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Foods
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Non-Destructive Quality Evaluation Methods for Foods
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Non-Destructive Quality Evaluation Methods for Foods

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

Deadline for manuscript submissions: 5 March 2025 | Viewed by 3395

Special Issue Editors

Dr. Yanlong Han

E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: processing of agricultural products; non-destructive detection; image identification; detection equipment; damage of agricultural products
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anqi Li

E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: rice processing; agricultural product modeling; material analysis; parameter optimization; simulation design
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangyi Meng

E-Mail Website
Guest Editor
College of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China
Interests: cereal; grain processing; discrete element method
Dr. Yawen Xiao

E-Mail Website
Guest Editor
College of Mechanical Engineering, Yangzhou University, Yangzhou, China
Interests: food processing and engineering

Special Issue Information

Dear Colleagues,

The safety of foods and agro-products is directly related to human health and social stability and, as such, has long been the subject of great attention.​ Due to the large consumption of foods and agro-products, finding non-destructive large-scale quality evaluation methods for these products is an urgent problem, solved by emerging strategies such as spectral analysis and imaging and sensor technologies​ Although these technologies help improve productivity, reduce wastage, and optimize resource utilization, they still face many challenges in the large-scale non-destructive quality evaluation of foods and agro-products. Therefore, it is important to build on these existing technologies to reduce their technical complexity, control their usage costs, and improve their applicability and manoeuvrability in large-scale production environments.

This Special Issue analyses the principles and improvements in non-destructive quality evaluation methods, focusing also on the creation and application of new approaches. This issue will include interdisciplinary studies embracing agriculture, with disciplines ranging from biology to engineering and electronic information engineering. The research articles will cover various non-destructive quality evaluation methods such as near-infrared spectroscopy and machine vision technologies. All article types, including original research, opinion, and review articles, are welcome.

Dr. Yanlong Han
Prof. Dr. Anqi Li
Dr. Xiangyi Meng
Dr. Yawen Xiao
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

  • food and agro-product safety
  • non-destructive quality evaluation
  • non-destructive testing
  • mass production
  • spectral analysis

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Published Papers (3 papers)

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Research

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13 pages, 1244 KiB  
Article
The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning
by Jikai Che, Qing Liang, Yifan Xia, Yang Liu, Hongshan Li, Ninggang Hu, Weibo Cheng, Hong Zhang, Hong Zhang and Haipeng Lan
Foods 2024, 13(21), 3522; https://doi.org/10.3390/foods13213522 - 4 Nov 2024
Cited by 3 | Viewed by 1049
Abstract
Quality control and grading of Korla fragrant pears significantly impact their commercial value. Rapid and non-destructive detection of soluble solids content (SSC) and firmness is crucial to improving this. This study proposes a method combining near-infrared spectroscopy (NIRS) with machine learning for the [...] Read more.
Quality control and grading of Korla fragrant pears significantly impact their commercial value. Rapid and non-destructive detection of soluble solids content (SSC) and firmness is crucial to improving this. This study proposes a method combining near-infrared spectroscopy (NIRS) with machine learning for the rapid, non-destructive detection of SSC and firmness in Korla pears. By analyzing absorbance in the 900–1800 nm range, six preprocessing methods—Savitzky–Golay derivative (SGD), standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky–Golay smoothing (SGS), vector normalization (VN), and min-max normalization (MMN)—were applied to the raw spectral data. uninformative variable elimination (UVE) and successive projections algorithm (SPA) were then used to extract effective wavelengths. Partial least squares regression (PLSR) models were developed for SSC and firmness based on the extracted data. The results showed that all preprocessing and wavelength-extraction methods improved model accuracy. The optimal SSC prediction model was MSC-SPA-PLSR (R = 0.93, RMSE = 0.195), and the best hardness prediction model was MSC-UVE-PLSR (R = 0.83, RMSE = 0.249). This research aids in establishing a non-destructive testing system, offering producers a rapid and accurate quality assessment tool, and provides the food industry with better production control measures to enhance standardization and market competitiveness of Korla pears. Full article
(This article belongs to the Special Issue Non-Destructive Quality Evaluation Methods for Foods)
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23 pages, 18477 KiB  
Article
Simulation Analysis of 3-D Airflow and Temperature Uniformity of Paddy in a Laboratory Drying Oven
by Changzhi Wang, Yongsheng Pei, Zhongqiu Mu, Lin Fan, Jian Kong, Guizhong Tian, Shiyuan Miao, Xiangyi Meng and Hai Qiu
Foods 2024, 13(21), 3466; https://doi.org/10.3390/foods13213466 - 29 Oct 2024
Viewed by 1131
Abstract
This study analyzed the effects of airflow characteristics on the temperature distribution and drying uniformity of paddy during convective drying. Simulations of the drying process with varying airflow inlet and outlet positions were conducted using COMSOL Multiphysics 6.1 software. The determination coefficient ( [...] Read more.
This study analyzed the effects of airflow characteristics on the temperature distribution and drying uniformity of paddy during convective drying. Simulations of the drying process with varying airflow inlet and outlet positions were conducted using COMSOL Multiphysics 6.1 software. The determination coefficient (R2) between the simulated data and experimental values of Sample1 (S1), Sample2 (S2), and Sample3 (S3) was calculated, and its average values were 0.964, 0.963, 0.963, and 0.967, respectively. This study demonstrates that the airflow direction and outlet location have a significant impact on the temperature uniformity of the paddy. The vortex structure generated by the obstruction of the sidewalls and paddy influences both the airflow and temperature distribution within the drying chamber. When the outlet was on the left side and the inlet airflow was in a vertical orientation (VO), the temperature distribution of the paddy exhibited higher temperatures in the edge regions and lower temperatures in the center, with a maximum temperature difference of around 16 °C. The time required for the temperature to reach equilibrium with the outlet positioned on the left was 28.6% shorter than with the outlets positioned in the center or on both sides. Moreover, the temperature uniformity of the three paddy samples was better under this condition. The developed model accurately reflected the paddy drying process. It could also be used to analyze the optimal heating uniformity, providing a technical basis for the design of grain dryers. Full article
(This article belongs to the Special Issue Non-Destructive Quality Evaluation Methods for Foods)
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Review

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36 pages, 1021 KiB  
Review
Advances in Computer Vision and Spectroscopy Techniques for Non-Destructive Quality Assessment of Citrus Fruits: A Comprehensive Review
by Kai Yu, Mingming Zhong, Wenjing Zhu, Arif Rashid, Rongwei Han, Muhammad Safiullah Virk, Kaiwen Duan, Yongjun Zhao and Xiaofeng Ren
Foods 2025, 14(3), 386; https://doi.org/10.3390/foods14030386 - 24 Jan 2025
Viewed by 553
Abstract
Citrus fruits, classified under the Rutaceae family and Citrus genus, are valued for their high nutritional content, attributed to their rich array of natural bioactive compounds. To ensure both quality and nutritional value, precise non-destructive testing methods are crucial. Among these, computer vision [...] Read more.
Citrus fruits, classified under the Rutaceae family and Citrus genus, are valued for their high nutritional content, attributed to their rich array of natural bioactive compounds. To ensure both quality and nutritional value, precise non-destructive testing methods are crucial. Among these, computer vision and spectroscopy technologies have emerged as key tools. This review examines the principles and applications of computer vision technologies—including traditional computer vision, hyperspectral, and multispectral imaging—as well as various spectroscopy techniques, such as infrared, Raman, fluorescence, terahertz, and nuclear magnetic resonance spectroscopy. Additionally, data fusion methods that integrate these technologies are discussed. The review explores innovative uses of these approaches in Citrus quality inspection and grading, damage detection, adulteration identification, and traceability assessment. Each technology offers distinct characteristics and advantages tailored to the specific testing requirements in Citrus production. Through data fusion, these technologies can be synergistically combined, enhancing the accuracy and depth of Citrus quality assessments. Future advancements in this field will likely focus on optimizing data fusion algorithms, selecting effective preprocessing and feature extraction techniques, and developing portable, on-site detection devices. These innovations will drive the Citrus industry toward increased intelligence and precision in quality control. Full article
(This article belongs to the Special Issue Non-Destructive Quality Evaluation Methods for Foods)
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