ArticleScientific reports2017
Discrimination of CRISPR/Cas9-induced mutants of rice seeds using near-infrared hyperspectral imaging.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- CRISPR/Cas-mediated genome editing: playing a versatile role in mitigating the challenges of sustainable rice improvement.3 Biotech · 2025Review
- Haplotype-based insights into seminal root angle in barley.The plant genome · 2025Article
- Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng.Heliyon · 2024Article
- Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network.Plant methods · 2023Article
- Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping.Plant phenomics (Washington, D.C.) · 2023Article
- Insights and protocols for discrimination of sugarcane clones by dissimilarity measures on RGB and NIR data.PloS one · 2023Article
- Near-Infrared Spectroscopy Coupled with Chemometrics and Artificial Neural Network Modeling for Prediction of Emulsion Droplet Diameters.Micromachines · 2022Article
- Discrimination ofInternational journal of molecular sciences · 2022Article
- Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination ofMolecules (Basel, Switzerland) · 2022Article
- Molecular and Computational Strategies to Increase the Efficiency of CRISPR-Based Techniques.Frontiers in plant science · 2022Review
- An Overview of Near Infrared Spectroscopy and Its Applications in the Detection of Genetically Modified Organisms.International journal of molecular sciences · 2021Review
- Hotter, drier, CRISPR: the latest edit on climate change.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
- The Advance of CRISPR-Cas9-Based and NIR/CRISPR-Cas9-Based Imaging System.Frontiers in chemistry · 2021Review
- Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning.Frontiers in bioengineering and biotechnology · 2021Article
- Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2020Article
- Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network.Frontiers in plant science · 2020Article
- Time-Series Chlorophyll Fluorescence Imaging Reveals Dynamic Photosynthetic Fingerprints ofSensors (Basel, Switzerland) · 2019Article
- Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network.RSC advances · 2019Article
- Discrimination ofMolecules (Basel, Switzerland) · 2018Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying individuals with target mutant phenotypes is a significant procedure in mutant exploitation for implementing genome editing technology in a crop breeding programme. In the present study, a rapid and non-invasive method was proposed to identify CRISPR/Cas9-induced rice mutants from their acceptor lines (huaidao-1 and nanjing46) using hyperspectral imaging in the near-infrared (NIR) range (874.41-1733.91 nm) combined with chemometric analysis. The hyperspectral imaging data were analysed using principal component analysis (PCA) for exploratory purposes, and a support vector machine (SVM) and an extreme learning machine (ELM) were applied to build discrimination models for classification. Meanwhile, PCA loadings and a successive projections algorithm (SPA) were used for extracting optimal spectral wavelengths. The SVM-SPA model achieved best performance, with classification accuracies of 93% and 92.75% being observed for calibration and prediction sets for huaidao-1 and 91.25% and 89.50% for nanjing46, respectively. Furthermore, the classification of mutant seeds was visualized on prediction maps by predicting the features of each pixel on individual hyperspectral images based on the SPA-SVM model. The above results indicated that NIR hyperspectral imaging together with chemometric data analysis could be a reliable tool for identifying CRISPR/Cas9-induced rice mutants, which would help to accelerate selection and crop breeding processes.
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