Research graph
References from Scan-level acquisition dependence and validation bias in batch near-infrared hyperspectral imaging: a two-instrument study using background spectra. Local targets link to admitted publications; unresolved targets remain external evidence.
Hyperspectral imaging
10.1038/s43586-026-00470-x · 2026 · External reference
Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning
10.1364/oe.406036 · 2020 · External reference
Optimization of pre-processing and data fusion strategies for multi-block spectroscopic characterization of cellular growth phases in the chlorophyte, Tetraselmis suecica
10.1016/j.chemolab.2023.104985 · 2023 · External reference
Hyperspectral imaging for seed quality and safety inspection: a review
10.1186/s13007-019-0476-y · 2019 · External reference
Longitudinal volumetric assessment of inflammatory arthritis via photoacoustic imaging and doppler ultrasound imaging
10.1016/j.pacs.2023.100514 · 2023 · External reference
Automated deep learning-based finger joint segmentation in 3-D ultrasound images with limited dataset
10.1177/01617346241277178 · 2025 · External reference
SwinDAF3D: pyramid Swin transformers with deep attentive features for automated finger joint segmentation in 3D ultrasound images for rheumatoid arthritis assessment
10.3390/bioengineering12040390 · 2025 · External reference
Confocal hyperspectral microscopic imager for the detection and classification of individual microalgae
10.1364/oe.438253 · 2021 · External reference
Multi-mode microscopic hyperspectral imager for the sensing of biological samples
10.3390/app10144876 · 2020 · External reference
Reliability analysis of photoluminescence measurement data from the intrinsic limitation on the instrument to the practical limitations during the experiments
10.1016/j.saa.2025.127107 · 2026 · External reference
Incoherent broadband cavity-enhanced absorption spectroscopy for sensitive measurement of nutrients and microalgae
10.1364/ao.449467 · 2022 · External reference
Machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager
10.1016/j.saa.2021.120054 · 2021 · External reference
Determination of geographic origins and types of Lindera aggregata samples using a portable short-wave infrared hyperspectral imager
10.1016/j.saa.2022.121370 · 2022 · External reference
Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping
10.1186/s13007-024-01315-y · 2024 · External reference
An automatic 3-D ultrasound and photoacoustic combined imaging system for human inflammatory arthritis
10.1109/tuffc.2023.3290824 · 2023 · External reference
Research on variety identification of common bean seeds based on hyperspectral and deep learning
10.1016/j.saa.2024.125212 · 2025 · External reference
A lightweight dual-channel feature fusion model for wheat variety identification in hyperspectral images
10.1016/j.saa.2026.127595 · 2026 · External reference
Research on nondestructive detection of sweet-waxy corn seed varieties and mildew based on stacked ensemble learning and hyperspectral feature fusion technology
10.1016/j.saa.2024.124816 · 2024 · External reference
Research on multi modal corn seed vitality grading based on three branch cross attention fusion network
10.1016/j.saa.2026.127828 · 2026 · External reference
Covariate balanced allocation of samples to batches to mitigate the impacts of technical variability
10.1093/bioadv/vbag048 · 2026 · External reference
Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling
10.1038/s41596-021-00620-3 · 2021 · External reference
Machine learning of Raman spectroscopic data: comparison of different validation strategies
10.1002/jrs.6842 · 2025 · External reference
Being aware of data leakage and cross-validation scaling in chemometric model validation
10.1002/cem.70026 · 2025 · External reference
On the sampling strategy for evaluation of spectral-spatial methods in hyperspectral image classification
10.1109/tgrs.2016.2616489 · 2017 · External reference
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
10.1111/ecog.02881 · 2017 · External reference
Nondestructive identification of barley seeds variety using near-infrared hyperspectral imaging coupled with convolutional neural network
10.1111/jfpe.13821 · 2021 · External reference
Mapping hyperspectral NIR images using supervised self-organizing maps: discrimination of weedy rice seeds
10.1016/j.microc.2023.108599 · 2023 · External reference
A rapid classification method for sorghum seed varieties based on HSI and PCA-SICNN algorithm
10.1016/j.microc.2024.111095 · 2024 · External reference
Deterioration of orthodox seeds during ageing: influencing factors, physiological alterations and the role of reactive oxygen species
10.1016/j.plaphy.2020.11.031 · 2021 · External reference
The role of redox-active small molecules and oxidative protein post-translational modifications in seed aging
10.1016/j.plaphy.2024.108810 · 2024 · External reference
Integration of hyperspectral imaging, non-targeted metabolomics and machine learning for vigour prediction of naturally and accelerated aged sweetcorn seeds
10.1016/j.foodcont.2023.109930 · 2023 · External reference
Modelling the vigour of maize seeds submitted to artificial accelerated ageing based on ATR-FTIR data and chemometric tools (PCA, HCA and PLS-DA)
10.1016/j.heliyon.2020.e03477 · 2020 · External reference
Identification of maize seed vigor under different accelerated aging times using hyperspectral imaging and spectral deep features
10.1016/j.compag.2025.109980 · 2025 · External reference
Viability discrimination and vigor estimation of naturally aged open-pollinated maize seeds via hyperspectral imaging
10.1016/j.microc.2025.116758 · 2026 · External reference
Unresolved reference
2025 · External reference
Multi-level data fusion strategy based on spectral and image information for identifying varieties of soybean seeds
10.1016/j.saa.2024.124815 · 2024 · External reference
Review of the most common pre-processing techniques for near-infrared spectra
10.1016/j.trac.2009.07.007 · 2009 · External reference
A well-conditioned estimator for large-dimensional covariance matrices
10.1016/s0047-259x(03)00096-4 · 2004 · External reference
Partial least squares for discrimination
10.1002/cem.785 · 2003 · External reference
Support-vector networks
10.1023/a:1022627411411 · 1995 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External reference
Convolutional neural networks for vibrational spectroscopic data analysis
10.1016/j.aca.2016.12.010 · 2017 · External reference
Deep residual learning for image recognition
2016 · External reference
Squeeze-and-excitation networks
2018 · External reference
Attention is all you need
2017 · External reference
Similarity of neural network representations revisited, in: proceedings of the 36th international conference on machine learning
2019 · External reference
Multi-level block permutation
10.1016/j.neuroimage.2015.05.092 · 2015 · External reference
The cluster bootstrap consistency in generalized estimating equations
10.1016/j.jmva.2012.09.003 · 2013 · External reference
Compensating illumination variations in hyperspectral single-pixel imaging
10.1016/j.optcom.2026.133029 · 2026 · External reference
Research on automatic spectral calibration algorithm for echelle spectrometer
10.1016/j.optcom.2024.130663 · 2024 · External reference
Leakage and the reproducibility crisis in machine-learning-based science
10.1016/j.patter.2023.100804 · 2023 · External reference
REFORMS: consensus-based recommendations for machine-learning-based science
10.1126/sciadv.adk3452 · 2024 · External reference
Being aware of data leakage and cross-validation scaling in chemometric model validation
10.1002/cem.70026 · ExternalCitation · doi-reference
Partial least squares for discrimination
10.1002/cem.785 · ExternalCitation · doi-reference
Machine learning of Raman spectroscopic data: comparison of different validation strategies
10.1002/jrs.6842 · ExternalCitation · doi-reference
Convolutional neural networks for vibrational spectroscopic data analysis
10.1016/j.aca.2016.12.010 · ExternalCitation · doi-reference
Optimization of pre-processing and data fusion strategies for multi-block spectroscopic characterization of cellular growth phases in the chlorophyte, Tetraselmis suecica
10.1016/j.chemolab.2023.104985 · ExternalCitation · doi-reference
Identification of maize seed vigor under different accelerated aging times using hyperspectral imaging and spectral deep features
10.1016/j.compag.2025.109980 · ExternalCitation · doi-reference
Integration of hyperspectral imaging, non-targeted metabolomics and machine learning for vigour prediction of naturally and accelerated aged sweetcorn seeds
10.1016/j.foodcont.2023.109930 · ExternalCitation · doi-reference
Modelling the vigour of maize seeds submitted to artificial accelerated ageing based on ATR-FTIR data and chemometric tools (PCA, HCA and PLS-DA)
10.1016/j.heliyon.2020.e03477 · ExternalCitation · doi-reference
The cluster bootstrap consistency in generalized estimating equations
10.1016/j.jmva.2012.09.003 · ExternalCitation · doi-reference
Mapping hyperspectral NIR images using supervised self-organizing maps: discrimination of weedy rice seeds
10.1016/j.microc.2023.108599 · ExternalCitation · doi-reference
A rapid classification method for sorghum seed varieties based on HSI and PCA-SICNN algorithm
10.1016/j.microc.2024.111095 · ExternalCitation · doi-reference
Viability discrimination and vigor estimation of naturally aged open-pollinated maize seeds via hyperspectral imaging
10.1016/j.microc.2025.116758 · ExternalCitation · doi-reference
Multi-level block permutation
10.1016/j.neuroimage.2015.05.092 · ExternalCitation · doi-reference
Research on automatic spectral calibration algorithm for echelle spectrometer
10.1016/j.optcom.2024.130663 · ExternalCitation · doi-reference
Compensating illumination variations in hyperspectral single-pixel imaging
10.1016/j.optcom.2026.133029 · ExternalCitation · doi-reference
Longitudinal volumetric assessment of inflammatory arthritis via photoacoustic imaging and doppler ultrasound imaging
10.1016/j.pacs.2023.100514 · ExternalCitation · doi-reference
Leakage and the reproducibility crisis in machine-learning-based science
10.1016/j.patter.2023.100804 · ExternalCitation · doi-reference
Deterioration of orthodox seeds during ageing: influencing factors, physiological alterations and the role of reactive oxygen species
10.1016/j.plaphy.2020.11.031 · ExternalCitation · doi-reference
The role of redox-active small molecules and oxidative protein post-translational modifications in seed aging
10.1016/j.plaphy.2024.108810 · ExternalCitation · doi-reference
Machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager
10.1016/j.saa.2021.120054 · ExternalCitation · doi-reference
Determination of geographic origins and types of Lindera aggregata samples using a portable short-wave infrared hyperspectral imager
10.1016/j.saa.2022.121370 · ExternalCitation · doi-reference
Multi-level data fusion strategy based on spectral and image information for identifying varieties of soybean seeds
10.1016/j.saa.2024.124815 · ExternalCitation · doi-reference
Research on nondestructive detection of sweet-waxy corn seed varieties and mildew based on stacked ensemble learning and hyperspectral feature fusion technology
10.1016/j.saa.2024.124816 · ExternalCitation · doi-reference
Research on variety identification of common bean seeds based on hyperspectral and deep learning
10.1016/j.saa.2024.125212 · ExternalCitation · doi-reference
Reliability analysis of photoluminescence measurement data from the intrinsic limitation on the instrument to the practical limitations during the experiments
10.1016/j.saa.2025.127107 · ExternalCitation · doi-reference
A lightweight dual-channel feature fusion model for wheat variety identification in hyperspectral images
10.1016/j.saa.2026.127595 · ExternalCitation · doi-reference
Research on multi modal corn seed vitality grading based on three branch cross attention fusion network
10.1016/j.saa.2026.127828 · ExternalCitation · doi-reference
Review of the most common pre-processing techniques for near-infrared spectra
10.1016/j.trac.2009.07.007 · ExternalCitation · doi-reference
A well-conditioned estimator for large-dimensional covariance matrices
10.1016/s0047-259x(03)00096-4 · ExternalCitation · doi-reference
Random forests
10.1023/a:1010933404324 · ExternalCitation · doi-reference
Support-vector networks
10.1023/a:1022627411411 · ExternalCitation · doi-reference
Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling
10.1038/s41596-021-00620-3 · ExternalCitation · doi-reference
Hyperspectral imaging
10.1038/s43586-026-00470-x · ExternalCitation · doi-reference
Covariate balanced allocation of samples to batches to mitigate the impacts of technical variability
10.1093/bioadv/vbag048 · ExternalCitation · doi-reference
On the sampling strategy for evaluation of spectral-spatial methods in hyperspectral image classification
10.1109/tgrs.2016.2616489 · ExternalCitation · doi-reference
An automatic 3-D ultrasound and photoacoustic combined imaging system for human inflammatory arthritis
10.1109/tuffc.2023.3290824 · ExternalCitation · doi-reference
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
10.1111/ecog.02881 · ExternalCitation · doi-reference
Nondestructive identification of barley seeds variety using near-infrared hyperspectral imaging coupled with convolutional neural network
10.1111/jfpe.13821 · ExternalCitation · doi-reference
REFORMS: consensus-based recommendations for machine-learning-based science
10.1126/sciadv.adk3452 · ExternalCitation · doi-reference
Automated deep learning-based finger joint segmentation in 3-D ultrasound images with limited dataset
10.1177/01617346241277178 · ExternalCitation · doi-reference
Hyperspectral imaging for seed quality and safety inspection: a review
10.1186/s13007-019-0476-y · ExternalCitation · doi-reference
Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping
10.1186/s13007-024-01315-y · ExternalCitation · doi-reference
Incoherent broadband cavity-enhanced absorption spectroscopy for sensitive measurement of nutrients and microalgae
10.1364/ao.449467 · ExternalCitation · doi-reference
Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning
10.1364/oe.406036 · ExternalCitation · doi-reference
Confocal hyperspectral microscopic imager for the detection and classification of individual microalgae
10.1364/oe.438253 · ExternalCitation · doi-reference
Multi-mode microscopic hyperspectral imager for the sensing of biological samples
10.3390/app10144876 · ExternalCitation · doi-reference
SwinDAF3D: pyramid Swin transformers with deep attentive features for automated finger joint segmentation in 3D ultrasound images for rheumatoid arthritis assessment
10.3390/bioengineering12040390 · ExternalCitation · doi-reference
