Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.7
5.1
Journals
Foods
Special Issues
Advancements and Applications of Imaging and Hyperspectral Technologies in Non-Destructive...
share announcement

Advancements and Applications of Imaging and Hyperspectral Technologies in Non-Destructive Food Testing

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 473

Special Issue Editors

Dr. Zhongzhi Han

E-Mail Website
Guest Editor
School of Science and Information, Qingdao Agricultural University, Qingdao, China
Interests: computer vision; food safety; hyperspectral imaging; aflatoxin detection; deep learning
Dr. Limiao Deng

E-Mail Website
Guest Editor
School of Science and Information, Qingdao Agricultural University, Qingdao, China
Interests: machine learning; image processing; food quality inspection; smart agriculture

Special Issue Information

Dear Colleagues,

Non-destructive testing is critical for ensuring food safety, quality, and sustainability while minimizing waste and preserving product integrity. Imaging and hyperspectral technologies, such as hyperspectral imaging (HSI), multispectral imaging (MSI), and advanced image processing algorithms, have emerged as powerful tools for rapid, accurate, and contactless analysis of food properties. This Special Issue is dedicated to exploring the advancements and extensive applications of imaging and hyperspectral technologies within the realm of non-destructive food testing. Topics of interest include, but are not limited to, the following: food safety monitoring, quality grading, defect detection, authenticity verification, and process optimization. We invite contributions that highlight innovative methodologies, case studies, and future perspectives on utilizing imaging and hyperspectral data for precise, rapid, and non-invasive food analysis. This issue aims to provide a comprehensive platform for researchers and industry professionals to explore the potential of these technologies in ensuring food safety, enhancing sustainability, and advancing the global food supply chain.

Dr. Zhongzhi Han
Dr. Limiao Deng
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

  • image processing
  • hyperspectral imaging
  • non-destructive testing
  • food quality
  • food safety
  • computer vision
  • defect detection
  • intelligent food inspection

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 4595 KiB  
Article
Prediction of Key Quality Parameters in Hot Air-Dried Jujubes Based on Hyperspectral Imaging
by Quancheng Liu, Chunzhan Yu, Yuxuan Ma, Hongwei Zhang, Lei Yan and Shuxiang Fan
Foods 2025, 14(11), 1855; https://doi.org/10.3390/foods14111855 - 23 May 2025
Viewed by 337
Abstract
Traditional biochemical analysis methods are not only resource-intensive and time-consuming, but are increasingly inadequate for meeting the demands of modern production and quality testing. In recent years, hyperspectral imaging (HSI) technology has been widely applied as a non-destructive detection method for fruit and [...] Read more.
Traditional biochemical analysis methods are not only resource-intensive and time-consuming, but are increasingly inadequate for meeting the demands of modern production and quality testing. In recent years, hyperspectral imaging (HSI) technology has been widely applied as a non-destructive detection method for fruit and vegetable quality assessment. This study, based on HSI technology, systematically investigates the distribution patterns of jujube quality parameters under various drying temperature conditions. It focuses on analyzing six key quality indicators: L*, a*, b*, soluble solid content (SSC), hardness, and moisture content. HSI was used to acquire reflectance (R), absorbance (A), and Kubelka–Munk (K-M) spectral data of jujubes at various drying temperatures, followed by several spectral preprocessing methods, including standard normal variate (SNV), baseline correction (baseline), and Savitzky–Golay first derivative (SG1st). Subsequently, a nonlinear support vector regression (SVR) model was used to perform regression modeling for the six quality parameters. The results demonstrate that the SG1st preprocessing method significantly enhanced the predictive capability of the model. For the predictions of L*, a*, b*, SSC, hardness, and moisture content, the best inversion models achieved coefficients of determination Rp2 of 0.9972, 0.9970, 0.9857, and 0.9972, respectively. To further enhance modeling accuracy, deep learning models such as bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and convolutional neural network–bidirectional gated recurrent unit (CNN-BiGRU) were introduced and compared comprehensively under the optimal spectral preprocessing conditions. The results demonstrate that deep learning models significantly improved modeling accuracy, with the CNN-BiGRU model performing particularly well. Compared to the SVR model, the Rp2 values for L*, a*, b*, SSC, hardness, and moisture increased by 0.005, 0.007, 0.008, 0.011, 0.007, and 0.006, respectively; the RPD values increased by 0.036, 0.04, 0.26, 0.462, 0.428, and 0.216. This study provides important insights into the further application of HSI technology in the quality monitoring and optimization of the jujube drying process. Full article
(This article belongs to the Special Issue Advancements and Applications of Imaging and Hyperspectral Technologies in Non-Destructive Food Testing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop