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
Rapid Detection and Quality Control of Food
share announcement

Rapid Detection and Quality Control of Food

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

Deadline for manuscript submissions: closed (22 March 2024) | Viewed by 1647

Special Issue Editor

Dr. Fei Ma

E-Mail Website
Guest Editor
School of Food and Biological Engineering, Hefei University of Technology, Hefei, China
Interests: rapid and non-destructive testing; digital synchronous identification technology; intelligent online detection equipment; quality visual early warning system

Special Issue Information

Dear Colleagues,

With the globalization of food trade and food supply chain, food safety has become a hot issue nowadays. Chemical contamination, microbial contamination and adulteration in food have attracted worldwide attention; the complex instrumental operation and sample pre-processing of the traditional detection techniques have great limitations, and they can no longer satisfy today's needs. In the process of monitoring, fast, convenient and accurate analytical detection technology has become the trend of future development, and the development of rapid food testing and quality control has become an important research topic at present. At this time, the application of new materials and methods in the field of food testing has become the latest research direction, and the development and application of new tools for food testing have great prospects for development. This Special Issue aims to provide theoretical guidance for rapid testing and quality control in the food industry and agriculture and welcomes manuscripts dealing with the latest research results in rapid food detection and quality control.

Dr. Fei Ma
Guest Editor

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

  • infrared spectroscopy
  • data mining
  • spectral imaging
  • image processing
  • non-destructive detection
  • acoustic detection technology

Published Papers (2 papers)

Download All Papers
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, 1639 KiB  
Article
The Prediction of Quality Parameters of Craft Beer with FT-MIR and Chemometrics
by Ofelia Gabriela Meza-Márquez, Andrés Ricardo Rodríguez-Híjar, Tzayhri Gallardo-Velázquez, Guillermo Osorio-Revilla and Oswaldo Arturo Ramos-Monroy
Foods 2024, 13(8), 1157; https://doi.org/10.3390/foods13081157 - 11 Apr 2024
Viewed by 398
Abstract
Beer is one of the oldest and most known alcoholic beverages whose organoleptic characteristics are the attributes that the consumer seeks, which is why it is essential to ensure proper quality control of the final product. Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with [...] Read more.
Beer is one of the oldest and most known alcoholic beverages whose organoleptic characteristics are the attributes that the consumer seeks, which is why it is essential to ensure proper quality control of the final product. Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis can be an alternative to traditional methods to predict quality parameters in craft beer. This study aims to develop prediction models based on FT-MIR spectroscopy to simultaneously quantify quality parameters (color, specific gravity, alcohol volume, bitterness, turbidity, pH, and total acidity) in craft beer. Additionally, principal component analysis (PCA) was applied, and it was possible to classify craft beer samples according to their style. Partial least squares (PLS1) developed the best predictive model by obtaining higher R2c (0.9999) values and lower standard error of calibration (SEC: 0.01–0.11) and standard error of prediction (SEP: 0.01–0.14) values in comparison to the models developed with the other algorithms. Specific gravity could not be predicted due to the low variability in the values. Validation and prediction with external samples confirmed the predictive capacity of the developed model. By making a comparison to traditional techniques, FT-MIR coupled with multivariate analysis has a higher advantage, since it is rapid (approximately 6 min), efficient, cheap, and eco-friendly because it does not require the use of solvents or reagents, representing an alternative to simultaneously analyzing quality parameters in craft beer. Full article
(This article belongs to the Special Issue Rapid Detection and Quality Control of Food)
►▼ Show Figures

Figure 1

18 pages, 7788 KiB  
Article
Crater–Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy
by Rongqin Chen, Xiaolong Li, Weijiao Li, Rui Yang, Yi Lu, Zhengkai You and Fei Liu
Foods 2024, 13(7), 1083; https://doi.org/10.3390/foods13071083 - 01 Apr 2024
Viewed by 720
Abstract
Panax notoginseng (P. notoginseng) is a valuable herbal medicine, as well as a dietary food supplement known for its satisfactory clinical efficacy in alleviating blood stasis, reducing swelling, and relieving pain. However, the ability of P. notoginseng to absorb and accumulate [...] Read more.
Panax notoginseng (P. notoginseng) is a valuable herbal medicine, as well as a dietary food supplement known for its satisfactory clinical efficacy in alleviating blood stasis, reducing swelling, and relieving pain. However, the ability of P. notoginseng to absorb and accumulate cadmium (Cd) poses a significant environmental pollution risk and potential health hazards to humans. In this study, we employed laser-induced breakdown spectroscopy (LIBS) for the rapid detection of Cd. It is important to note that signal uncertainty can impact the quantification performance of LIBS. Hence, we proposed the crater–spectrum feature fusion method, which comprises ablation crater morphology compensation and characteristic peak ratio correction (CPRC), to explore the feasibility of signal uncertainty reduction. The crater morphology compensation method, namely, adding variables using multiple linear regression (MLR) analysis, decreased the root-mean-square error of the prediction set (RMSEP) from 7.0233 μg/g to 5.4043 μg/g. The prediction results were achieved after CPRC pretreatment using the calibration curve model with an RMSEP of 3.4980 μg/g, a limit of detection of 1.92 μg/g, and a limit of quantification of 6.41 μg/g. The crater–spectrum feature fusion method reached the lowest RMSEP of 2.8556 μg/g, based on a least-squares support vector machine (LSSVM) model. The preliminary results suggest the effectiveness of the crater–spectrum feature fusion method for detecting Cd. Furthermore, this method has the potential to be extended to detect other toxic metals in addition to Cd, which significantly contributes to ensuring the quality and safety of agricultural production. Full article
(This article belongs to the Special Issue Rapid Detection and Quality Control of Food)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop