Classifying cultivars of rice (Oryza sativa L.) based on corrected canopy reflectance spectra data using the orthogonal projections to latent structures (O-PLS) method

Chemometrics and Intelligent Laboratory Systems

Volumn 115, Issue , 2012, Pages 25-36

  Lin, Wen Shin ^a   Yang, Chwen Ming ^b   Kuo, Bo Jein ^c  

^a NATIONAL PINGTUNG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b TAIWAN AGRICULTURAL RESEARCH INSTITUTE   (Taiwan)

^c NATIONAL CHUNG HSING UNIVERSITY   (Taiwan)

Author keywords

Canopy reflectance spectra data; Orthogonal projections to latent structures (O PLS); Partial least square for discriminant analysis (PLS DA); Rice cultivar

Indexed keywords

ACCURACY; ARTICLE; CALIBRATION; CANOPY REFLECTANCE SPECTROSCOPY; CULTIVAR IDENTIFICATION; DISCRIMINANT ANALYSIS; GENOTYPE; GROWTH CURVE; HIGH RESOLUTION REFLECTANCE SPECTROSCOPY; NOISE; NONHUMAN; ORTHOGONAL PROJECTION TO LATENT STRUCTURE ANALYSIS; PARTIAL LEAST SQUARES REGRESSION; PLANT DEVELOPMENT; PRIORITY JOURNAL; RICE; SPECIES COMPARISON; SPECTROSCOPY; STATISTICAL ANALYSIS; VALIDATION PROCESS; VEGETATIVE STAGE;

EID: 84861191046     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2012.04.005     Document Type: Article

References (74)

1. Zhou J., Ma C., Xu H., Yuan K., Lu X., Zhu Z., Wu Y., Xu G. Metabolic profiling of transgenic rice with crylAc and sck genes: an evaluation of unintended effects at metabolic level by using GC-F1D and GC-MS. J. Chromatogr. B 2009, 877:725-732.
2. Wiegand C., Richardson A. Leaf area, light interception, and yield estimates from spectral components analysis. Agron. J. 1984, 16:543-546.
3. Næs T., Irgens C., Martens H. Comparison of linear statistical methods for calibration of NIR instruments. Appl. Stat. 1986, 35:195-206.
4. Inoue Y., Moran M.S., Horie T. Analysis of spectral measurements in paddy field for predicting rice growth and yield based on a simple crop simulation model. Plant Prod. Sci. 1998, 1:269-279.
5. Chang J., Clay D.E., Dalsted K., Clay S., O'Neill M. Corn (Zea mays L.) yield prediction using multispectral and multidate reflectance. Agron. J. 2003, 95:1447-1453.
6. Royo C., Aparicio N., Villegas D., Casadesus J., Monneveux P., Araus J.L. Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions. Int. J. of Remote Sens. 2003, 24:4403-4419.
7. Reich G. Near-infrared spectroscopy and imaging: basic principles and pharmaceutical applications. Adv. Drug Deliv. Rev. 2005, 57:1109-1143.
8. Larraín M., Guesalaga A.R., Agosín E. A multipurpose portable instrument for determining ripeness in wine grapes using NIR spectroscopy. IEEE Trans. Instrum. Meas. 2008, 57:294-302.
9. Patel N.K., Singh T.P., Sahai B., Patel M.S. Spectral response of rice crop and its relation to yield and yield attributes. Int. J. of Remote Sens. 1985, 6:657-664.
10. Shibayama M., Akiyama T. Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sens. Environ. 1991, 36:45-53.
11. Shibayama M., Munakata K. A spectroradiometer for field use. IV. Radiometric prediction of grain yields for ripening rice plants. Jpn. J. Crop Sci. 1986, 55:53-59.
12. Wiegand C., Shibayama M., Yamagata Y., Akiyama T. Spectral observations for estimating the growth and yield of rice. Jpn. J. Crop Sci. 1989, 58:673-683.
13. Nguyen H.T., Lee B.W. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. Eur. J. Agron. 2006, 24:349-356.
14. Rao N.R. Development of a crop-specific spectral library and discrimination of various agricultural crop varieties using hyperspectral imagery. Int. J. of Remote Sens. 2008, 29:131-144.
15. Pisharoty P.R. Introduction to remote sensing. Proc. Indian Acad. Sci. Eng. Sci. 1983, 6:97-107.
16. Hatfield J.L., Gitelson A.A., Schepers J.S., Walthall C.L. Application of spectral remote sensing for agronomic decisions. Agron. J. 2008, 100:117-131.
17. Wang Y.P., Chang K.W., Chen R.K., Lo J.C., Shen Y. Large area rice yield forecasting using satellite imageries. Int. J. Appl. Earth Observation & Geoinformation 2010, 12:27-35.
18. Casanova D., Epema G.F., Goudriaan J. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Res. 1998, 55:83-92.
19. Peñuelas J., Filella I. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Sci. 1998, 3:151-156.
20. Wright D.L., Rasmussen V.P., Ramsey R.D., Baker R.D., Ellsworth W. Canopy reflectance estimation of wheat nitrogen content for grain protein management. GIScience Remote Sens. 2004, 41:287-300.
21. Xue L.H., Cao W.X., Luo W.H., Dai T.B., Zhu Y. Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agron. J. 2004, 96:135-142.
22. Ferrio J., PVillegas D., Zarco J., Aparicio N., Araus J.L., Royo C. Assessment of durum wheat yield using visible and near-infrared reflectance spectra of canopies. Field Crops Res. 2005, 94:126-148.
23. Huang H., Yu H., Xu H., Ying Y. Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. J. Agr. Food Chem. 2008, 87:303-313.
24. Zwiggelaar R. A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops. Crop Prot. 1998, 17:189-206.
25. Li X., He Y. Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks. Biosyst. Eng. 2008, 99:313-321.
26. Houts M.E., Lee R., Price K.P., Egbert S.L., Martinko E.A. Using 10-meter Digital Airborne Imagery to Classify Crop Types in Iowa 2000, Proceedings of the Second International Conference on Geospatial Information in Agriculture and, Forestry.
27. Chen R.K., Yang C.M. Discriminate rice cultivars using canopy reflectance spectra. Crop, Environment & Bioinformatics 2004, 1:239-250.
28. Nguyen D., Rocke D.M. Partial least squares proportional hazard regression for application to DNA microarray data. Bioinformatics 2002, 18:1625-1632.
29. Baek J., Kim M. Face recognition using partial least squares components. Pattern Recogn. 2004, 37:1303-1306.
30. Jiang J.H., Tsenkova R., Ozaki Y. Principal discriminant variate method for classification of multicollinear data: principle and application. Anal. Sci. 2001, 17:i471-i474.
31. Tan Y., Shi L., Tong W., Wang C. Multi-class cancer classification by total principal component regression (PCR) using microarray gene expression data. Nucleic Acids Res. 2005, 33:56-65.
32. H. Nocairi, EI M. Qannari, E. Vigneau, D. Bertrand, Discrimination on latent components with respect to patterns. Application to multicollinear data, Comput. Stat. Data An. 48 (2005) 139-147.
33. Liu Y.S., Rayens W. PLS and dimension reduction for classification. Computation. Stat. 2007, 22:189-208.
34. Boulesteix A.L. PLS dimension reduction for classification of microarray data. Stat. Appl. Genet. Mol. Biol. 2004, 3:1-30.
35. Trygg J. O2-PLS for qualitative and quantitative analysis in multivariate calibration. J. Chem. 2002, 16:283-293.
36. Bylesjö B., Rantalainen M., Cloarec O., Nicholson J., Holmes E., Trygg J. OPLS discriminant analysis, combining the strengths of PLS-DA and SIMCA classification. J. Chem. 2006, 20:341-351.
37. Barker M., Rayens W. Partial least squares for discrimination. J. Chem. 2003, 17:166-173.
38. J.J. Dai, L. Lieu, D. Rocke, Dimension reduction for classification with gene expression data, Stat. Appl. Genet. Mol. Biol. 5 (2006) Article 6.
39. Demetriades-Shah T.H., Steven M., Clark J.A. High resolution derivative spectra in remote sensing. Remote Sens. Environ. 1990, 33:55-64.
40. Shibayama M., Takahashi W., Morinaga S., Akiyama T. Canopy water deficit detection in paddy rice using a high resolution field spectroradiometer. Remote Sens. Environ. 1993, 45:117-126.
41. Dhanoa M.S., Lister S.J., Sanderson R., Barnes R.J. The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. J. Near Infrared Spectrosc. 1994, 2:43-47.
42. Datt B. Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid in eucalyptus leaves. Remote Sens. Environ. 1998, 66:111-121.
43. Verron T., Sabatier R., Joffre R. Some theoretical properties of the O-PLS method. J. Chem. 2004, 18:62-68.
44. Candolfi A., De Maesschalck R., Jouan-Rimbaud D., Hailey P.A., Massart D.L. The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra. J. of Pharm. Biomed. Anal. 1999, 21:115-132.
45. Yee N. Potato crisp moisture estimation using near infrared spectroscopy. Int. J. Food Prop. 2001, 4:247-260.
46. Elvidge C.D., Lyon R.J.P. Influence of rock-soil spectral variation in assessment of green biomass. Remote Sens. Environ. 1985, 17:265-279.
47. Huete A.R., Jackson R.D., Post D.F. Spectral response of a plant canopy with different soil backgrounds. Remote Sens. Environ. 1985, 17:37-53.
48. Ma B.L., Dwyer L.M., Costa C., Cober E.R., Morrison M.J. Early prediction of soybean yield from canopy reflectance measurements. Agron. J. 2001, 93:1227-1234.
49. Chang K.W., Shen Y., Lo J.C. Predicting rice yield using canopy reflectance measured at booting stage. Agron. J. 2005, 97:872-878.
50. Trygg J., Wold S. Orthogonal projections to latent structures (O-PLS). J. Chem. 2002, 16:119-128.
51. Eriksson L., Johansson E., Kettaneh-Wold N., Trygg J., Wikström C., Wold S. Multi- and Megavariate Data Analysis Part II: Advanced Applications and Method Extensions 2006, Umetrics AB, Sweden.
52. Cloarec O., Dumas M.E., Craig A., Barton R.H., Trygg J., Hudson J., Blancher C., Gauguier D., Lindon J.C., Holmes E., Nicholson J. Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal. Chem. 2005, 77:1282-1289.
53. Crockford D.J., Lindon J.C., Cloarec O., Plumb R.S., Bruce S.J., Zirah S., Rainville P., Stumpf C.L., Johnson K., Holmes E., Nicholson J.K. Statistical search space reduction and two-dimensional data display approaches for UPLC-MS in biomarker discovery and pathway analysis. Anal. Chem. 2006, 78:4398-4408.
54. Trygg J., Holmes E., Lundstedt T. Chemometrics in metabonomics. J. Proteome. Res. 2007, 6:469-479.
55. Hedenström M., Wiklund S., Sundberg B., Edlund U. Visualization and interpretation of OPLS models based on 2D NMR data. Chem. Intell. Lab. Syst. 2008, 92:110-117.
56. Whelehan O.P., Earll M.E., Johansson M.E., Toft M., Eriksson L. Detection of ovarian cancer using chemometric analysis of proteomic profiles. Chem. Intell. Lab. Syst. 2006, 84:82-87.
57. Stenlund H., Johansson E., Gottfries J., Trygg J. Unlocking interpretation in near infrared multivariate calibrations by orthogonal partial least squares. Anal. Chem. 2009, 81:203-209.
58. Y. Gu, Y. Zhang, Y. Liu, Unmixing component analysis for anomaly detection in hyperspectral imagery, 2006 IEEE International Conference on Image Processing, Atlanta, GA, USA, 2006, p.p 965-968.
59. Martens H., Næs T. Multivariate Calibration 1989, John Wiley and Sons, New York, USA.
60. Martens H., Martens M. Multivariate Analysis of Quality: An Introduction 2000, John Wiley and Sons, New York, USA.
61. Defernez M., Kemsley E.K. The use and misuse of chemometrics for treating classification problems. Trends Anal. Chem. 1997, 16:216-221.
62. Tominaga Y. Comparative study of class data analysis with PCA-LDA, SIMCA, PLS, ANNs, and k-NN. Chem. Intell. Lab. Syst. 1999, 49:105-115.
63. Wold S., Antti H., Lindgren F., Ohman J. Orthogonal signal correction of near-infrared spectra. Chem. Intell. Lab. Syst. 1998, 44:175-185.
64. Stenlund H., Gorzsás A., Persson P., Sundberg B., Trygg J. Orthogonal projections to latent structures discriminant analysis modeling on in situ FT-IR spectral imaging of liver tissue for identifying sources of variability. Anal. Chem. 2008, 80:6898-6906.
65. Yang C.M., Chen R.K. Modeling rice growth with hyperspectral reflectance data. Crop Sci. 2004, 44:1283-1290.
66. Fiella I., Penuelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int. J. Remote Sens. 1994, 15:1459-1470.
67. Sims D.A., Gamon J.A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 2002, 81:337-354.
68. Karimi G., Ghorbanli M., Heidari H., Khavarinejad R.A., Assareh M.H. The effects of NaCl on growth, water relations, osmolytes and ion content in Kochia prostrate. Biol. Plant. 2005, 49:301-304.
69. N. Abu-Khalaf, B. Bennedsen, G. Bjorn, Distinguishing carrot's characteristics by near infrared (NIR) reflectance and multivariate data analysis, In: Agricultural engineering international: The CIGR Journal of Scientific Research and Development, 2004.
70. Shafri H.Z.M., Salleh M.A.M., Ghiyamat A. Hyperspectral remote sensing of vegetation using red edge position techniques. Am. J. Appl. Sci. 2006, 3:1864-1871.
71. Xu Z., Chen W., Zhang W., Ma D., Xu H. A preliminary analysis on difference of nadir reflectance of canopy and its affecting factors in different rice varieties. Scientia Agricultura Sinica 2008, 41:2868-2872. (in Chinese).
72. Annual Report of Rice Improvement 2000, Development of the Tainung No. 71. Taichung District Agricultural Research and Extension Station, Taichung, Taiwan (R.O.C.), 2000, pp. 1-31. (in Chinese).
73. T.F. Lin, The breeding of Taichung Sen No. 10. Bulletin of Taichung Districit Agraicultral Improvement Station 3 (1980) 1-6. (in Chinese).
74. Lee C.Y., Liu W.T., Ting C.H., Cheng M.C., Chen C.C., Tseng T.H., Liu D.J. Development of the new rice variety Taiken 16. Bulletin of the Hualien District Agricultural Improvement Station 1998, 16:1-22. (in Chinese).