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Advancements and Applications of Imaging and Hyperspectral Technologies in Non-Destructive...
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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 981

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

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Published Papers (2 papers)

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Research

19 pages, 4445 KB  
Article
Hyperspectral Imaging-Based Deep Learning Method for Detecting Quarantine Diseases in Apples
by Hang Zhang, Naibo Ye, Jingru Gong, Huajie Xue, Peihao Wang, Binbin Jiao, Liping Yin and Xi Qiao
Foods 2025, 14(18), 3246; https://doi.org/10.3390/foods14183246 - 18 Sep 2025
Viewed by 153
Abstract
Rapid detection of quarantine diseases in apples is essential for import–export control but remains difficult because routine inspections rely on manual visual checks that limit automation at port scale. A fast, non-destructive system suitable for deployment at customs is therefore needed. In this [...] Read more.
Rapid detection of quarantine diseases in apples is essential for import–export control but remains difficult because routine inspections rely on manual visual checks that limit automation at port scale. A fast, non-destructive system suitable for deployment at customs is therefore needed. In this study, three common apple quarantine pathogens were targeted using hyperspectral images acquired by a close-range hyperspectral camera and analyzed with a convolutional neural network (CNN). Symptoms of these diseases often appear similar in RGB images, making reliable differentiation difficult. Reflectance from 400 to 1000 nm was recorded to provide richer spectral detail for separating subtle disease signatures. To quantify stage-dependent differences, average reflectance curves were extracted for apples infected by each pathogen at early, middle, and late lesion stages. A CNN tailored to hyperspectral inputs, termed HSC-Resnet, was designed with an increased number of convolutional channels to accommodate the broad spectral dimension and with channel and spatial attention integrated to highlight informative bands and regions. HSC-Resnet achieved a precision of 95.51%, indicating strong potential for fast, accurate, and non-destructive detection of apple quarantine diseases in import–export management. Full article
(This article belongs to the Special Issue Advancements and Applications of Imaging and Hyperspectral Technologies in Non-Destructive Food Testing)
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20 pages, 4595 KB  
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
Cited by 2 | Viewed by 670
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)
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