BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

164 related articles for article (PubMed ID: 35540920)

  • 1. Application of hyperspectral imaging and chemometrics for variety classification of maize seeds.
    Zhao Y; Zhu S; Zhang C; Feng X; Feng L; He Y
    RSC Adv; 2018 Jan; 8(3):1337-1345. PubMed ID: 35540920
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds.
    Bai X; Zhang C; Xiao Q; He Y; Bao Y
    RSC Adv; 2020 Mar; 10(20):11707-11715. PubMed ID: 35496579
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Variety classification of coated maize seeds based on Raman hyperspectral imaging.
    Liu Q; Wang Z; Long Y; Zhang C; Fan S; Huang W
    Spectrochim Acta A Mol Biomol Spectrosc; 2022 Apr; 270():120772. PubMed ID: 34973616
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis.
    Zhao Y; Zhang C; Zhu S; Gao P; Feng L; He Y
    Molecules; 2018 Jun; 23(6):. PubMed ID: 29867071
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds.
    Zhang X; Liu F; He Y; Li X
    Sensors (Basel); 2012 Dec; 12(12):17234-46. PubMed ID: 23235456
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods.
    Wang Z; Huang W; Tian X; Long Y; Li L; Fan S
    Front Plant Sci; 2022; 13():849495. PubMed ID: 35620676
    [TBL] [Abstract][Full Text] [Related]  

  • 7. [Identification of varieties of black bean using ground based hyperspectral imaging].
    Zhang C; Liu F; Zhang HL; Kong WW; He Y
    Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Mar; 34(3):746-50. PubMed ID: 25208405
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed.
    Wang Z; Fan S; Wu J; Zhang C; Xu F; Yang X; Li J
    Spectrochim Acta A Mol Biomol Spectrosc; 2021 Jun; 254():119666. PubMed ID: 33744703
    [TBL] [Abstract][Full Text] [Related]  

  • 9. [Variety recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning].
    Cheng SX; Kong WW; Zhang C; Liu F; He Y
    Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Sep; 34(9):2519-22. PubMed ID: 25532356
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Application of Joint Skewness Algorithm to Select Optimal Wavelengths of Hyperspectral Image for Maize Seed Classification YANG Sai, ZHU Qi-bing*, HUANG Min.
    Yang S; Zhu QB; Huang M
    Guang Pu Xue Yu Guang Pu Fen Xi; 2017 Mar; 37(3):990-6. PubMed ID: 30160845
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network.
    Wu N; Zhang Y; Na R; Mi C; Zhu S; He Y; Zhang C
    RSC Adv; 2019 Apr; 9(22):12635-12644. PubMed ID: 35515879
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging.
    Feng L; Zhu S; Zhang C; Bao Y; Gao P; He Y
    Molecules; 2018 Nov; 23(11):. PubMed ID: 30412997
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning.
    Xu P; Sun W; Xu K; Zhang Y; Tan Q; Qing Y; Yang R
    Foods; 2022 Dec; 12(1):. PubMed ID: 36613360
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging.
    Feng L; Zhu S; Zhang C; Bao Y; Feng X; He Y
    Molecules; 2018 Nov; 23(12):. PubMed ID: 30477266
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties.
    Zhu S; Zhou L; Gao P; Bao Y; He Y; Feng L
    Molecules; 2019 Sep; 24(18):. PubMed ID: 31500333
    [TBL] [Abstract][Full Text] [Related]  

  • 16. [Discrimination of Varieties of Cabbage with Near Infrared Spectra Based on Principal Component Analysis and Successive Projections Algorithm].
    Luo W; Du YZ; Zhang HL
    Guang Pu Xue Yu Guang Pu Fen Xi; 2016 Nov; 36(11):3536-41. PubMed ID: 30198665
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Identification of the variety of maize seeds based on hyperspectral images coupled with convolutional neural networks and subregional voting.
    Zhou Q; Huang W; Tian X; Yang Y; Liang D
    J Sci Food Agric; 2021 Aug; 101(11):4532-4542. PubMed ID: 33452811
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Hyperspectral imaging combined with GA-SVM for maize variety identification.
    Zhang F; Wang M; Zhang F; Xiong Y; Wang X; Ali S; Zhang Y; Fu S
    Food Sci Nutr; 2024 May; 12(5):3177-3187. PubMed ID: 38726456
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis.
    Zhang C; Liu F; He Y
    Sci Rep; 2018 Feb; 8(1):2166. PubMed ID: 29391427
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Sugarbeet Seed Germination Prediction Using Hyperspectral Imaging Information Fusion.
    Wang J; Sun L; Xing W; Feng G; Yang J; Li J; Li W
    Appl Spectrosc; 2023 Jul; 77(7):710-722. PubMed ID: 37246428
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.