Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
5.2
Journals
Foods
Special Issues
The Non-destructive Testing, In Situ Analysis, and Automated Sorting of...
share announcement

The Non-destructive Testing, In Situ Analysis, and Automated Sorting of Food/Agricultural Product Quality

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 564

Special Issue Editors

Prof. Dr. Qiaohua Wang

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: non-destructive intelligent detection; origin traceability of agricultural and livestock products; agricultural electrification and automation; machine vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Meihu Ma

E-Mail Website
Guest Editor
National Research and Development Center for Egg Processing, College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
Interests: films; egg white protein emulsions; food hydrocolloids; egg
Dr. Wei Fan

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: intelligent detection and digital equipment; machine vision

Special Issue Information

Dear Colleagues,

The quality issues surrounding food/agricultural products have long been a focus of public attention, as their quality directly impacts both food safety and economic value. Conventional chemical analysis, while a common method for assessing the quality of food/agricultural products, can identify specific chemical and nutritional components, yet its operation is intricate, cumbersome and time-consuming. Non-destructive testing techniques, including spectroscopy, machine vision, vibration signal analysis, electromagnetic ultrasonic technology, electronic noses and electronic tongues, offer advantages such as non-invasiveness, effective detection, efficiency and simplicity of operation. These techniques are currently emerging as hot topics in the realm of food/agricultural product quality testing. Researchers have also shown considerable interest in furthering the in situ analysis and automation of food/agricultural product sorting. Therefore, this Special Issue aims to explore the application of various non-destructive testing techniques in quality assessment, enabling real-time analysis and automated sorting of food/agricultural products, thereby advancing the resolution of quality-related issues in this domain.

Prof. Dr. Qiaohua Wang
Prof. Dr. Meihu Ma
Dr. Wei Fan
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

  • the quality of food/agricultural products
  • non-destructive inspection
  • in situ analysis
  • automated sorting
  • spectral technology
  • machine vision
  • vibration signal analysis
  • electromagnetic ultrasonic technology
  • electronic nose/electronic tongue

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

14 pages, 3585 KiB  
Article
Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato
by Paresh Chaukhande, Satish Kumar Luthra, R. N. Patel, Siddhant Ranjan Padhi, Pooja Mankar, Manisha Mangal, Jeetendra Kumar Ranjan, Amolkumar U. Solanke, Gyan Prakash Mishra, Dwijesh Chandra Mishra, Brajesh Singh, Rakesh Bhardwaj, Bhoopal Singh Tomar and Amritbir Singh Riar
Foods 2024, 13(11), 1655; https://doi.org/10.3390/foods13111655 - 25 May 2024
Viewed by 284
Abstract
Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used [...] Read more.
Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQexternal), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQexternal values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change. Full article
(This article belongs to the Special Issue The Non-destructive Testing, In Situ Analysis, and Automated Sorting of Food/Agricultural Product Quality)
►▼ 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