Nondestructive Testing Technologies for Food Quality and Safety Assessment

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

Deadline for manuscript submissions: 20 September 2026 | Viewed by 630

Editors


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CREA—Research Centre for Olive, Fruit and Citrus Crops, Via di Fioranello 52, 00134 Rome, Italy
Interests: spectroscopic applications in food and agriculture; food chemistry; food safety; shelf life assessment; metabolomics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
CREA-Research Center for Engineering and Agro-Food Processing, Via Manziana 30, 00189 Rome, Italy
Interests: cereals and grains; cereal chemistry; cereal functional foods; cereal foods; cereal based food; cereals compositional analyses; biochemical characterization of cereals; cereals processing; semolina pasta; pasta making; milling; bioactive compounds, antioxidants, brewer spent grain upcycling; food technology; food quality; functional food; food by products valorisation; grain and flour quality; sorghum; tef; einkorn; rice; durum and common wheat
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food chemical characterization constitutes a fundamental discipline in modern food science, providing high‑resolution information on the molecular composition, structural properties, and functional attributes of food matrices. Such information is essential for quality assessment, authentication, and safety evaluation, as well as for understanding the functional and nutritional behavior of foods, thereby supporting process optimization and technological innovation.

Conventional physicochemical analytical methods, although accurate and reliable, are generally destructive, labor‑intensive, costly, and time‑consuming. These limitations have driven the agri‑food sector toward the adoption of rapid and non‑destructive analytical approaches aimed at improving efficiency in supply chains.

In this context, non‑destructive techniques are gaining increasing relevance in food quality monitoring. In particular, the integrated application of computer vision and spectroscopic methods, combined with artificial intelligence techniques, offers powerful tools for characterizing the chemical and physical attributes of food samples through high‑dimensional, information‑rich datasets suitable for advanced chemometric modeling and data-driven interpretation.

This Special Issue of Foods will address key topics relevant to the agri-food sector, ranging from targeted analytical applications within specific stages of production chains to integrated approaches for comprehensive food quality evaluation along the entire value chain. Original research articles, review papers, and both theoretical and experimental studies are welcome. 

Dr. Roberto Ciccoritti
Dr. Federica Taddei
Guest Editors

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Keywords

  • food quality assessment
  • non-destructive analysis
  • spettroscopic techniques
  • computer vision
  • food adulteration
  • food authentication
  • food control
  • food safety
  • process optimization
  • food shelf life

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Published Papers (1 paper)

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Research

23 pages, 6626 KB  
Article
Reconstruction-Assisted Band Selection for Non-Destructive Prediction of Citrus Soluble Solids Content from VNIR Hyperspectral Images
by Junjie Zhao, Siya Liu, Fengyong Yang, Long Cheng, Fang Hu, Sixing Xu and Lei Shan
Foods 2026, 15(10), 1774; https://doi.org/10.3390/foods15101774 - 18 May 2026
Viewed by 357
Abstract
The increasing demand for better fruit flavor and eating quality has driven the need for rapid and non-destructive assessment of internal attributes to support fruit grading and precision supply. Visible–near-infrared hyperspectral imaging (VNIR-HSI) provides rich spectral–spatial information for evaluating sweetness in citrus fruit, [...] Read more.
The increasing demand for better fruit flavor and eating quality has driven the need for rapid and non-destructive assessment of internal attributes to support fruit grading and precision supply. Visible–near-infrared hyperspectral imaging (VNIR-HSI) provides rich spectral–spatial information for evaluating sweetness in citrus fruit, but its practical use is constrained by high spectral dimensionality, redundancy, and system cost. Here, we propose a reconstruction-assisted, attention-guided band-selection framework for non-destructive prediction of soluble solids content (SSC) in Shimen honey mandarins. The framework integrates spectral–spatial attention, probability-based differentiable band selection, and full-band reconstruction into a unified end-to-end architecture, enabling compact and informative band learning. Using 952 samples, the model selected 56 informative bands from the original 176-band hyperspectral data and achieved competitive SSC prediction on the test set (RMSE = 0.63 °Brix, R2 = 0.80) while maintaining high-fidelity reconstruction of the full-band hyperspectral cube from the compact input (peak signal-to-noise ratio, PSNR = 36.47 dB; structural similarity index, SSIM = 0.89). These findings support the proposed framework as a methodological proof of concept for non-destructive citrus quality evaluation, indicating that substantial spectral compression can be achieved under the current VNIR setting while largely preserving predictive performance. The selected bands may provide candidate spectral regions for future compact citrus-quality sensing systems. Full article
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