Innovative Chromatographic and Spectroscopic Analytical 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: closed (25 July 2023) | Viewed by 2380

Special Issue Editors


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Guest Editor
Analytical Chemistry Department, GUIA Group, I3A, EINA, University of Zaragoza, María de Luna 3, 50018 Zaragoza, Spain
Interests: analytical chemistry; GC-MS/Q; UPLC/MS-Q-TOF
Special Issues, Collections and Topics in MDPI journals
Department of Analytical Chemistry, University of Zaragoza, Zaragoza, Spain
Interests: analytical chemistry; food packaging; nanomaterials; sensory analysis

Special Issue Information

Dear Colleagues,

Food products are very complex mixtures consisting of naturally occurring compounds and other substances, generally originating from technological processes, agrochemical treatments, or packaging materials. Chromatography and spectroscopy are key analytical techniques in the analysis of food, enabling complex organic substances to be separated and identified.

This Special Issue, entitled “Innovative Chromatographic and Spectroscopic Analytical Methods for Foods”, calls for the latest innovative methodologies applied to food analysis. Works related to food components or food contaminant detection methods for migrants (intentionally and non-intentionally added substances), toxins, pesticides, etc., are welcome, in addition to works related to liquid and/or gas chromatography, mass spectrometry, UV, IR, and Raman spectroscopy. Submissions may take the form of either reviews or research articles.

Dr. Elena Canellas
Dr. Paula Vera
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

  • gas chromatography
  • liquid chromatography
  • UV
  • Raman spectroscopy
  • infrared
  • mass spectrometry
  • food contaminants
  • food components

Published Papers (2 papers)

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Research

15 pages, 2528 KiB  
Article
Identifying the Producer and Grade of Matcha Tea through Three-Dimensional Fluorescence Spectroscopy Analysis and Distance Discrimination
by Yue Xu, Xiangyang Zhou and Wenjuan Lei
Foods 2023, 12(19), 3614; https://doi.org/10.3390/foods12193614 - 28 Sep 2023
Cited by 1 | Viewed by 777
Abstract
The three-dimensional fluorescence spectroscopy features the advantage of obtaining emission spectra at different excitation wavelengths and providing more detailed information. This study established a simple method to discriminate both the producer and grade of matcha tea by coupling three-dimensional fluorescence spectroscopy analysis and [...] Read more.
The three-dimensional fluorescence spectroscopy features the advantage of obtaining emission spectra at different excitation wavelengths and providing more detailed information. This study established a simple method to discriminate both the producer and grade of matcha tea by coupling three-dimensional fluorescence spectroscopy analysis and distance discrimination. The matcha tea was extracted three times and three-dimensional fluorescence spectroscopies of these tea infusions were scanned; then, the dimension of three-dimensional fluorescence spectroscopies was reduced by the integration at three specific areas showing local peaks of fluorescence intensity, and a series of vectors were constructed based on a combination of integrated vectors of the three tea infusions; finally, four distances were used to discriminate the producer and grade of matcha tea, and two discriminative patterns were compared. The results indicated that proper vector construction, appropriate discriminative distance, and correct steps are three key factors to ensure the high accuracy of the discrimination. The vector based on the three-dimensional fluorescence spectroscopy of all three tea infusions resulted in a higher accuracy than those only based on spectroscopy of one or two tea infusions, and the first tea infusion was more sensitive than the other tea infusion. The Mahalanobis distance had a higher accuracy that was up to 100% when the vector is appropriate, while the other three distances were about 60–90%. The two-step discriminative pattern, identifying the producer first and the grade second, showed a higher accuracy and a smaller uncertainty than the one-step pattern of identifying both directly. These key conclusions above help discriminate the producer and grade of matcha in a quick, accurate, and green method through three-dimensional fluorescence spectroscopy, as well as in quality inspections and identifying the critical parameters of the producing process. Full article
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17 pages, 3425 KiB  
Article
A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy
by Shuang Fan, Chaoqi Qin, Zhuopin Xu, Qi Wang, Yang Yang, Xiaoyu Ni, Weimin Cheng, Pengfei Zhang, Yue Zhan, Liangzhi Tao and Yuejin Wu
Foods 2023, 12(16), 2997; https://doi.org/10.3390/foods12162997 - 09 Aug 2023
Viewed by 1340
Abstract
Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer [...] Read more.
Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models’ predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R2p of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer. Full article
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