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Vibrational Spectroscopic Sensing Technologies and Applications for the Evaluation of Food Quality and Authenticity

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 5553

Special Issue Editors

Dr. Francisco Ferrero

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Oviedo, Gijón, Spain
Interests: smart instrumentation; optical measurements; near-infrared spectroscopy; chemometrics
Special Issues, Collections and Topics in MDPI journals
Dr. Ana Soldado

E-Mail Website
Guest Editor
Department of Physical and Analytical Chemistry, University of Oviedo, 33006 Oviedo, Spain
Interests: analytical chemistry; agrofood; near-infrared spectroscopy; chemometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food may be inadvertently or intentionally contaminated with chemicals or other physical substances that are considered unacceptable by law or for other reasons.

Despite the potential of vibrational spectroscopic techniques for assessing food quality and authenticity, modern food inspection methods still require the further development of measurement equipment and dynamic chemometric analytical techniques.

This Special Issue will focus on the application of vibrational spectroscopic techniques, including near-infrared, mid-infrared, Raman spectroscopy, and hyperspectral imaging, to the evaluation of food quality and authenticity analysis.

The characteristics and applications of these techniques, along with an analysis of their major limitations, will be discussed, with an emphasis on the treatment of spectral data using chemometrics.

Researchers in the field of food will be able to present their latest findings in this Special Issue.

Dr. Francisco Ferrero
Dr. Ana Soldado
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 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. Sensors 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 2600 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

  • near-infrared spectroscopy
  • chemometrics
  • instrumentation
  • machine learning
  • food quality applications
  • food authenticity applications
 

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Published Papers (8 papers)

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Research

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14 pages, 1143 KB  
Article
Near-Infrared Spectroscopy for the Single-Kernel Analysis of Sorghum Protein Content
by Princess Tiffany D. Mendoza, Paul R. Armstrong, Erin D. Scully, Xiaorong Wu, Kamaranga H. S. Peiris, Scott R. Bean and Kaliramesh Siliveru
Sensors 2026, 26(10), 2936; https://doi.org/10.3390/s26102936 - 7 May 2026
Viewed by 549
Abstract
Protein content is an important quality trait in sorghum that influences breeding approaches, end-use applications, and market value. Influenced by genetic, agronomic, and environmental variability, sorghum is characterized by its wide variation in composition, which may also be evident in kernels from the [...] Read more.
Protein content is an important quality trait in sorghum that influences breeding approaches, end-use applications, and market value. Influenced by genetic, agronomic, and environmental variability, sorghum is characterized by its wide variation in composition, which may also be evident in kernels from the same sample. This study developed and evaluated a method for a non-destructive and rapid prediction of protein content in individual sorghum kernels using single-kernel near-infrared spectroscopy (SKNIR). Applying different pre-processing techniques to the spectra collected from intact kernels, the calibration models were developed using partial least squares regression and the reference protein content values obtained from the LECO combustion method. The best model was obtained using multiplicative scatter correction as pre-processing, resulting in a standard error of prediction of 0.83% and a relative predictive determinant of 3.40. These were indicative of the good predictive ability of the model and the instrument to be applied in quality control and sorting applications. These results highlight the potential of SKNIR to capture the inter-kernel variability in sorghum protein content and enhance screening for grain quality in breeding and grain processing. Full article
(This article belongs to the Special Issue Vibrational Spectroscopic Sensing Technologies and Applications for the Evaluation of Food Quality and Authenticity)
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23 pages, 1071 KB  
Article
Rapid Assessment of Italian Honey Chemical Composition and Botanical Origin Using NIR Spectroscopy Coupled with Chemometric Analysis
by Alessia Zoroaster, Andrea Calore, Anisseh Sobhani, Nicoletta Dainese, Anna Granato, Severino Segato and Lorenzo Serva
Sensors 2026, 26(9), 2796; https://doi.org/10.3390/s26092796 - 30 Apr 2026
Viewed by 400
Abstract
Honey quality and authenticity assessment require rapid and reliable analytical tools capable of supporting both laboratory and on-site applications. Near-infrared (NIR) spectroscopy represents a non-destructive and cost-effective approach; however, its performance depends on instrument characteristics and chemometric strategies. This study compared one benchtop [...] Read more.
Honey quality and authenticity assessment require rapid and reliable analytical tools capable of supporting both laboratory and on-site applications. Near-infrared (NIR) spectroscopy represents a non-destructive and cost-effective approach; however, its performance depends on instrument characteristics and chemometric strategies. This study compared one benchtop and two portable NIR-based systems for predicting key physicochemical parameters (moisture, electrical conductivity, glucose, fructose, reducing sugars, pH, hydroxymethylfurfural, and diastatic activity) and for discriminating botanical origin in 80 Italian honey samples. Spectral data were processed using multiple pre-processing techniques and algorithms (PLS, k-NN, Random Forest, SVM), with and without wavelength selection (siPLS and CARS-PLS), under cross-validation schemes. The benchtop system achieved the highest regression performance (R2 up to 0.91 for glucose and electrical conductivity) and the most reliable botanical classification (balanced accuracy = 0.90). Portable systems showed moderate predictive ability for bulk compositional parameters (R2 up to 0.86 for glucose) but limited classification performance. Wavelength selection resulted in only marginal improvements. Hydroxymethylfurfural and diastatic activity were poorly predicted (R2 up to 0.49), likely due to their low concentrations. Summarising, the main outcomes suggested that tested portable NIR settings are also suitable for rapid quantitative screening of chemical traits, whereas the benchtop system provide higher precision for botanical qualitative authentication. Full article
(This article belongs to the Special Issue Vibrational Spectroscopic Sensing Technologies and Applications for the Evaluation of Food Quality and Authenticity)
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21 pages, 5794 KB  
Article
Comparative Evaluation of Portable and Benchtop NIR Spectroscopy and Hyperspectral Imaging for Detecting Honey Adulteration
by Aysenur-Betul Bilgin, Miguel Vega-Castellote, José-Antonio Entrenas, Irina Torres-Rodríguez, Didem-Peren Aykas, Pervin Basaran and Dolores Pérez-Marín
Sensors 2026, 26(9), 2750; https://doi.org/10.3390/s26092750 - 29 Apr 2026
Viewed by 369
Abstract
Honey adulteration remains a major challenge for ensuring food authenticity and sustainable quality control. In this study, near-infrared (NIR) spectroscopy and hyperspectral imaging (HSI) were comparatively evaluated as green, non-destructive analytical techniques for the discrimination of pure and adulterated honey using chemometric modeling. [...] Read more.
Honey adulteration remains a major challenge for ensuring food authenticity and sustainable quality control. In this study, near-infrared (NIR) spectroscopy and hyperspectral imaging (HSI) were comparatively evaluated as green, non-destructive analytical techniques for the discrimination of pure and adulterated honey using chemometric modeling. A total of 180 honey samples, including pure and adulterated samples with agave syrup, sucrose syrup, or water at varying concentrations, were analyzed using two NIR platforms (MicroNIR™ 1700 and NIRS™ DS2500) and an HSI system (Micro-Hyperspec® NIR camera). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied for exploratory analysis and supervised classification, respectively. Both techniques enabled effective discrimination between pure and adulterated honey. The results demonstrated that the two NIR platforms achieved superior classification performance: the MicroNIR™ 1700 yielded overall sensitivities, specificities, and accuracies of 100%, respectively. While the HSI system provided complementary spectral-spatial information, its performance and that of the NIRS™ DS2500 were slightly lower, with an overall accuracy of 93.10%, particularly at low levels of adulteration (≤10%). Overall, these results demonstrate that NIR-based spectroscopy is a reliable, fast, non-destructive, and eco-friendly analytical tool for testing the authenticity of honey. The portable NIR system, in particular, provides a cost-effective and field-deployable solution for in situ quality control. Integrating it into routine quality control practices could help prevent food fraud, protect consumer trust, and promote sustainable industry development. Full article
(This article belongs to the Special Issue Vibrational Spectroscopic Sensing Technologies and Applications for the Evaluation of Food Quality and Authenticity)
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19 pages, 1751 KB  
Article
Simultaneous Assessment of Chicken Freshness and Authenticity Using a Single Multispectral Imaging Device: A Cross-Laboratory Evaluation Using Identical Instruments
by Anastasia Lytou, Maria-Konstantina Spyratou, Aske Schultz Carstensen, George-John Nychas and Nikos Chorianopoulos
Sensors 2026, 26(9), 2702; https://doi.org/10.3390/s26092702 - 27 Apr 2026
Viewed by 651
Abstract
This study evaluated a portable multispectral imaging (MSI) system for simultaneously assessing chicken meat quality, including freshness and authenticity detection. For freshness, total aerobic counts and MSI analyses were performed on fresh and thawed samples throughout storage at 4 °C. For authenticity (product [...] Read more.
This study evaluated a portable multispectral imaging (MSI) system for simultaneously assessing chicken meat quality, including freshness and authenticity detection. For freshness, total aerobic counts and MSI analyses were performed on fresh and thawed samples throughout storage at 4 °C. For authenticity (product condition and origin), Greek and Danish chicken samples, both fresh and thawed, were analyzed in separate laboratories using identical instruments. Data were modeled using PLS-R, kNN, and SVM. Model performance for total viable count prediction was evaluated via R2 and RMSE, while classification used accuracy, specificity, recall and precision. PLS-R beta coefficients highlighted the contribution of specific wavelengths. For Greek chicken fillets, kNN achieved the best performance on fresh samples (RMSE = 0.347, R2 = 0.979), while PLS-R performed best on thawed samples (RMSE = 0.787, R2 = 0.859). Wavelength 460 nm was the most important for all freshness predictions. Differences between Danish and Greek samples were observed in classification performance, optimal algorithms and key wavelengths. For origin classification (using fresh and thawed samples), models reached near-perfect accuracy, with PLS-DA highlighting 660 nm and 850 nm as most significant. These results demonstrate the MSI system’s potential for the rapid, accurate and simultaneous evaluation of multiple chicken meat quality attributes using a single instrument. Full article
(This article belongs to the Special Issue Vibrational Spectroscopic Sensing Technologies and Applications for the Evaluation of Food Quality and Authenticity)
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15 pages, 1121 KB  
Article
Detection and Quantification of Corn Starch and Wheat Flour as Adulterants in Milk Powder by Raman Spectroscopy Coupled with Chemometric Routines
by Edwin R. Caballero-Agosto, Louang D. Cruz-Dorta, Samuel P. Hernandez-Rivera, Leonardo C. Pacheco-Londoño and Ricardo Infante-Castillo
Sensors 2026, 26(4), 1304; https://doi.org/10.3390/s26041304 - 18 Feb 2026
Viewed by 554
Abstract
Adulteration of milk powder (MP) is performed, especially in underdeveloped countries, by adding corn starch (CS) or wheat flour (WF) without mentioning it. Multiple techniques have been established to reduce these deceptive methods. Most of these techniques require samples to be sent to [...] Read more.
Adulteration of milk powder (MP) is performed, especially in underdeveloped countries, by adding corn starch (CS) or wheat flour (WF) without mentioning it. Multiple techniques have been established to reduce these deceptive methods. Most of these techniques require samples to be sent to the laboratory for results through a time-consuming, expert-requiring, and destructive procedure. Raman spectroscopy (RS) has seen application due to the availability of portable modalities and its non-destructive, water-insensitive nature. Using principal component analysis (PCA), the differences and similarities between MP and the adulterants (CS and WF) have been evaluated. To quantify the percentages of CS and WF binary mixtures independently with MP, partial least squares regression (PLSR) has been employed. A total of 70 MP samples independently adulterated with CS and WF were prepared. Thirteen chemometric modes were developed by combining the first and second derivatives with Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) to quantify adulteration. The results obtained for CS and WF mixtures show errors of 0.76 and 0.77 %w/w, respectively, with the optimized math pretreatment. These results demonstrate that the portable RS modality can be used as an effective technique for detecting adulterants in milk powder. Full article
(This article belongs to the Special Issue Vibrational Spectroscopic Sensing Technologies and Applications for the Evaluation of Food Quality and Authenticity)
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11 pages, 1823 KB  
Article
Comparison of Benchtop and Portable Near-Infrared Instruments to Predict the Type of Microplastic Added to High-Moisture Food Samples
by Adam Kolobaric, Shanmugam Alagappan, Jana Čaloudová, Louwrens C. Hoffman, James Chapman and Daniel Cozzolino
Sensors 2026, 26(1), 210; https://doi.org/10.3390/s26010210 - 29 Dec 2025
Cited by 1 | Viewed by 655
Abstract
Near-infrared (NIR) spectroscopy is a rapid, non-destructive analytical tool widely used in the food and agricultural sectors. In this study, two NIR instruments were compared for classifying the addition of microplastics (MPs) to high-moisture-content samples such as vegetables and fruit. Polyethylene (PE), polypropylene [...] Read more.
Near-infrared (NIR) spectroscopy is a rapid, non-destructive analytical tool widely used in the food and agricultural sectors. In this study, two NIR instruments were compared for classifying the addition of microplastics (MPs) to high-moisture-content samples such as vegetables and fruit. Polyethylene (PE), polypropylene (PP), and a mix of polymers (PE + PP) MP were added to mixtures of spinach and banana and scanned using benchtop (Bruker Tango) and portable (MicroNIR) instruments. Both principal component analysis (PCA) and partial least squares (PLS) were used to analyze and interpret the spectra of the samples. Quantitative models were developed to predict the addition of Mix, PP, or PE to spinach and banana samples using PLS regression. The R2 CV and the SECV obtained were 0.88 and 0.44 for the benchtop samples, and 0.54 and 0.67 for the portable instruments, respectively. Two wavenumber regions were also evaluated: 11,520–7500 cm−1 (short to medium wavelengths), and 7500–4200 cm−1 (long wavelengths). The R2 CV and the SECV obtained were 0.88 and 0.46, 0.86 and 0.49, respectively, for the prediction of addition in samples analyzed on the benchtop instrument using short and long wavenumbers, respectively. This study provides new insights into the comparison of two instruments for detecting the addition of MPs in high-moisture samples. The results of this study will ensure that NIR can be utilized not only to measure the quality of these samples but also to monitor MPs. Full article
(This article belongs to the Special Issue Vibrational Spectroscopic Sensing Technologies and Applications for the Evaluation of Food Quality and Authenticity)
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Review

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24 pages, 1913 KB  
Review
Trends in Vibrational Spectroscopy: NIRS and Raman Techniques for Health and Food Safety Control
by Candela Melendreras, Jesús Montero, José M. Costa-Fernández, Ana Soldado, Francisco Ferrero, Francisco Fernández Linera, Marta Valledor and Juan Carlos Campo
Sensors 2026, 26(3), 989; https://doi.org/10.3390/s26030989 - 3 Feb 2026
Cited by 2 | Viewed by 805
Abstract
There is an increasing need to establish reliable safety controls in the food industry and to protect public health. Consequently, there are numerous efforts to develop sensitive, robust, and selective analytical strategies. As regulatory requirements for food and the concentration for target biomarkers [...] Read more.
There is an increasing need to establish reliable safety controls in the food industry and to protect public health. Consequently, there are numerous efforts to develop sensitive, robust, and selective analytical strategies. As regulatory requirements for food and the concentration for target biomarkers in clinical analysis evolve, the food and health sectors are showing a growing interest in developing non-destructive, rapid, on-site, and environmentally safe methodologies. One alternative that meets the conditions is non-destructive spectroscopic sensors, such as those based on vibrational spectroscopy (Raman, surface-enhanced Raman—SERS, mid- and near-infrared spectroscopy, and hyperspectral imaging built on those techniques). The use of vibrational spectroscopy in food safety and health applications is expanding rapidly, moving beyond the laboratory bench to include on-the-go and in-line deployment. The dominant trends include the following: (1) the miniaturisation and portability of instruments; (2) surface-enhanced Raman spectroscopy (SERS) and nanostructured substrates for the detection of trace contaminants; (3) hyperspectral imaging (HSI) and deep learning for the spatial screening of quality and contamination; (4) the stronger integration of chemometrics and machine learning for robust classification and quantification; (5) growing attention to calibration transfer, validation, and regulatory readiness. These advances will bring together a variety of tools to create a real-time decision-making system that will address the issue in question. This article review aims to highlight the trends in vibrational spectroscopy tools for health and food safety control, with a particular focus on handheld and miniaturised instruments. Full article
(This article belongs to the Special Issue Vibrational Spectroscopic Sensing Technologies and Applications for the Evaluation of Food Quality and Authenticity)
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14 pages, 257 KB  
Review
New Developments and Future Challenges of Non-Destructive Near-Infrared Spectroscopy Sensors in the Cheese Industry
by Maria Tarapoulouzi, Wenyang Jia and Anastasios Koidis
Sensors 2026, 26(2), 556; https://doi.org/10.3390/s26020556 - 14 Jan 2026
Cited by 1 | Viewed by 866
Abstract
Near-infrared (NIR) spectroscopy has emerged as a pivotal non-destructive analytical technique within the cheese industry, offering rapid and precise insights into the chemical composition and quality attributes of various cheese types. This review explores the evolution of NIR spectral sensors, highlighting key technological [...] Read more.
Near-infrared (NIR) spectroscopy has emerged as a pivotal non-destructive analytical technique within the cheese industry, offering rapid and precise insights into the chemical composition and quality attributes of various cheese types. This review explores the evolution of NIR spectral sensors, highlighting key technological advancements and their integration into cheese production processes as well as final products already in markets. In addition, the review discusses challenges such as calibration complexities, the influence of sample heterogeneity and the need for robust data and interpretation models through spectroscopy coupled with AI methods. The future potential of NIR spectral sensors, including real-time in-line monitoring and the development of portable devices for on-site analysis, is also examined. This review aims to provide a critical assessment of current NIR spectral sensors and their impact on the cheese industry, offering insights for researchers and industry professionals aiming to enhance quality control and innovation in cheese production, as well as authenticity and fraud studies. The review concludes that the integration of advanced NIR spectroscopy with AI represents a transformative approach for the cheese industry, enabling more accurate, efficient and sustainable quality assessment practices that can strengthen both production consistency and consumer trust. Full article
(This article belongs to the Special Issue Vibrational Spectroscopic Sensing Technologies and Applications for the Evaluation of Food Quality and Authenticity)
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