Screening for six genetically modified soybean lines by an event-specific multiplex PCR method: Collaborative trial validation of a novel approach for GMO detection

Transgenic poplar (Populus davidiana×P. bolleana Loucne) expressing dsRNA of insect chitinase gene: lines identification and resistance assay

Poplar is a valuable tree species that is distributed all over the world. However, many insect pests infest poplar trees and have caused significant damage. To control poplar pests, we transformed a poplar species, Populus davidiana × P. bolleana Loucne, with the dsRNA of the chitinase gene of a poplar defoliator, Clostera anastomosis (Linnaeus) (Lepidoptera: Notodontidae), employing an Agrobaterium-mediated approach. The transgenic plant has been identified by cloning the T-DNA flanking sequences using TAIL-PCR and quantifying the expression of the dsRNA using qPCR. The toxicity assay of the transgenic poplar lines was carried out by feeding the target insect species (C. anastomosis). The results showed that, in C. anastomosis, the activity of chitinase was significantly decreased, consistent with the expression on mRNA levels, and the larval mortality was significantly increased. These results suggested that the transgenic poplar of dsRNA could be used for pest control.

Rapid Visual LAMP Method for Detection of Genetically Modified Organisms

We developed a novel loop-mediated isothermal amplification (LAMP) method using DNA captured on polyacrylamide microparticles (PAMMPs) as templates (PAMMPs@DNA-LAMP) for rapid qualitative detection of genetically modified organisms (GMOs). Here, DNA was extracted by a fast and cost-effective method using PAMMPs. Four LAMP primers were designed for the PAMMPs@DNA-LAMP method to detect the cauliflower mosaic virus 35S (CaMV35S) promotor in GMOs. We thus developed this method for rapid extraction of DNA (5-10 min) and fast amplification of DNA within ∼30 min at a constant temperature of 63 °C. Moreover, the DNA captured by PAMMPs (PAMMPs@DNA) could be effectively detected by both conventional and quantitative PCR (qPCR) and LAMP. The PAMMPs@DNA-LAMP method was validated with high specificity, sensitivity, and performance for practical sample analysis. This assay detected 0.01% target sequences, which had a high specificity like qPCR and better than the conventional PCR (cPCR). Furthermore, PAMMPs@DNA-LAMP was successfully used to extract and detect DNA from food samples of the major crops (soybean, maize, rice, etc.). In summary, a novel PAMMPs@DNA-LAMP assay has been developed, which has higher sensitivity and spends less time than the cPCR detection using the conventional DNA extracted process. This method offers a novel approach for rapid detection of GMOs in the field.

Real-Time PCR Assay for the Detection and Quantification of Roe Deer to Detect Food Adulteration-Interlaboratory Validation Involving Laboratories in Austria, Germany, and Switzerland

Game meat products are particularly prone to be adulterated by replacing game meat with cheaper meat species. Recently, we have presented a real-time polymerase chain reaction (PCR) assay for the identification and quantification of roe deer in food. Quantification of the roe deer content in % (w/w) was achieved relatively by subjecting the DNA isolates to a reference real-time PCR assay in addition to the real-time PCR assay for roe deer. Aiming at harmonizing analytical methods for food authentication across EU Member States, the real-time PCR assay for roe deer has been tested in an interlaboratory ring trial including 14 laboratories from Austria, Germany, and Switzerland. Participating laboratories obtained aliquots of DNA isolates from a meat mixture containing 24.8% (w/w) roe deer in pork, roe deer meat, and 12 meat samples whose roe deer content was not disclosed. Performance characteristics included amplification efficiency, level of detection (LOD_95%), repeatability, reproducibility, and accuracy of quantitative results. With a relative reproducibility standard deviation ranging from 13.35 to 25.08% (after outlier removal) and recoveries ranging from 84.4 to 114.3%, the real-time PCR assay was found to be applicable for the detection and quantification of roe deer in raw meat samples to detect food adulteration.

Fast Identification of Soybean Seed Varieties Using Laser-Induced Breakdown Spectroscopy Combined With Convolutional Neural Network

Soybean seed purity is a critical factor in agricultural products, standardization of seed quality, and food processing. In this study, laser-induced breakdown spectroscopy (LIBS) as an effective technology was successfully used to identify ten varieties of soybean seeds. We improved the traditional sample preparation scheme for LIBS. Instead of grinding and squashing, we propose a time-efficient method by pressing soybean seeds into rubber sand filled with culture plates through a ruler to ensure a relatively uniform surface height. In our experimental scheme, three LIBS spectra were finally collected for each soybean seed. A majority vote based on three spectra was applied as the final decision judging the attribution of a single soybean seed. The results showed that the support vector machine (SVM) obtained the optimal identification accuracy of 90% in the prediction set. In addition, PCA-ResNet (propagation coefficient adaptive ResNet) and PCSA-ResNet (propagation coefficient synchronous adaptive ResNet) were designed based on typical ResNet structure by changing the way of self-adaption of propagation coefficients. Combined with a new form of input data called spectral matrix, PCSA-ResNet obtained the optimal performance with the discriminate accuracy of 91.75% in the prediction set. T-distributed stochastic neighbor embedding (t-SNE) was used to visualize the clustering process of the extracted features by PCSA-ResNet. For the interpretation of the good performance of PCSA-ResNet coupled with the spectral matrix, saliency maps were further applied to visually show the pixel positions of the spectral matrix that had a significant influence on the discrimination results, indicating that the content and proportion of elements in soybean seeds could reflect the variety differences.