Independent component analysis in information extraction from visible/near-infrared hyperspectral imaging data of cucumber leaves

Chemometrics and Intelligent Laboratory Systems

Volumn 104, Issue 2, 2010, Pages 265-270

  Xiaobo, Zou ^a   Jiewen, Zhao ^a   Holmes, Mel ^b   Hanpin, Mao ^a   Jiyong, Shi ^a   Xiaopin, Yin ^a   Yanxiao, Li ^a  

^a JIANGSU UNIVERSITY   (China)

^b UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

Chlorophyll content; Cucumber leaves; Hyperspectral imaging; Independent component analysis; Multi linear regression; Principal component analysis

Indexed keywords

CHLOROPHYLL;

ARTICLE; CALIBRATION; CHLOROPHYLL CONTENT; CONTROLLED STUDY; CUCUMBER; FEASIBILITY STUDY; IMAGE ANALYSIS; IMAGE PROCESSING; INDEPENDENT COMPONENT ANALYSIS; INTERMETHOD COMPARISON; NEAR INFRARED SPECTROSCOPY; NONHUMAN; PHYTOCHEMISTRY; PIGMENTATION; PLANT LEAF; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; VALIDATION PROCESS;

EID: 78650518060     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemolab.2010.08.019     Document Type: Article

References (38)

1. Kokaly R.F., Asner G.P., Ollinger S.V., Martin M.E., Wessman C.A. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens. Environ. 2009, 113:78-91.
2. Sabins F.F. Remote sensing for mineral exploration. Geol. Rev. 1999, 14:157-183.
3. Gao B.-C., Montes M.J., Davis C.O., Goetz A.F.H. Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sens. Environ. 2009, 113:17-24.
4. Verrelst J., Schaepman M.E., Malenovsk Z., Clevers J.G.P.W. Effects of woody elements on simulated canopy reflectance: implications for forest chlorophyll content retrieval. Remote Sens. Environ. 2010, 114:647-656.
5. Sommer S., Hill J., Meier J. The potential of remote sensing for monitoring rural land use changes and their effects on soil conditions. Agric. Ecosyst. Environ. 1998, 67:197-209.
6. Gowen A.A., O'Donnell C.P., Cullen P.J., Downey G., Frias J.M. Hyperspectral imaging - an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 2007, 18:590-598.
7. Gendrin C., Roggo Y., Collet C. Content uniformity of pharmaceutical solid dosage forms by near infrared hyperspectral imaging: a feasibility study. Talanta 2007, 73:733-741.
8. Roggo Y., Edmond A., Chalus P., Ulmschneider M. Infrared hyperspectral imaging for qualitative analysis of pharmaceutical solid forms. Anal. Chim. Acta 2005, 535:79-87.
9. Ravn C., Skibsted E., Bro R. Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches. J. Pharm. Biomed. Anal. 2008, 48:554-561.
10. Zarco-Tejada P.J., Berjie A., Lirez-Lozano R., Miller J.R., Martri P., Cachorro V., Gonzreez M.R., de Frutos A. Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 2005, 99:271-287.
11. Vogt F., Tacke M. Fast principal component analysis of large data sets. Chemom. Intell. Lab. Syst. 2001, 59:1-18.
12. Qiao J., Ngadi M.O., Wang N., Gariiry C., Prasher S.O. Pork quality and marbling level assessment using a hyperspectral imaging system. J. Food Eng. 2007, 83:10-16.
13. Xing J., De Baerdemaeker J. Bruise detection on 'Jonagold' apples using hyperspectral imaging. Postharvest Biol. Technol. 2005, 37:152-162.
14. Ariana D.P., Lu R., Guyer D.E. Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers. Comput. Electron. Agric. 2006, 53:60-70.
15. Tyler B.J., Rayal G., Castner D.G. Multivariate analysis strategies for processing ToF-SIMS images of biomaterials. Biomaterials 2007, 28:2412-2423.
16. Lelong C.C.D., Pinet P.C., Poilv H. Hyperspectral imaging and stress mapping in agriculture: a case study on wheat in Beauce (France). Remote Sens. Environ. 1998, 66:179-191.
17. Caiafa C.F., Salerno E., Proto A.N., Fiumi L. Blind spectral unmixing by local maximization of non-Gaussianity. Signal Process. 2008, 88:50-68.
18. Moussaoui S., Hauksdfitir H., Schmidt F., Jutten C., Chanussot J., Brie D., Dout S., Benediktsson J.A. On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation. Neurocomputer 2008, 71:2194-2208.
19. Katsumata N., Matsuyama Y. Database retrieval for similar images using ICA and PCA bases. Eng. Appl. Artif. Intell. 2005, 18:705-717.
20. Naganathan G.K., Grimes L.M., Subbiah J., Calkins C.R., Samal A., Meyer G.E. Visible/near-infrared hyperspectral imaging for beef tenderness prediction. Comput. Electron. Agric. 2008, 64:225-233.
21. Uno Y., Prasher S.O., Lacroix R., Goel P.K., Karimi Y., Viau A., Patel R.M. Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data. Comput. Electron. Agric. 2005, 47:149-161.
22. Du P.-j., Tan K., Su H.-j. Feature extraction for target identification and image classification of OMIS hyperspectral image. Min. Sci. Technol. (China) 2009, 19:835-841.
23. Hyvärinen A., Karhunen J., Oja E. Independent Component Analysis 2001, Wiley, New York.
24. Antonelli A., Cocchi M., Fava P., Foca G., Franchini G.C., Manzini D., Ulrici A. Automated evaluation of food colour by means of multivariate image analysis coupled to a wavelet-based classification algorithm. Anal. Chim. Acta 2004, 515:3-13.
25. Peng Y., Lu R. Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biol. Technol. 2008, 48:52-62.
26. Lu R., Peng Y. Hyperspectral scattering for assessing peach fruit firmness. Biosystems Eng. 2006, 93:161-171.
27. Galvir L.S., Roberts D.A., Formaggio A.R., Numata I., Breunig F.M. View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data. Remote Sens. Environ. 2009, 113:846-856.
28. Staenz K., Secker J., Gao B.C., Davis C., Nadeau C. Radiative transfer codes applied to hyperspectral data for the retrieval of surface reflectance. ISPRS J. Photogramm. Remote Sens. 2002, 57:194-203.
29. Schulz H.B.M., Baranski R. Potential of NIR-FT-Raman spectroscopy in natural carotenoid analysis. Biopolymers 2005, 77:212-221.
30. Fortuna J., Capson D. Improved support vector classification using PCA and ICA feature space modification. Pattern Recognit. 2004, 37:1117-1129.
31. Estienne F., Despagne F., Walczak B., de Noord O.E., Massart D.L. A comparison of multivariate calibration techniques applied to experimental NIR data sets: part III: robustness against instrumental perturbation conditions. Chemom. Intell. Lab. Syst. 2004, 73:207-218.
32. Chauchard F., Cogdill R., Roussel S., Roger J.M., Bellon-Maurel V. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes. Chemom. Intell. Lab. Syst. 2004, 71:141-150.
33. Huang J., Brennan D., Sattler L., Alderman J., Lane B., O'Mathuna C. A comparison of calibration methods based on calibration data size and robustness. Chemom. Intell. Lab. Syst. 2002, 62:25-35.
34. Zeaiter M., Roger J.M., Bellon-Maurel V., Rutledge D.N. Robustness of models developed by multivariate calibration. Part I: the assessment of robustness. TrAC, Trends Anal. Chem. 2004, 23:157-170.
35. Gitelson A.A., Gritz Y., Merzlyak M.N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 2003, 160:271-282.
36. Gitelson A.A., Merzlyak M.N. Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 1997, 18:2691-2697.
37. Li Q.-B., Huang Y.-W., Zhang G.-J., Zhang Q.-X., Li X., Wu J.-G. Chlorophyll content nondestructive measurement method based on Vis/NIR spectroscopy. Guang Pu Xue Yu Guang Pu Fen Xi/Spectro. & Spect. Analy. 2009, 29:3275-3278.
38. Zou X.B., Zhao J.W., Mao H.P. Genetic algorithm interval partial least squares regression combined successive projections algorithm for variable selection in near-infrared quantitative analysis of pigment in cucumber leaves. Appl. Spectrosc. 2010, 64:786-794.