projections algorithm (SPA) were applied to select the most informative variables related to gastrodin content. Based on that, partial least squares regression (PLSR) and multiple linear regression (MLR) models were compared. The autoOPS-SPA-MLR model showed the best prediction performances, with the determination coefficient of prediction (Rp2), ratio performance deviation (RPD) and range error ratio (RER) values of 0.9712, 5.83 and 27.65, respectively. Consequently, these results indicated that FT-NIRS technique combined with chemometrics could be an efficient tool to rapidly quantify gastrodin in Gastrodia elata and thus facilitate quality control of Gastrodia elata. Full article
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