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Applications of Spectroscopy and Imaging Techniques in Food Quality and...
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Applications of Spectroscopy and Imaging Techniques in Food Quality and Safety

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 4966

Special Issue Editors

Dr. Cleiton Antonio Nunes

E-Mail Website
Guest Editor
Department of Food Science, Federal University of Lavras, University Campus, P.O. Box 3037, Lavras 37200-900, Minas Gerais, Brazil
Interests: spectroscopy techniques; image analysis; chemometrics
Special Issues, Collections and Topics in MDPI journals
Dr. Yhan Da Silva Mutz

E-Mail Website
Guest Editor
Department of Food Science, Federal University of Lavras, P.O. Box 3037, Lavras 37200-900, Minas Gerais, Brazil
Interests: spectroscopy techniques; image analysis; chemometrics
Special Issues, Collections and Topics in MDPI journals
Dr. Leticia Tessaro

E-Mail Website
Guest Editor Assistant
Department of Chemistry, Federal University of Lavras, P.O. Box 3037, Lavras 37200-900, Minas Gerais, Brazil
Interests: spectroscopy techniques; food quality; method validation

Special Issue Information

Dear Colleagues,

Spectroscopic and imaging techniques have emerged as innovative tools for determining the quality and safety of food and beverages, providing rapid, accurate, and non-destructive analytical methods. These techniques have enabled the development of methods for analyzing the physical and chemical properties of food and beverages, making it possible to detect adulterations and contaminants and determine nutritional composition in these products. In this context, the integration with chemometric and artificial intelligence methods further expands the analytical potential of spectroscopic and imaging techniques, making it possible to automate inspection processes, increase efficiency, reduce costs, and even make the methods portable. In this way, these tools can play a critical role in traceability, quality assurance, and safety along the food production and distribution chain, protecting consumers and contributing to the development of safer and more sustainable practices in the food sector. We invite readers to explore the contributions related to the future of spectroscopy and imaging applications in food quality and safety.

Dr. Cleiton Antonio Nunes
Dr. Yhan Da Silva Mutz
Guest Editors

Dr. Leticia Tessaro
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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 analysis
  • food composition
  • authentication
  • chemometrics
  • qualitative analysis
  • quantitative analysis
  • contaminants

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Published Papers (4 papers)

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Research

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22 pages, 2084 KB  
Article
Estimating Fibrosity Scores of Plant-Based Meat Products from Images: A Deep Neural Network Approach
by Abdullah Aljishi, Shirin Sheikhizadeh, Sanjoy Das and Sajid Alavi
Foods 2026, 15(4), 665; https://doi.org/10.3390/foods15040665 - 12 Feb 2026
Viewed by 505
Abstract
This paper proposes a deep neural network to estimate the fibrosities of plant-based meat product images. Images of varying fibrous microstructures were collected for this purpose, which were subject to spatial preprocessing and data enhancement. Their corresponding fibrosity scores were provided by two [...] Read more.
This paper proposes a deep neural network to estimate the fibrosities of plant-based meat product images. Images of varying fibrous microstructures were collected for this purpose, which were subject to spatial preprocessing and data enhancement. Their corresponding fibrosity scores were provided by two human experts. This data was used to train the network and to analyze its performance. Various statistical performance metrics were applied to evaluate the accuracy of the trained network’s estimated scores. It was found that the network performed significantly better when trained separately with fibrosity scores of each individual subject than with their combined scores, indicating that it was able to capture nuanced aspects of a subject’s perception. Another study was directed at explainability of the network’s estimates. Using standard software, a set of synthetic images of varying shapes and sizes were created as inputs to the network. Visual inspection of the output scores indicated that its estimates were influenced only by those features (i.e., food matrices and air cells) that were directly relevant to fibrosity, and not by extraneous factors. Full article
(This article belongs to the Special Issue Applications of Spectroscopy and Imaging Techniques in Food Quality and Safety)
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20 pages, 6299 KB  
Article
Quality and Maturity Detection of Korla Fragrant Pears via Integrating Hyperspectral Imaging with Multiscale CNN–LSTM
by Zhengbao Long, Tongzhao Wang, Zhijuan Zhang and Yuanyuan Liu
Foods 2025, 14(20), 3561; https://doi.org/10.3390/foods14203561 - 19 Oct 2025
Cited by 7 | Viewed by 1360
Abstract
To address the limitations of single indices in comprehensively evaluating the quality of Korla fragrant pears, this study proposes the firmness–soluble solids ratio (FSR), defined as the ratio of average firmness (FI) to soluble solid content (SSC) for each individual fruit, as a [...] Read more.
To address the limitations of single indices in comprehensively evaluating the quality of Korla fragrant pears, this study proposes the firmness–soluble solids ratio (FSR), defined as the ratio of average firmness (FI) to soluble solid content (SSC) for each individual fruit, as a novel index. Using 600 samples from five maturity stages with hyperspectral imaging (950–1650 nm), the dataset was split 4:1 by the SPXY algorithm. The findings demonstrated that FSR’s effectiveness in quantifying the dynamic relationship between FI and SSC during maturation. The developed multiscale convolutional neural network–long short-term memory (MSCNN–LSTM) model achieved high prediction accuracy with determination coefficients of 0.8934 (FI), 0.8731 (SSC), and 0.8610 (FSR), and root mean square errors of 0.9001 N, 0.7976%, and 0.1676, respectively. All residual prediction deviation values exceeded 2.5, confirming model robustness. The MSCNN–LSTM showed superior performance compared to other benchmark models. Furthermore, the integration of prediction models with visualization techniques successfully mapped the spatial distribution of quality indices. For maturity discrimination, hyperspectral-based partial least squares discriminant analysis and linear discriminant analysis models achieved perfect classification accuracy (100%) under five-fold cross-validation across all five maturity stages. This work provides both a theoretical basis and a technical framework for non-destructive evaluation of comprehensive quality and maturity in Korla fragrant pears. Full article
(This article belongs to the Special Issue Applications of Spectroscopy and Imaging Techniques in Food Quality and Safety)
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Review

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27 pages, 3298 KB  
Review
Applicability of Raman Spectroscopy for the Assessment of Wheat Flour Quality and Functionality in Bakery Applications
by Justine Van der Vennet, Fien De Witte, Peter Vandenabeele, Mia Eeckhout and Filip Van Bockstaele
Foods 2025, 14(19), 3330; https://doi.org/10.3390/foods14193330 - 25 Sep 2025
Cited by 1 | Viewed by 1746
Abstract
Advancements in Raman spectroscopy have broadened the utilization possibilities for food applications. The present review covers the working principle and methodology of the emerging technique in the context of wheat (flour) as a bakery ingredient. Special attention is paid to the primary constituents [...] Read more.
Advancements in Raman spectroscopy have broadened the utilization possibilities for food applications. The present review covers the working principle and methodology of the emerging technique in the context of wheat (flour) as a bakery ingredient. Special attention is paid to the primary constituents of wheat flour, starch and gluten proteins, both in their isolated forms and within complex matrices such as flour, dough, and various end products. This review examines how compositional and structural variations in these components are reflected in their Raman spectra and imaging characteristics and how this can be interpreted in terms of quality and functionality. The review concludes by outlining prospective research directions and future opportunities for advancing Raman-based analysis in cereal and bakery science. Full article
(This article belongs to the Special Issue Applications of Spectroscopy and Imaging Techniques in Food Quality and Safety)
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Other

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32 pages, 2266 KB  
Systematic Review
A Systematic Review of Imaging Techniques for the Botanical and Geographical Classification of Coffee
by Leticia Tessaro, Yhan da Silva Mutz, Davide Orsolini, Rosalba Calvini, Natália de Oliveira Souza, Giulia Mitestainer Silva, Alessandro Ulrici and Cleiton Antônio Nunes
Foods 2026, 15(5), 821; https://doi.org/10.3390/foods15050821 - 1 Mar 2026
Viewed by 691
Abstract
With evolving consumption trends, the coffee market is experiencing increasing demand for high-quality, traceable coffees, which, in turn, has led to price growth. Therefore, due to its increased economic value, coffee has become a constant target of fraudulent actions. As result, many analytical [...] Read more.
With evolving consumption trends, the coffee market is experiencing increasing demand for high-quality, traceable coffees, which, in turn, has led to price growth. Therefore, due to its increased economic value, coffee has become a constant target of fraudulent actions. As result, many analytical techniques have been explored as tools for coffee classification and authentication, of which the use of digital, hyperspectral and/or multispectral imaging is noteworthy. This type of analysis provides rapid, non-destructive, environmentally friendly, and increasingly accessible alternatives to conventional analytical methods. By consulting three different databases, this work systematically revised articles published in the last 10 years, which utilize digital image analysis and hyper/multispectral imaging for the botanical and geographical classification and authentication of coffees. The reviewed studies (n = 17) demonstrate that, when paired with classification algorithms, discrimination across species, origins, and quality categories can be achieved. A critical point to highlight is the importance of using whole beans and standardizes roast degree to avoid biasing the models. Concerning digital images, relying solely on color features limits the robustness of the classification models. Incorporating complementary textural and shape features is thus necessary to capture the coffee botanical or geographic information, as shown in a minor number of the selected studies. In a similar fashion, for hyper/multispectral imaging, there is still potential to further exploit the spatial information, thus achieving the technique’s full potential. The evidence indicates that image-based methods are steadily progressing into reliable tools for coffee authentication. Full article
(This article belongs to the Special Issue Applications of Spectroscopy and Imaging Techniques in Food Quality and Safety)
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