Statistical Evaluation of Real-Time PCR Protocols Applied to Quantify Genetically Modified Maize

Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning

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.

GMO quantification: valuable experience and insights for the future

Cultivation and marketing of genetically modified organisms (GMOs) have been unevenly adopted worldwide. To facilitate international trade and to provide information to consumers, labelling requirements have been set up in many countries. Quantitative real-time polymerase chain reaction (qPCR) is currently the method of choice for detection, identification and quantification of GMOs. This has been critically assessed and the requirements for the method performance have been set. Nevertheless, there are challenges that should still be highlighted, such as measuring the quantity and quality of DNA, and determining the qPCR efficiency, possible sequence mismatches, characteristics of taxon-specific genes and appropriate units of measurement, as these remain potential sources of measurement uncertainty. To overcome these problems and to cope with the continuous increase in the number and variety of GMOs, new approaches are needed. Statistical strategies of quantification have already been proposed and expanded with the development of digital PCR. The first attempts have been made to use new generation sequencing also for quantitative purposes, although accurate quantification of the contents of GMOs using this technology is still a challenge for the future, and especially for mixed samples. New approaches are needed also for the quantification of stacks, and for potential quantification of organisms produced by new plant breeding techniques.

Comparison of three common DNA concentration measurement methods

Accurate measurement of DNA concentration is important for DNA-based biological applications. DNA concentration is usually determined by the ultraviolet (UV) absorption, fluorescence staining, and diphenylamine reaction methods. However, the best method for quality assurance of measurements is unknown. Here, we comprehensively compared these methods using different types of samples. We found that all three methods accurately determined the concentrations of high-purity DNA solutions. After digestion of DNA samples, concentration measurements revealed that the PicoGreen dye method was very sensitive to the degradation of DNA. The three methods displayed different anti-jamming ability when contaminants such as transfer RNA (tRNA), protein, and organic chemicals were included in DNA solutions. The diphenylamine reaction method gave the highest accuracy, with an average error of approximately 10% between measured and true values. The PicoGreen dye method was influenced by tRNA and protein, and the UV absorption method was susceptible to all kinds of impurities. Overall, the diphenylamine reaction method gave the most accurate results when DNA was mixed with contaminants, the PicoGreen dye method was most suitable for degraded DNA samples or DNA extracted from processed products, and the UV absorbance method was best for evaluating the impurities in DNA solutions.