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Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality...
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Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control: 2nd Edition

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

Deadline for manuscript submissions: 30 December 2025 | Viewed by 4873

Special Issue Editor

Dr. Mourad Kharbach

E-Mail Website
Guest Editor
Circular Economy/Sustainable Solutions, LAB University of Applied Sciences, Mukkulankatu 19, 15101 Lahti, Finland
Interests: spectroscopy; foods; statistics; chemometrics; material sciences; climate change; catalytic reactions; mixture analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of food analysis and quality control has greatly benefited from the advancements in chromatographic and spectroscopic techniques. These techniques play a pivotal role in ensuring the safety, authenticity, and quality of food products. Traditional analytical methods, although widely used, often suffer from limitations such as time-consuming procedures, destructive sample preparation, and high costs.

In recent years, advanced chromatographic and spectroscopic techniques have emerged as powerful tools for food analysis and quality control. Chromatographic techniques, including gas chromatography (GC) and liquid chromatography (LC), provide excellent separation and identification capabilities, enabling the detection of various compounds in complex food matrices. These techniques offer enhanced sensitivity, selectivity, and efficiency, making them indispensable in food applications.

Complementing the chromatographic methods, spectroscopic techniques encompass a range of approaches such as infrared spectroscopy (IR), nuclear magnetic resonance (NMR), mass spectrometry (MS), and ultraviolet-visible (UV-Vis) spectroscopy. These techniques provide valuable insights into the chemical composition, structural characteristics, and functional properties of food components. They offer advantages such as non-destructive analysis, rapid detection, and minimal sample preparation requirements.

Furthermore, the integration of chemometric approaches with chromatographic and spectroscopic techniques has revolutionized data analysis in food analysis and quality control. Chemometrics enables the extraction of meaningful information from complex datasets, facilitates pattern recognition, and allows for the quantitative analysis of food samples. Statistical methods, multivariate analysis, and machine learning algorithms aid in the interpretation of chromatographic and spectroscopic data, enabling reliable classification, prediction, and quality assessment.

This Special Issue aims to highlight the recent advancements and applications of advanced chromatographic and spectroscopic techniques in food analysis and quality control. It focuses on research related to method development, validation, and the practical implementation of these techniques in various areas such as food safety, authenticity, nutritional analysis, and quality evaluation. Contributions that explore the integration of chemometrics with chromatography and spectroscopy are particularly encouraged.

By bringing together cutting-edge research and practical industrial applications, this Special Issue seeks to promote the adoption of advanced chromatographic and spectroscopic techniques for enhanced food analysis and quality control, ultimately contributing to the assurance of consumer safety and confidence in the food industry.

Dr. Mourad Kharbach
Guest Editor

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

  • food control
  • food analysis
  • food safety
  • food classification
  • quality control
  • authenticity
  • gas chromatography
  • liquid chromatography
  • spectroscopic techniques
  • chemometric approaches
  • multivariate analysis

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Published Papers (3 papers)

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Research

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15 pages, 2464 KiB  
Article
Grade Classification of Camellia Seed Oil Based on Hyperspectral Imaging Technology
by Yuqi Gu, Jianhua Wu, Yijun Guo, Sheng Hu, Kaixuan Li, Yuqian Shang, Liwei Bao, Muhammad Hassan and Chao Zhao
Foods 2024, 13(20), 3331; https://doi.org/10.3390/foods13203331 - 20 Oct 2024
Cited by 2 | Viewed by 1088
Abstract
To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. [...] Read more.
To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths (GA-SVM), characteristic wavelengths (CARS-GA-SVM), and fusing spectral and image features (CARS-GLCM-GA-SVM). The results show that the CARS-GLCM-GA-SVM model, which combined spectral and image information, had the best classification effect, and the accuracy of the calibration set and prediction set of the CARS-GLCM-GA-SVM model were 98.30% and 96.61%, respectively. Compared with the CARS-GA-SVM model, the accuracy of the calibration set and prediction set were improved by 10.75% and 12.04%, respectively. Compared with the GA-SVM model, the accuracy of the calibration set and prediction set were improved by 18.28% and 18.15%, respectively. The research showed that hyperspectral imaging technology can rapidly classify camellia seed oil grades. Full article
(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control: 2nd Edition)
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15 pages, 4575 KiB  
Article
Quantitative Prediction of Acid Value of Camellia Seed Oil Based on Hyperspectral Imaging Technology Fusing Spectral and Image Features
by Yuqi Gu, Lifang Shi, Jianhua Wu, Sheng Hu, Yuqian Shang, Muhammad Hassan and Chao Zhao
Foods 2024, 13(20), 3249; https://doi.org/10.3390/foods13203249 - 12 Oct 2024
Cited by 1 | Viewed by 1919
Abstract
Acid value (AV) serves as an important indicator to assess the quality of oil, which can be used to judge the deterioration of edible oil. In order to realize the quantitative prediction of the AV of camellia seed oil, which was made from [...] Read more.
Acid value (AV) serves as an important indicator to assess the quality of oil, which can be used to judge the deterioration of edible oil. In order to realize the quantitative prediction of the AV of camellia seed oil, which was made from camellia oleifolia, hyperspectral data of 168 camellia seed oil samples were collected using a hyperspectral imaging system, which were related to their AV content measured via classical chemical titration. On the basis of hyperspectral full wavelengths, characteristic wavelengths, and fusing spectral and image features, the quantitative prediction AV models for camellia seed oil were established. The results demonstrating the 2Der-SPA-GLCM-PLSR model fusing spectral and image features stood out as the optimal choices for the AV prediction of camellia seed oil, with the correlation coefficient of calibration set (Rc2) and the correlation coefficient of prediction set (Rp2) at 0.9698 and 0.9581, respectively. Compared with those of 2Der-SPA-PLSR, the Rc2 and Rp2 were improved by 2.11% and 2.57%, respectively. Compared with those of 2Der-PLSR, the Rc2 and Rp2 were improved by 5.02% and 5.31%, respectively. Compared with the model based on original spectrum, the Rc2 and Rp2 were improved by 32.63% and 40.11%, respectively. After spectral preprocessing, characteristic wavelength selection, and fusing spectral and image features, the correlation coefficient of the optimal AV prediction model was continuously improved, while the root mean square error was continuously decreased. The research demonstrated that hyperspectral imaging technology could precisely and quantitatively predict the AV of camellia seed oil and also provide a new environmental method for detecting the AV of other edible oils, which is conducive to sustainable development. Full article
(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control: 2nd Edition)
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Review

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17 pages, 2946 KiB  
Review
Application of Mass Spectrometry for Analysis of Nucleobases, Nucleosides and Nucleotides in Tea and Selected Herbs: A Critical Review of the Mass Spectrometric Data
by Magdalena Frańska and Rafał Frański
Foods 2024, 13(18), 2959; https://doi.org/10.3390/foods13182959 - 18 Sep 2024
Viewed by 1473
Abstract
The main and most commonly known biological function of nucleobases, nucleosides, and nucleotides is usually associated with the fact that they are the building blocks of nucleic acids. However, these compounds also belong to plant secondary metabolites, although in that role they have [...] Read more.
The main and most commonly known biological function of nucleobases, nucleosides, and nucleotides is usually associated with the fact that they are the building blocks of nucleic acids. However, these compounds also belong to plant secondary metabolites, although in that role they have attracted less attention than the others, e.g., terpenes, phenolics, or alkaloids. The former compounds are also important constituents of the human diet, e.g., as ingredients of tea and herbs, endowing them with specific taste qualities and pharmacological activities. Liquid chromatography–mass spectrometry seems to be the most important analytical method that permits the identification and determination of nucleobases, nucleosides, and nucleotides, along with the other metabolites. The main goal of this review is to discuss in detail the aspects of mass spectrometric detection of nucleobases, nucleosides, and nucleotides in tea and selected herbs. An important conclusion is that the identification of the compounds of interest should be performed not only on the basis of [M + H]+/[M − H]− ions but should also be confirmed by the respective product ions; however, as discussed in detail in this review, it may sometimes be problematic. It also clear that all difficulties that may be encountered when analyzing plant material are caused by the complexity of the analyzed samples and the need to analyze different classes of compounds, and this review absolutely does not debase any of the mentioned papers. Full article
(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control: 2nd Edition)
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