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Foods
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Hyperspectral Imaging and Other Nondestructive Methods for Analyzing Food Quality
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Hyperspectral Imaging and Other Nondestructive Methods for Analyzing Food Quality

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 4149

Special Issue Editor

Dr. Jun Sun

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: hyperspectral imaging; electronic nose; food quality; deep learning

Special Issue Information

Dear Colleagues,

Hyperspectral imaging and other nondestructive methods are revolutionizing food quality analysis by enabling precise, real-time assessment without compromising product integrity. Hyperspectral imaging, which captures both spatial and spectral data, allows for detailed analysis of the chemical, physical, and biological properties of food.

Hyperspectral imaging and other nondestructive methods are particularly valuable for detecting contaminants, identifying damage or defects, detecting adulteration, distinguishing between varieties or quality grades, monitoring ripeness, assessing shelf life, and performing the quantitative analysis of food composition, all while preserving the sample for further use. Advanced machine learning and deep learning algorithms enhance the accuracy and efficiency of data interpretation, enabling rapid decision-making in quality control and supply chain management. By integrating these technologies, the food industry can ensure higher standards of safety, reduce waste, and meet consumer demands for transparency and sustainability.

Applications of hyperspectral imaging and nondestructive methods span across various stages of food production, from farm to table. They are used to optimize agricultural practices, improve processing efficiency, and ensure compliance with regulatory standards. This interdisciplinary approach not only enhances food quality but also drives innovation in the industry, paving the way for smarter, more sustainable food systems.

Dr. Jun Sun
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 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. 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 quality
  • nondestructive detection
  • hyperspectral imaging
  • spectral analysis
  • sensory sensing
  • chemometrics
  • machine learning

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Published Papers (4 papers)

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Research

23 pages, 5981 KB  
Article
High-Accuracy Prediction of Chunmee Tea Grade via DeepSpectra Model and Near-Infrared Spectroscopy
by Yatong Zhang, Mobing Ren, Xiaohong Wu and Bin Wu
Foods 2026, 15(11), 1848; https://doi.org/10.3390/foods15111848 - 23 May 2026
Viewed by 163
Abstract
Chunmee tea quality is critical to its grading, and accurate identification is essential for quality evaluation and market valuation. However, traditional machine learning relies on manual feature extraction and causes spectral information loss, while conventional one-dimensional convolutional neural networks (1D-CNNs) are restricted by [...] Read more.
Chunmee tea quality is critical to its grading, and accurate identification is essential for quality evaluation and market valuation. However, traditional machine learning relies on manual feature extraction and causes spectral information loss, while conventional one-dimensional convolutional neural networks (1D-CNNs) are restricted by fixed kernels and narrow receptive fields, making multi-scale feature capture difficult. In this study, an improved DeepSpectra model integrated with the Inception module and residual connections was proposed for end-to-end automatic grading of Chunmee tea. A total of 360 samples across six grades (60 samples per grade) were collected using an Antaris II near-infrared spectrometer and preprocessed by multiplicative scatter correction (MSC). The proposed model was compared with other models. Results showed that under a 7:1:2 train–validation–test split, the proposed DeepSpectra achieved an average test accuracy of 96.39 ± 1.63% across ten random sample divisions, significantly outperforming the other models (p < 0.05). The model also exhibited excellent stability in five-fold cross-validation and superior generalization in small-sample scenarios, and a lightweight structure with low inference latency of 2.2 ms, which is suitable for real-time industrial applications. This work provides a reliable, efficient, and end-to-end method for grading Chunmee tea and offers a promising strategy for intelligent and rapid quality control of green tea. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Other Nondestructive Methods for Analyzing Food Quality)
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23 pages, 16495 KB  
Article
Visualization of Three-Dimensional SSC (Soluble Solids Content) Across the Entire Surface of Strawberries Using Near-Infrared Hyperspectral Imaging
by Hayato Seki, Bin Li, Tetsuo Kawaide, Te Ma, Satoru Tsuchikawa and Tetsuya Inagaki
Foods 2026, 15(9), 1563; https://doi.org/10.3390/foods15091563 - 1 May 2026
Viewed by 354
Abstract
Near-infrared hyperspectral imaging (NIR-HSI) is widely used as a non-destructive technique for evaluating internal fruit quality; however, reliable pixel-wise visualization remains challenging due to geometry-induced spectral distortions and the lack of statistically interpretable validation criteria. This study proposes an integrated framework for three-dimensional [...] Read more.
Near-infrared hyperspectral imaging (NIR-HSI) is widely used as a non-destructive technique for evaluating internal fruit quality; however, reliable pixel-wise visualization remains challenging due to geometry-induced spectral distortions and the lack of statistically interpretable validation criteria. This study proposes an integrated framework for three-dimensional visualization of soluble solids content (SSC) across the entire surface of strawberries using NIR-HSI combined with shape-aware spectral correction and pixel-level reliability assessment. Two complementary imaging systems—a line-scan system and a rotation-scan system—were used to acquire hyperspectral and 3D shape data. Fruit height and surface orientation were incorporated into spectral preprocessing to reduce illumination and curvature effects. Partial least squares regression (PLSR) models were developed using region-of-interest-averaged spectra and applied to pixel-wise SSC mapping. To assess the statistical validity of pixel-level predictions, an imaging reliability index based on the Mahalanobis distance in the PLS score space was introduced. The results show that models with high sample-level accuracy do not necessarily produce reliable SSC maps, whereas reliability-based model selection improves image interpretability. This framework enables consistent three-dimensional SSC visualization and is applicable to hyperspectral imaging of internal fruit attributes. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Other Nondestructive Methods for Analyzing Food Quality)
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16 pages, 2112 KB  
Article
Nondestructive Detection of Soluble Solids Content in Apples Based on Multi-Attention Convolutional Neural Network and Hyperspectral Imaging Technology
by Yan Tian, Jun Sun, Xin Zhou, Sunli Cong, Chunxia Dai and Lei Shi
Foods 2025, 14(22), 3832; https://doi.org/10.3390/foods14223832 - 9 Nov 2025
Cited by 2 | Viewed by 1113
Abstract
Soluble solids content is the most important attribute related to the quality and price of apples. The objective of this study was to detect the soluble solids content (SSC) in ‘Fuji’ apples using hyperspectral imaging combined with a deep learning algorithm. The hyperspectral [...] Read more.
Soluble solids content is the most important attribute related to the quality and price of apples. The objective of this study was to detect the soluble solids content (SSC) in ‘Fuji’ apples using hyperspectral imaging combined with a deep learning algorithm. The hyperspectral images of 570 apple samples were obtained and the whole region of apple sample hyperspectral data was collected and preprocessed. In addition, a method involving multi-attention convolutional neural network (MA-CNN) is proposed, which extracts spectral and spatial features from hyperspectral images by embedding channel attention (CA) and spatial attention (SA) modules in a convolutional neural network. The CA and SA modules help the network adaptively focus on important spectral–spatial features while reducing the interference of redundant information. Additionally, the Bayesian optimization algorithm (BOA) is used for model hyperparameter optimization. A comprehensive evaluation is conducted by comparing the proposed model with CA-CNN models, SA-CNN, and the current mainstream models. Furthermore, the best prediction performances for detecting SSC in apple samples were obtained from the MA-CNN model, with an Rp2 value of 0.9602 and an RMSEP value of 0.0612 °Brix. The results of this study indicated that the MA-CNN algorithm combined with hyperspectral imaging technology can be used as an effective method for rapid detection of apple quality parameters. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Other Nondestructive Methods for Analyzing Food Quality)
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20 pages, 8637 KB  
Article
Quality Assessment of Prune Jam with Different Concentration Methods Based on Physicochemical Properties, GC-IMS, and Intelligent Sensory Analysis
by Rui Yang, Langhan Zhao, Wei Wang, Qingping Du, Wei Li, Tongle Sun and Shihao Huang
Foods 2025, 14(12), 2084; https://doi.org/10.3390/foods14122084 - 13 Jun 2025
Cited by 3 | Viewed by 1790
Abstract
This study systematically investigated the impacts of four concentration methods—vacuum freezing concentration (VFC), microwave vacuum concentration (MVC), atmospheric thermal concentration (ATC), and vacuum thermal concentration (VTC)—on the quality and volatile compounds of prune jam. Advanced analytical techniques, including electronic tongue, electronic nose, gas [...] Read more.
This study systematically investigated the impacts of four concentration methods—vacuum freezing concentration (VFC), microwave vacuum concentration (MVC), atmospheric thermal concentration (ATC), and vacuum thermal concentration (VTC)—on the quality and volatile compounds of prune jam. Advanced analytical techniques, including electronic tongue, electronic nose, gas chromatography–ion mobility spectrometry (GC-IMS), and multivariate statistical methods (principal component analysis, partial least squares discriminant analysis), were employed to evaluate physicochemical properties and flavor profiles. Results showed that non-thermal methods (particularly VFC) significantly outperformed thermal methods (ATC/VTC) in nutrient preservation. For instance, VFC retained 91.4% of ascorbic acid and limited dietary fiber loss to 4.55%, while ATC caused up to 60.1% ascorbic acid degradation and 51.75% dietary fiber loss. In terms of color stability, VFC induced a 1.04-fold increase in browning index (BI) and a 2.54-fold increase in total color difference (ΔE), significantly lower than ATC’s 1.6-fold BI increase and 7.26-fold ΔE rise. GC-IMS identified 42 volatile compounds, categorized into aldehydes (17), alcohols (9), esters (7), etc. Multivariate analysis screened 15 key flavor compounds (VIP > 1, p < 0.05), such as ethyl acetate and methanol, revealing that non-thermal methods better preserved the characteristic sweet–sour flavor and reduced off-flavor formation. These findings highlight VFC’s superiority in maintaining nutritional and sensory quality, providing scientific guidance for industrial jam production and flavor optimization in fruit processing. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Other Nondestructive Methods for Analyzing Food Quality)
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