There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41–1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS–DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS–DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling.

Genetically modified (GM) crops have been widely cultivated across the world and the development of rapid, ultrasensitive, visual multiplex detection platforms that are suitable for field deployment is critical for GM organism regulation. In this study, we developed a novel one-pot system, termed MR-DCA (Multiplex RPA and Dual CRISPR assay), for the simultaneous detection of CaMV35S and NOS genetic targets in GM crops. This innovative approach combined Multiplex RPA (recombinase polymerase amplification) with the Dual CRISPR (clustered regularly interspaced short palindromic repeat) assay technique, to provide a streamlined and efficient method for GM crop detection. The RPA reaction used for amplification CaMV35S and NOS targets was contained in the tube base, while the dual CRISPR enzymes were placed in the tube cap. Following centrifugation, the dual CRISPR (Cas13a/Cas12a) detection system was initiated. Fluorescence visualization was used to measure CaMV35S through the FAM channel and NOS through the HEX channel. When using lateral flow strips, CaMV35S was detected using rabbit anti-digoxin (blue line), whilst NOS was identified using anti-mouse FITC (red line). Line intensity was quantified using Image J and depicted graphically. Detection of the targets was completed in 35 min, with a limit of detection as low as 20 copies. In addition, two analysis systems were developed and they performed well in the MR-DCA assay. In an analysis of 24 blind samples from GM crops with a wide genomic range, MR-DCA gave consistent results with the quantitative PCR method, which indicated high accuracy, applicability and semi-quantitative ability. The development of MR-DCA represents a significant advancement in the field of GM detection, offering a rapid, sensitive and portable method for multiple target detection that can be used in resource-limited environments.

With rapid advancements in breeding technologies, phenomics, and artificial intelligence, crop breeding is progressively advancing toward an era of greater precision and efficiency. In this context, three-dimensional (3D) phenotyping techniques, leveraging their multi-dimensional spatial resolution capabilities, have overcome the limitations of two-dimensional (2D) phenotyping in breeding analysis, enabling precise characterization of crop spatial interactions, plant architecture spatial distribution, and complex 3D structural traits. Recent breakthroughs in computer technology for 3D reconstruction and 3D segmentation have provided robust technical support for crop 3D phenotypic analysis. Furthermore, the effective integration of extracted 3D phenotypic data with genotypic data serves as a powerful tool for future research in crop gene function and genomics-assisted breeding. This review systematically examines the major advances in 3D phenotyping techniques and their representative applications, with particular emphasis on innovations in 3D phenotyping and analysis techniques. Simultaneously, we describe the latest interdisciplinary advances in 3D phenotyping within crop gene function and genomics-assisted breeding research. We objectively evaluate the advantages and limitations of 3D phenotyping compared to 2D approaches to assist breeders in selecting appropriate technologies. Finally, addressing the conceptual challenges in current research, we propose future perspectives for promoting the deep integration of phenomics and breeding technologies. Despite facing technical challenges, it is foreseeable that the cross-disciplinary integration of phenomics and genomics will offer promising prospects for crop breeding.

Low-cost meat, such as duck, is frequently used to adulterate more expensive foods like lamb or beef in many countries. However, the lack of DNA-based reference materials has limited the quality control and detection of adulterants. Here, we report the development and validation of duck genomic DNA certified reference materials (CRMs) through the detection of the duck interleukin 2 (IL2) gene by digital PCR (dPCR) for the identification of duck meat in food products. The certified value of IL2 in CRMs was 5.78 ± 0.51 × 10³ copies/μL with extended uncertainty (coverage factor k = 2) based on IL2 quantification by eight independent collaborating laboratories. Quantification of the mitochondrial gene cytb revealed a concentration of 2.0 × 10⁶ copies/μL, as an information value. The CRMs were also used to determine the limit of detection (LOD) for six commercial testing kits, which confirmed that these kits meet or exceed their claimed sensitivity and are reliable for duck detection.