Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.3
Journals
Agriculture
Special Issues
Application of Chromatography and Spectroscopy in Agriculture
share announcement

Application of Chromatography and Spectroscopy in Agriculture

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 2385

Special Issue Editors

Prof. Dr. Elenilson de Godoy Alves Filho

E-Mail Website
Guest Editor
Department of Food Engineering, Federal University of Ceara, Fortaleza 60356-000, Brazil
Interests: infrared; nuclear magnetic resonance; mass spectrometry; chromatography; chemometrics
Special Issues, Collections and Topics in MDPI journals
Dr. Lorena Mara Alexandre e Silva

E-Mail Website
Guest Editor
LMQPN, Embrapa Agroindustria Tropical, Fortaleza 60511-110, Brazil
Interests: infrared; nuclear magnetic resonance; mass spectrometry; chromatography; chemometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In general, due to the complexity of food composition, the use of advanced analytical techniques covering chromatography and spectroscopy analyses is necessary to achieve the most appropriate results. In agriculture, applications of chromatography and spectroscopy are as varied as the studies involving molecular conformation, compound separation and quantification, as well as improved understanding of the metabolomic networks and the subsequent biochemical variability of plants and other biological organisms under induced and natural environment influence. Several agricultural strategies have been employed to improve food quality in terms of chemical compounds and physical and physicochemical characteristics. Along with these advanced techniques, the use of multivariate statistical analysis is becoming increasingly necessary for an appropriate dataset evaluation and prediction of selected food characteristics based on agricultural practices.

This Special Issue focuses on the development of innovative chromatography and spectroscopy methods for the most appropriate evaluation of the influence of different agricultural practices on food composition or characteristics (chemical, physical and physicochemical), which can be related to industrial and consumer interest. Research articles will cover a broad range of agricultural strategies coupled to improved analytical methods. All types of articles, such as original research, opinions, and reviews, are welcome.

Prof. Dr. Elenilson de Godoy Alves Filho
Dr. Lorena Mara Alexandre e Silva
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. Agriculture is an international peer-reviewed open access monthly 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 2600 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

  • infrared
  • nuclear magnetic resonance
  • mass spectrometry
  • chemometrics
  • fingerprinting
  • machine learning
  • regression analysis
  • quality control
  • sustainable chemistry

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

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

15 pages, 3828 KiB  
Article
A Study on Hyperspectral Apple Bruise Area Prediction Based on Spectral Imaging
by Yue Zhang, Yang Li, Xiang Han, Ang Gao, Shuaijie Jing and Yuepeng Song
Agriculture 2023, 13(4), 819; https://doi.org/10.3390/agriculture13040819 - 31 Mar 2023
Viewed by 1690
Abstract
Achieving fast and accurate prediction of the fruit mechanical damage area is important to improve the accuracy and efficiency of apple quality grading. In this paper, the spectral data of all samples in the wavelength range from 376 to 1011 nm were collected, [...] Read more.
Achieving fast and accurate prediction of the fruit mechanical damage area is important to improve the accuracy and efficiency of apple quality grading. In this paper, the spectral data of all samples in the wavelength range from 376 to 1011 nm were collected, the sample set was divided by the physicochemical coeval distance method, and the spectral preprocessing methods were evaluated by establishing a full-wavelength artificial neural network model. The wavelength selection of spectral data was performed by competitive adaptive reweighted sampling, L1 parameter method, and the Pearson correlation coefficient method, and the partial least squares, artificial neural network, and support vector machine (Gaussian kernel) prediction models were established to predict the fruit bruise area size. The surface fitting was performed using the actual apple bruise area, and the regression surface equation of the damage time and damage height of the fruit was established. The results showed that (1) the preprocessing method of first-order difference + SG smoothing can make the prediction model more accurate; (2) the CARS-ANN prediction model has better prediction performance and higher operation efficiency, with the prediction set root mean square error of prediction and R-value of 0.1150 and 0.8675, respectively; (3) the sparrow search algorithm was used to optimize the model, which improved the accuracy of the prediction model. The root mean square error of prediction reached 0.0743 and The R-value reached 0.9739. (4) The relationship between spectral information, bruise area, damage time, and damage degree was obtained by combining the establishment of the fitted surface of the apple bruise area with the prediction model. This study is of application and extension value for the rapid nondestructive prediction of fruit bruise area. Full article
(This article belongs to the Special Issue Application of Chromatography and Spectroscopy in Agriculture)
►▼ 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