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In recent years, the rapid development of genetically modified (GM) technology has raised concerns about the safety of GM crops and foods for human health and the ecological environment. Gene flow from GM crops to other crops, especially in the Brassicaceae family, might pose a threat to the environment due to their weediness. Hence, finding reliable, quick, and low-cost methods to detect and monitor the presence of GM crops and crop products is important. In this study, we used visible near-infrared (Vis-NIR) spectroscopy for the effective discrimination of GM and non-GM Brassica napus, B. rapa, and F1 hybrids (B. rapa X GM B. napus). Initially, Vis-NIR spectra were collected from the plants, and the spectra were preprocessed. A combination of different preprocessing methods (four methods) and various modeling approaches (eight methods) was used for effective discrimination. Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy rate (100%). The use of a Convolutional Neural Network with Normalization resulted in 98.9%. The same higher accuracy was found in the use of Gradient Boosted Trees and Fast Large Margin approaches. Later, phenolic acid concentration among the different plants was assessed using GC-MS analysis. Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes. The results showed that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used for the effective discrimination of GM and non-GM B. napus, B. rapa, and F1 hybrids. Biochemical composition analysis can also be combined with the Vis-NIR spectra for efficient discrimination.

Brown mustard (Brassica juncea (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four B. juncea varieties in Korea. The spectra from the leaves of four different growth stages of four B. juncea varieties were measured in the Vis-NIR range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky–Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating B. juncea varieties in the field in all the growth stages.

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Interspecific hybridization between transgenic crops and their wild relatives is a major concern for transgene dispersal in the environment. Under controlled conditions, artificial hand pollination experiments were performed in order to assess the hybridization potential and the fitness of interspecific hybrids between Brassica rapa and genetically modified (GM) Brassica napus. Initially, six subspecies of B. rapa were hybridized with GM B. napus through hand pollination. In the resulting F1 hybrids, the combination of B. rapa ssp. narinosa (♀) × GM B. napus (♂) had the highest crossability index (16.9 ± 2.6). However, the F1 selfing progenies of B. rapa ssp. rapa (♀) × GM B. napus were found to be more effective in producing viable future generations with the highest crossability index (1.6 ± 0.69) compared to other subspecies. Consequently, they were used for the generation of F2 and F3 progenies. The 18 different morphological characteristics among the parental cross-combinations and F1 hybrid progenies were measured and visualized through hierarchical clustering. Different generations were found to be grouped based on their different morphological characteristics. The chromosome numbers among the interspecific hybrids ranged from 2n = 29 to 2n = 40. Furthermore, the SSR markers revealed the presence of genomic portions in the hybrids in comparison with their parental lines. There is a high possibility of transgene flow between GM B. napus and B. rapa. The study concluded that the interspecific hybrids between B. napus and B. rapa can be viable and can actively hybridize up to F3 generations and more. This suggests that the GM B. napus can disperse the transgene into B. rapa, and that it can pass through for several generations by hand pollination in a greenhouse environment.

In nature, interspecific hybridization occurs frequently and can contribute to the production of new species or the introgression of beneficial adaptive features between species. It has great potential in agricultural systems to boost the process of targeted crop improvement. In the advent of genetically modified (GM) crops, it has a disadvantage that it involves the transgene escaping to unintended plants, which could result in non-specific weedy crops. Several crop species in the

The occurrence of exotic weeds and their influx into farmlands due to climate change poses many problems. Therefore, it is necessary to generate a prediction model for the occurrence pattern of these exotic weeds based on scientific evidence and devise prevention measures. The photosynthetic apparatus is known as the most temperaturesensitive component of a plant cell and its initial response to temperature stress is to inhibit the activation of photosystem II. This study investigated the potential of OJIP transients in assessing temperature stress in exotic weeds. The four exotic weeds currently flowing into Korean farmlands include Amaranthus spinosus, Conyza bonariensis, Crassocephalum crepidioides, and Amaranthus viridis. These weeds were treated at 5°C, 10°C, 15°C, 20°C, 25°C, 30°C, 35°C, and 40°C and the OJIP curves and JIP parameters were measured and analyzed. The results showed that heat and chilling stress affected the photosystem II (PSII) electron transport of A. spinosus, whereas C. crepidioides and A. viridis were more affected by high-temperature stress than by low-temperature stress. Lastly, C. bonariensis showed resistance to both high and low-temperature stress. The results of this study suggest that OJIP transients and JIP parameters can be used to analyze damage to the photosynthetic apparatus by temperature stress and that they can serve as sensitive indicators for the occurrence pattern of exotic weeds.

The rapid advancement of genetically modified (GM) technology over the years has raised concerns about the safety of GM crops and foods for human health and the environment. Gene flow from GM crops may be a threat to the environment. Therefore, it is critical to develop reliable, rapid, and low-cost technologies for detecting and monitoring the presence of GM crops and crop products. Here, we used visible near-infrared (Vis-NIR) spectroscopy to distinguish between GM and non-GM Brassica napus, B. juncea, and F1 hybrids (B. juncea X GM B. napus). The Vis-NIR spectra were preprocessed with different preprocessing methods, namely normalization, standard normal variate, and Savitzky–Golay. Both raw and preprocessed spectra were used in combination with eight different chemometric methods for the effective discrimination of GM and non-GM plants. The standard normal variate and support vector machine combination was determined to be the most accurate model in the discrimination of GM, non-GM, and hybrid plants among the many combinations (99.4%). The use of deep learning in combination with Savitzky–Golay resulted in 99.1% classification accuracy. According to the findings, it is concluded that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used to distinguish between GM and non-GM B. napus, B. juncea, and F1 hybrids.

Simple Summary With the advent of genetic engineering technology, the development and cultivation of genetically modified (GM) crops has increased. They were mainly developed for high yielding, herbicide resistance, and tolerance against different biotic and abiotic stresses. Rapeseed, also known as canola, was developed mainly for herbicide resistance and to increase the production of canola oil. Since it forms weedy, feral populations and has a proven ability to hybridize with its close relatives, it is important to manage the GM crops’ cultivation and spread, especially the rapeseed. Several studies have reported that the spread of GM rapeseed in non-GM fields and road verges is possible due to transport and agronomic practices, and it may become a weed. Hence, in this review, we summarized the cases of unintentional spread of feral GM rapeseed in the fields and road verges. In addition, we made recommendations for the effective management of feral GM and non-GM rapeseed in agricultural fields and along roads. Abstract Globally, the cultivation area of genetically modified (GM) crops is increasing dramatically. Despite their well-known benefits, they may also pose many risks to agriculture and the environment. Among the various GM crops, GM rapeseed (Brassica napus L.) is widely cultivated, mainly for oil production. At the same time, B. napus possesses a number of characteristics, including the ability to form feral populations and act as small-seeded weeds, and has a high potential for hybridization with other species. In this review, we provide an overview of the commercialization, approval status, and cultivation of GM rapeseed, as well as the status of the feral rapeseed populations. In addition, we highlight the case studies on the unintentional environmental release of GM rapeseed during transportation in several countries. Previous studies suggest that the main reason for the unintentional release is seed spillage during transport/importing of rapeseed in both GM rapeseed-cultivating and -non-cultivating countries. Despite the fact that incidents of unintentional release have been recorded often, there have been no reports of serious detrimental consequences. However, since rapeseed has a high potential for hybridization, the possibilities of gene flow within the genus, especially with B. rapa, are relatively significant, and considering their weedy properties, effective management methods are needed. Hence, we recommend that specific programs be used for the effective monitoring of environmental releases of GM rapeseed as well as management to avoid environmental and agricultural perturbations.

Near-infrared spectroscopy (NIRS) has become a more popular approach for quantitative and qualitative analysis of feeds, foods and medicine in conjunction with an arsenal of chemometric tools. This was the foundation for the increased importance of NIRS in other fields, like genetics and transgenic monitoring. A considerable number of studies have utilized NIRS for the effective identification and discrimination of plants and foods, especially for the identification of genetically modified crops. Few previous reviews have elaborated on the applications of NIRS in agriculture and food, but there is no comprehensive review that compares the use of NIRS in the detection of genetically modified organisms (GMOs). This is particularly important because, in comparison to previous technologies such as PCR and ELISA, NIRS offers several advantages, such as speed (eliminating time-consuming procedures), non-destructive/non-invasive analysis, and is inexpensive in terms of cost and maintenance. More importantly, this technique has the potential to measure multiple quality components in GMOs with reliable accuracy. In this review, we brief about the fundamentals and versatile applications of NIRS for the effective identification of GMOs in the agricultural and food systems.

BACKGROUND: This study investigated the effects of rice genetically modified to be resistant against rice blast and rice bacterial blight on the soil microbial community. A comparative analysis of the effects of rice genetically modified rice choline kinase (OsCK1) gene for disease resistance (GM rice) and the Nakdong parental cultivar (non-GM rice) on the soil microbial community at each stage was conducted using rhizosphere soil of the OsCK1 and Nakdong rice. METHODS AND RESULTS: The soil chemistry at each growth stage and the bacterial and fungal population densities were analyzed. Soil DNA was extracted from the samples, and the microbial community structures of the two soils were analyzed by pyrosequencing. No significant differences were observed in the soil chemistry and microbial population density between the two soils. The taxonomic analysis showed that Chloroflexi, Proteobacteria, Firmicutes, Actinobacteria, and Acidobacteria were present in all soils as the major phyla. Although the source tracking analysis per phylogenetic rank revealed that there were differences in the bacteria between the GM and non-GM soil as well as among the cultivation stages, the GM and non-GM soil were grouped according to the growth stages in the UPGMA dendrogram analysis. CONCLUSION: The difference in bacterial distributions between Nakdong and OsCK1 rice soils at each phylogenetic level detected in microbial community analysis by pyrosequencing may be due to the genetic modification done on GM rice or due to heterogeneity of the soil environment. In order to clarify this, it is necessary to analyze changes in root exudates along with the expression of transgene. A more detailed study involving additional multilateral soil analyses is required.

Isoflavones are ecophysiologically active secondary metabolites derived from the phenylpropanoid pathway. They were mostly found in leguminous plants, especially in the pea family. Isoflavones play a key role in plant–environment interactions and act as phytoalexins also having an array of health benefits to the humans. According to epidemiological studies, a high intake of isoflavones-rich diets linked to a lower risk of hormone-related cancers, osteoporosis, menopausal symptoms, and cardiovascular diseases. These characteristics lead to the significant advancement in the studies on genetic and metabolic engineering of isoflavones in plants. As a result, a number of structural and regulatory genes involved in isoflavone biosynthesis in plants have been identified and characterized. Subsequently, they were engineered in various crop plants for the increased production of isoflavones. Furthermore, with the advent of high-throughput technologies, the regulation of isoflavone biosynthesis gains attention to increase or decrease the level of isoflavones in the crop plants. In the review, we begin with the role of isoflavones in plants, environment, and its benefits in human health. Besides, the main theme is to discuss the updated research progress in metabolic engineering of isoflavones in other plants species and regulation of production of isoflavones in soybeans.

Bacterial communities in rhizosphere and root nodules have significant contributions to the growth and productivity of the soybean (Glycine max (L.) Merr.). In this report, we analyzed the physiological properties and dynamics of bacterial community structure in rhizosphere and root nodules at different growth stages using BioLog EcoPlate and high-throughput sequencing technology, respectively. The BioLog assay found that the metabolic capability of rhizosphere is in increasing trend in the growth of soybeans as compared to the bulk soil. As a result of the Illumina sequencing analysis, the microbial community structure of rhizosphere and root nodules was found to be influenced by the variety and growth stage of the soybean. At the phylum level, Actinobacteria were the most abundant in rhizosphere at all growth stages, followed by Alphaproteobacteria and Acidobacteria, and the phylum Bacteroidetes showed the greatest change. But, in the root nodules Alphaproteobacteria were dominant. The results of the OTU analysis exhibited the dominance of Bradyrhizobium during the entire stage of growth, but the ratio of non-rhizobial bacteria showed an increasing trend as the soybean growth progressed. These findings revealed that bacterial community in the rhizosphere and root nodules changed according to both the variety and growth stages of soybean in the field.

The nationwide weed survey was conducted in arable land, paddy field, upland, orchard and pasture, over whole country of Korea during 3 years from 2013 to 2015. Also, these survey were conducted in order to determine a change of weed community and to identify a major dominant weed species, and problem weeds. Weeds of arable land were composed of 619 species belonging to 81 families. Weeds occurred in paddy field were 90 species belonging to 28 families, upland weeds were 375 species of 50 families, orchard weeds were 492 species of 63 families and pasture weeds 275 species of 52 families. Among total 81 families, the Asteraceae (15.5%) was the biggest family, followed by Poaceae (13.1%), Fabaceae (5.5%), Cyperaceae (5.2%), Polygonaceae (6.3%), respectively. And these weed species were composed of annual weeds of 209 species (33.8%), winter annual of 102 species (16.4%) and perennial weeds of 308 species (49.8%). Exotic weeds naturalized in Korea were investigated as 166 species. Weed flora in Korean cultivation area have been increased as much as 13 families and 186 species for 10 years compared to 2000~2004 weed survey result of 68 families and 433 species. The increment could be resulted from the change of weed population such as increase of the herbicide-resistant and the difference of investigation method.