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Applied Sciences
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Recent Advancements in Non-destructive Technologies for Food Quality and Safety...
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Recent Advancements in Non-destructive Technologies for Food Quality and Safety Assessment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Food Science and Technology".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 1625

Special Issue Editors

Dr. Ebrahiema Arendse

E-Mail Website
Guest Editor
Agricultural Research Council–Vegetables, Industrial and Medicinal Plants, Pretoria 0001, South Africa
Interests: spectroscopy; X-ray computed tomography; chemometrics; postharvest technology; agro-processing
Dr. Lembe Magwaza

E-Mail Website
Guest Editor
School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
Interests: plant physiology; non-destructive technology; near infrared spectroscopy; X-ray computed tomography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global food processing industry is frequently confronted with new technological challenges to meet the increasing demand for quality-assured processed products. Non-destructive technologies have been successfully used over the past decade for the assessment of physical, chemical, and microbial characteristics in the food, beverage, and agro-processing industries. Non-destructive technologies are always improving through the development of better photoelectric sensors and advancements in artificial intelligence, machine learning, and deep learning algorithms.

This Special Issue is intended for the presentation of recent advancements in the field of non-destructive technologies. Authors are invited to submit original high-quality research articles that cover non-destructive technologies and the application of artificial intelligence, machine learning, deep learning, and novel concurrent algorithms used for food quality and safety assessment.

Dr. Ebrahiema Arendse
Dr. Lembe Magwaza
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. Applied Sciences 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 2400 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

  • vibrational spectroscopy
  • infrared spectroscopy
  • Raman spectroscopy
  • hyperspectral imaging
  • X-ray computed tomography
  • magnetic resonance imaging
  • artificial intelligence
  • machine learning and deep learning
  • chemometrics

Published Papers (1 paper)

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Research

17 pages, 3524 KiB  
Article
Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
by Bin Wang, Hua Yang, Shujuan Zhang and Lili Li
Appl. Sci. 2023, 13(5), 3279; https://doi.org/10.3390/app13053279 - 3 Mar 2023
Cited by 3 | Viewed by 1319
Abstract
Detection of skin defects in Cerasus humilis fruit is a critical process to guarantee its quality and price. This study presents a valid method for the detection of defective features in Cerasus humilis fruits based on hyperspectral imaging. A total of 420 sample [...] Read more.
Detection of skin defects in Cerasus humilis fruit is a critical process to guarantee its quality and price. This study presents a valid method for the detection of defective features in Cerasus humilis fruits based on hyperspectral imaging. A total of 420 sample images were acquired that included three types of natural defects and undamaged samples. After acquiring hyperspectral images of Cerasus humilis fruits, the spectral data were extracted from the region of interest (ROI). Five spectral preprocessing methods were used to preprocess the original spectral data, including Savitsky–Golay (S-G), standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction (BC), and de-trending (De-T). Regression coefficient (RC), successive projections algorithm (SPA), and competitive adaptive reweighed sampling (CARS) were conducted to select optimal sensitive wavelengths (SWs); as a result, 11 SWs, 17 SWs, and 13 SWs were selected, respectively. Then, the least squares-support vector machine (LS-SVM) discrimination model was established using the selected SWs. The results showed that the discriminate accuracy of the CARS-LS-SVM method was 91.43%. Based on the characteristics of image information, images corresponding to eight sensitive wavebands (950, 994, 1071, 1263, 1336, 1457, 1542, and 1628 nm) selected by CARS were subjected to principal component analysis (PCA). Then, an effective approach for detecting the defective features was exploited based on the imfill function, canny operator, region growing algorithm, bwareaopen function, and the images of PCA. The location and area of defect feature of 105 Cerasus humilis fruits could be recognized; the detect precision was 88.57%. This investigation demonstrated that hyperspectral imaging combined with an image processing technique could achieve the rapid identification of undamaged samples and natural defects in Cerasus humilis fruit. This provides a theoretical basis for the development of Cerasus humilis fruit grading and sorting equipment. Full article
(This article belongs to the Special Issue Recent Advancements in Non-destructive Technologies for Food Quality and Safety Assessment)
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