Nanotechnology and Food Safety

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Quality and Safety".

Deadline for manuscript submissions: closed (25 September 2022) | Viewed by 5026

Special Issue Editor

School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: spectroscopy; chemometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the crucial role of food safety in human health and economic development around the world is being increasingly acknowledged. In light of this, research in the food industry should keep food safety as a top priority. The pursuit of food safety covers a wide range of processes, including production on farms, processing, distribution, storage, selection, preparation, and consumption.

The advent of nanotechnology has resulted in greater opportunities and diverse novel technological advancements, particularly in the area of food safety issues that have hampered progress, society, and the food industry at large. As a multifaceted technique of manipulating matter at the nanoscale, nanotechnology is concerned with processes at a resolution of between 1 and 100 nanometers and altering the properties of nanoparticles to develop new properties that are useful for a range of innovative applications. With its cost-effective, simple, and rapid system with high sensitivity and accuracy, nanotechnology has great potential to solve the ever-growing threat of persistent food safety issues, as it can offer an on-site method for detecting and quantifying contaminants in food samples.

For this Special Issue of Foods, we are inviting the submission of manuscripts focusing on research that applies nanotechnology in realizing the whole chain detection of food safety. Topics featured in this Special Issue include nanotechnology and its applications in food safety and the chemistry of food additives, contaminants, and other indicators together with their metabolism, toxicology, and food fate. In light of the diverse nature of this field, we will also be particularly focusing on the safety issues and regulatory concerns surrounding nanoprocessed food products. The potential applications of nanosensors for pathogens detection in food. However, fundamental questions must be addressed with regard to high-performance, low-toxic nanomaterials.

Our aim is to gather all the new information in this field and include it in this Special Issue “Nanotechnology and Food Safety”. We invite researchers to contribute original and unpublished research and review articles on this topic.

Dr. Huanhuan Li
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

  • food safety
  • nanotechnology
  • nanosensors
  • low toxicity
  • high sensitivity
  • food additives
  • contaminants
  • processing
  • nanomaterials

Published Papers (3 papers)

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Research

14 pages, 2449 KiB  
Article
A Novel Colorimetric Sensor Array Coupled Multivariate Calibration Analysis for Predicting Freshness in Chicken Meat: A Comparison of Linear and Nonlinear Regression Algorithms
by Wenhui Geng, Suleiman A. Haruna, Huanhuan Li, Hafizu Ibrahim Kademi and Quansheng Chen
Foods 2023, 12(4), 720; https://doi.org/10.3390/foods12040720 - 07 Feb 2023
Cited by 4 | Viewed by 1456
Abstract
As a source of vital nutrients for the normal functioning of the body, chicken meat plays an important role in promoting good health. This study examines the occurrence of total volatile basic nitrogen (TVB-N) as an index for evaluating freshness, using novel colorimetric [...] Read more.
As a source of vital nutrients for the normal functioning of the body, chicken meat plays an important role in promoting good health. This study examines the occurrence of total volatile basic nitrogen (TVB-N) as an index for evaluating freshness, using novel colorimetric sensor arrays (CSA) in combination with linear and nonlinear regression models. Herein, the TVB-N was determined by steam distillation, and the CSA was fabricated via the use of nine chemically responsive dyes. The corresponding dyes utilized, and the emitted volatile organic compounds (VOCs) were found to be correlated. Afterwards, the regression algorithms were applied, assessed, and compared, with the result that a nonlinear model based on competitive adaptive reweighted sampling coupled with support vector machines (CARS-SVM) achieved the best results. Accordingly, the CARS-SVM model provided improved coefficient values (Rc = 0.98 and Rp = 0.92) based on the figures of merit used, as well as root mean square errors (RMSEC = 3.12 and RMSEP = 6.75) and a ratio of performance deviation (RPD) of 2.25. Thus, this study demonstrated that the CSA paired with a nonlinear algorithm (CARS-SVM) could be employed for fast, noninvasive, and sensitive detection of TVB-N concentration in chicken meat as a major indicator of freshness in meat. Full article
(This article belongs to the Special Issue Nanotechnology and Food Safety)
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13 pages, 3868 KiB  
Article
Cell-Based Metabolomics Approach for Anticipating and Investigating Cytotoxicity of Gold Nanorods
by Jian Ji, Jiadi Sun, Yinzhi Zhang and Xiulan Sun
Foods 2022, 11(22), 3569; https://doi.org/10.3390/foods11223569 - 09 Nov 2022
Cited by 1 | Viewed by 1166
Abstract
Despite the increasing application of gold nanoparticles, there has been little assessment of biological system toxicity to evaluate their potential impact on human health. In this study, the human hepatoma cell line (Hep G2) was used in a metabolomics approach to study the [...] Read more.
Despite the increasing application of gold nanoparticles, there has been little assessment of biological system toxicity to evaluate their potential impact on human health. In this study, the human hepatoma cell line (Hep G2) was used in a metabolomics approach to study the effects of shape, time, and dose of gold nanorods (GNRs). Using optimized parameters for chromatography and mass spectrometry, the metabolites detected by GC-MS were processed with MS DIAL and identified with Fiehnlib. Key metabolic pathways affected by GNRs were identified by endo-metabolic profiling of cells mixed with GNRs of varying shape while varying the dose and time of exposure. The shape of GNRs affected cytotoxicity, and short GNR (GNR-S) triggered disorder of cell metabolism. High concentrations of GNRs caused more significant toxicity. The cytotoxicity and bioTEM results illustrated that the mitochondria toxicity, as the main cytotoxicity of GNRs, caused declining cytoprotective ability. The mitochondrial dysfunction disrupted alanine, aspartate, glutamate, arginine, and proline metabolism, with amino acid synthesis generally downregulated. However, the efflux function of cells can exclude GNRs extracellularly within 24 h, resulting in reduced cell mitochondrial metabolic toxicity and allowing metabolic disorders to recover to normal function. Full article
(This article belongs to the Special Issue Nanotechnology and Food Safety)
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11 pages, 3046 KiB  
Article
Preparation of Mesoporous Silica Nanosphere-Doped Color-Sensitive Materials and Application in Monitoring the TVB-N of Oysters
by Binbin Guan, Fuyun Wang, Hao Jiang, Mi Zhou and Hao Lin
Foods 2022, 11(6), 817; https://doi.org/10.3390/foods11060817 - 12 Mar 2022
Cited by 2 | Viewed by 1812
Abstract
In this work, a new colorimetric sensor based on mesoporous silica nanosphere-modified color-sensitive materials was established for application in monitoring the total volatile basic nitrogen (TVB-N) of oysters. Firstly, mesoporous silica nanospheres (MSNs) were synthesized based on the improved Stober method, then the [...] Read more.
In this work, a new colorimetric sensor based on mesoporous silica nanosphere-modified color-sensitive materials was established for application in monitoring the total volatile basic nitrogen (TVB-N) of oysters. Firstly, mesoporous silica nanospheres (MSNs) were synthesized based on the improved Stober method, then the color-sensitive materials were doped with MSNs. The “before image” and the “after image” of the colorimetric senor array, which was composed of nanocolorimetric-sensitive materials by a charge-coupled device (CCD) camera were then collected. The different values of the before and after image were analyzed by principal component analysis (PCA). Moreover, the error back-propagation artificial neural network (BP-ANN) was used to quantitatively predict the TVB-N values of the oysters. The correlation coefficient of the colorimetric sensor array after being doped with MSNs was greatly improved; the Rc and Rp of BP-ANN were 0.9971 and 0.9628, respectively when the principal components (PCs) were 10. Finally, a paired sample t-test was used to verify the accuracy and applicability of the BP-ANN model. The result shows that the colorimetric-sensitive materials doped with MSNs could improve the sensitivity of the colorimetric sensor array, and this research provides a fast and accurate method to detect the TVB-N values in oysters. Full article
(This article belongs to the Special Issue Nanotechnology and Food Safety)
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