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Processes
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Genetically Modified Organisms Testing: Current Technology and Applications
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Genetically Modified Organisms Testing: Current Technology and Applications

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 8487

Special Issue Editors

Dr. Cheng Peng

E-Mail Website
Guest Editor
Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
Interests: molecular detection; sequencing technology; traceability of GMOs; detection of gene-editing plants and its derived food; DNA fingerprinting
Special Issues, Collections and Topics in MDPI journals
Dr. Xuping Feng

E-Mail Website
Guest Editor
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Interests: spectral analysis; transgenic plant classification; plant phenotyping; machine learning; machine vision

Special Issue Information

Dear Colleagues,

Application of genetic transformation to crop has made rapid strides in the past decades to meet some specific requirements, such as enhancement of disease and insect pest tolerance, quality improvement, and increasing nutritional value. The introduction of genetically modified organisms (GMOs) in agro-food markets should be accompanied by a regulatory need to monitor and verify the presence and amount of GM varieties to guarantee consumer safety. Consequently, there is a need of analytical methods for determination, characterization, and authentication of GMOs in crops and derived crop products.

This special issue on “Genetically Modified Organisms Testing” seeks high quality works focusing on the latest novel advances technology for diagnosing GMOs.we are encouraging submission of review or research mauniscripts related to the GMOs testing techniques and applications such as spectral analysis; PCR; enzyme-linked immunosorbent assays; lateral flow strip and microarray etc., for biosecurity and biosafety.

Topics include, but are not limited to:

  • Spectral(near-infrared, mid- infrared, terahertz and/or hyperspectral spectrum) and machine vision application;
  • Plant phenotyping for evaluating transgenic plant;
  • Traceability and detection of GMOs;
  • Risk assessment of GMOs;
  • New detection techniques;

Dr. Cheng Peng
Dr. Xuping Feng
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. Processes 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 2400 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.

Published Papers (6 papers)

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Research

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13 pages, 2972 KiB  
Article
Analysis of the Unintended Effects of the Bacillus thuringiensis Insecticidal Protein in Genetically Modified Rice Using Untargeted Transcriptomics
by Lin Ding, Guanwei Chen, Xiaoyun Chen, Xiaofu Wang, Yuwen Lu, Zehui Liang, Junfeng Xu and Cheng Peng
Processes 2023, 11(11), 3202; https://doi.org/10.3390/pr11113202 - 9 Nov 2023
Viewed by 797
Abstract
The safety and unintended effects of genetically modified (GM) crops have been the focus of public attention. Transcriptome analysis is a powerful tool to assess the potential impact of genetic modification on plant genomes. In this study, three transgenic (KMD, KF6, and TT51-1) [...] Read more.
The safety and unintended effects of genetically modified (GM) crops have been the focus of public attention. Transcriptome analysis is a powerful tool to assess the potential impact of genetic modification on plant genomes. In this study, three transgenic (KMD, KF6, and TT51-1) and three non-transgenic (XS11, MH86, and MH63) rice varieties were assessed at the genomic and protein levels. The results of polymerase chain reaction (PCR) and Cry1Ab/1Ac speed test strips showed that the Bt gene was successfully expressed in transgenic rice. The results of RNA-seq analysis to analyze the unintended effects of transgenic Bt rice showed fewer differentially expressed genes (DEGs) between the transgenic and non-transgenic rice varieties than among the different varieties. Meanwhile, the results of principal component analysis and cluster analysis found no significant genetic variation between the transgenic and non-transgenic rice varieties, except for the presence of Bt in transgenic rice. There were only two co-upregulated DEGs and no co-downregulated DEGs among three comparison groups. Although there were various DEGs among the groups, the two co-upregulated DEGs were not related to any significantly enriched gene ontology (GO) term or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, indicating that the differences among the subgroups were more likely caused by complex environmental or genetic factors, rather than unintended effects due to Bt expression. This study provides useful information to further explore the unexpected effects and safety of GM rice. Full article
(This article belongs to the Special Issue Genetically Modified Organisms Testing: Current Technology and Applications)
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13 pages, 5677 KiB  
Article
Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning
by Yuzhen Wei, Chao Yang, Liu He, Feiyue Wu, Qiangguo Yu and Wenjun Hu
Processes 2023, 11(2), 486; https://doi.org/10.3390/pr11020486 - 6 Feb 2023
Cited by 2 | Viewed by 1489
Abstract
The classification of GM and non-GM maize kernels is fundamental for further analysis of the gene action in maize. Therefore, a complete and novel detection scheme based on near-infrared spectra was designed to distinguish GM and non-GM maize kernels. Hyperspectral images (935–1720 nm) [...] Read more.
The classification of GM and non-GM maize kernels is fundamental for further analysis of the gene action in maize. Therefore, a complete and novel detection scheme based on near-infrared spectra was designed to distinguish GM and non-GM maize kernels. Hyperspectral images (935–1720 nm) of 777 maize kernels from 3 kinds were captured, and the average spectra of the maize kernels were extracted for modeling analysis. The classical modeling methods based on feature engineering were first studied, and the backpropagation neural network–genetic algorithm model showed the best performance with a prediction accuracy of 0.861. Then, novel modeling methods based on deep learning were developed. To dig out the interactive information between different bands and match the application scenarios, the original spectra were transformed into two-dimensional matrices before establishing the deep learning models. A modified convolution neural network (i.e., VGG net) with dilated convolution was finally constructed to classify the maize kernels, and the prediction accuracy reached 0.961. This research provides a referential and novel way to detect GM maize kernels. Future research will improve the detection scheme for monitoring unauthorized GM organisms by introducing the visualization technology of deep learning. Full article
(This article belongs to the Special Issue Genetically Modified Organisms Testing: Current Technology and Applications)
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10 pages, 2812 KiB  
Article
Rapid Identification of Insecticide- and Herbicide-Tolerant Genetically Modified Maize Using Mid-Infrared Spectroscopy
by Xiaodan Liu, Yonghui Yu, Xiulin Bai, Xiaolong Li, Jun Zhang and Dun Wang
Processes 2023, 11(1), 90; https://doi.org/10.3390/pr11010090 - 29 Dec 2022
Cited by 2 | Viewed by 1409
Abstract
Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It [...] Read more.
Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It is particularly necessary to find a fast and accurate method for transgenic product identification. In this research, mid-infrared spectroscopy, coupled with chemometric methods, was applied to discriminate GM maize from its non-GM parent. A total of 120 GM maize and 120 non-GM maize samples were prepared, and the spectral information in the range of 400–4000 cm−1 was collected. After acquiring the spectra, wavelet transform (WT) was used to preprocess the data, and k-means was carried out to split all samples into calibration and prediction sets in the ratio of 2:1. Principal component analysis (PCA) was then conducted to qualitatively distinguish the two types of samples, and an apparent cluster was observed. Since the full spectrum covered a large amount of data and redundant information, we adopted the successive projections algorithm (SPA) to select optimal wavelengths for further analysis. Chemometrics, including partial least squares-discriminant analysis (PLS-DA), the k-nearest neighbor algorithm (KNN), and the extreme learning machine (ELM), were performed to establish classification models based on full spectra and optimal wavelengths. The overall results indicated that ELM models based on full spectra and optimal spectra showed better accuracy and reliability, with a 100% recognition rate in the calibration set and a 98.75% recognition rate in the prediction set. It has been confirmed that mid-infrared spectroscopy, combined with chemometric methods, can be a novel approach to identify transgenic maize. Full article
(This article belongs to the Special Issue Genetically Modified Organisms Testing: Current Technology and Applications)
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12 pages, 701 KiB  
Article
Bt and G10evo-EPSPS Protein Expressed in ZDAB3 Corn Has No Impact on Nutritional Composition and Toxicological Safety
by Xiaoxing Yu, Guo Chen, Ziying Zhou, Xiaoyun Chen, Xiaoyun He, Yue Jiao and Pengfei Wang
Processes 2022, 10(12), 2739; https://doi.org/10.3390/pr10122739 - 19 Dec 2022
Cited by 1 | Viewed by 1341
Abstract
Genetically modified (GM) crops expressing insecticidal and herbicide-tolerant traits provide a new approach to agriculture production, but concerns about food safety were often raised by the public. The present research shows the findings of the nutritional assessment of ZDAB3 expressing insecticidal Cry proteins [...] Read more.
Genetically modified (GM) crops expressing insecticidal and herbicide-tolerant traits provide a new approach to agriculture production, but concerns about food safety were often raised by the public. The present research shows the findings of the nutritional assessment of ZDAB3 expressing insecticidal Cry proteins (Cry1Ab and Cry2Ab) and EPSPS protein (G10evo-EPSPS). The key nutrients and anti-nutrients of ZDAB3 maize were examined and contrasted with those of its non-transgenic control maize grown at the same locations during three planting seasons. The values for proximates, amino acids, fatty acids, minerals, vitamins, phytic acid, and trypsin inhibitor assessed for ZDAB3 were comparable to those of its non-transgenic control maize or within the range of values reported for other commercial lines. In addition, no adverse effects related to the G10evo-EPSPS protein in mammals were observed. These data indicated that the expression of Cry1Ab, Cry2Ab, and G10evo-EPSPS proteins in ZDAB3 maize does not affect the nutritional compositions, and ZDAB3 maize is equivalent to non-transgenic maize regarding those important compositions. Full article
(This article belongs to the Special Issue Genetically Modified Organisms Testing: Current Technology and Applications)
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19 pages, 3670 KiB  
Article
Time-Series Monitoring of Transgenic Maize Seedlings Phenotyping Exhibiting Glyphosate Tolerance
by Mingzhu Tao, Xiulin Bai, Jinnuo Zhang, Yuzhen Wei and Yong He
Processes 2022, 10(11), 2206; https://doi.org/10.3390/pr10112206 - 26 Oct 2022
Cited by 2 | Viewed by 1153
Abstract
Glyphosate is a widely used nonselective herbicide. Probing the glyphosate tolerance mechanism is necessary for the screening and development of resistant cultivars. In this study, a hyperspectral image was used to develop a more robust leaf chlorophyll content (LCC) prediction model based on [...] Read more.
Glyphosate is a widely used nonselective herbicide. Probing the glyphosate tolerance mechanism is necessary for the screening and development of resistant cultivars. In this study, a hyperspectral image was used to develop a more robust leaf chlorophyll content (LCC) prediction model based on different datasets to finally analyze the response of LCC to glyphosate-stress. Chlorophyll a fluorescence (ChlF) was used to dynamically monitor the photosynthetic physiological response of transgenic glyphosate-resistant and wild glyphosate-sensitive maize seedlings and applying chemometrics methods to extract time-series features to screen resistant cultivars. Six days after glyphosate treatment, glyphosate-sensitive seedlings exhibited significant changes in leaf reflection and photosynthetic activity. By updating source domain and transfer component analysis, LCC prediction model performance was improved effectively (the coefficient of determination value increased from 0.65 to 0.84). Based on the predicted LCC and ChlF data, glyphosate-sensitive plants are too fragile to protect themselves from glyphosate stress, while glyphosate-resistant plants were able to maintain normal photosynthetic physiological activity. JIP-test parameters, φE0, VJ, ψE0, and M0, were used to indicate the degree of plant damage caused by glyphosate. This study constructed a transferable model for LCC monitoring to finally evaluate glyphosate tolerance in a time-series manner and verified the feasibility of ChlF in screening glyphosate-resistant cultivars. Full article
(This article belongs to the Special Issue Genetically Modified Organisms Testing: Current Technology and Applications)
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Review

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14 pages, 688 KiB  
Review
Identification of Transgenic Agricultural Products and Foods Using NIR Spectroscopy and Hyperspectral Imaging: A Review
by Jun Zhang, Zihao Liu, Yaoyuan Pu, Jiajun Wang, Binman Tang, Limin Dai, Shuihua Yu and Ruqing Chen
Processes 2023, 11(3), 651; https://doi.org/10.3390/pr11030651 - 21 Feb 2023
Cited by 5 | Viewed by 1699
Abstract
Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other [...] Read more.
Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay. Full article
(This article belongs to the Special Issue Genetically Modified Organisms Testing: Current Technology and Applications)
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