DOI: 10.1016/j.jag.2014.11.001
Scopus记录号: 2-s2.0-84957604061
论文题名: Retrieval of tea polyphenol at leaf level using spectral transformation and multi-variate statistical approach
作者: Dutta D ; , Das P ; K ; , Bhunia U ; K ; , Singh U ; , Singh S ; , Sharma J ; R ; , Dadhwal V ; K
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 36 起始页码: 22
结束页码: 29
语种: 英语
英文关键词: Discriminant analysis
; Hyperspectral
; Partial least square regression
; Principal component analysis
; Stepwise multiple linear regression
; Tea polyphenol
Scopus关键词: discriminant analysis
; leaf
; least squares method
; multispectral image
; multivariate analysis
; phenolic compound
; principal component analysis
; regression analysis
; spectral reflectance
; Assam
; India
; Camellia sinensis
英文摘要: In the present study, field based hyperspectral data was used to estimate the tea (Camellia sinensis L.) polyphenol at Deha Tea garden of Assam state, India. Leaf reflectance spectra were first filtered for noise and then transformed into normalized and first derivative reflectance for further analysis. Stepwise discriminant analysis was carried out to select sensitive bands for a range of polyphenol concentration by minimizing the effects of other factors such as age of the bushes and management practices. The wavelengths at 358, 369, 484, 845, 916, 1387, 1420, 1435, 1621 and 2294 nm were identified as sensitive to tea polyphenol, among which 2294 nm was found to be the most recurring band. The noise removed selected bands, their transformed derivatives and principal components were regressed with the tea polyphenol using univariate and multi-variate analysis. In univariate analysis the correlation was very poor with RMSE more than 3.0. A significant improvement in R2 values were observed when multivariate analyses like stepwise multiple linear regression (SMLR) and partial least square regression (PLSR) was carried out. The PLSR of first derivative reflectance was most accurate (R2 = 0.81 and RMSE = 1.39 mg g-1) among all the uni- and multivariate analysis for predicting the polyphenol of fresh tea leaves. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79600
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: Regional Remote Sensing Centre-East (NRSC), ISRO, Kolkata, West Bengal, India; National Remote Sensing Centre, Balanagar, Hyderabad, India
Recommended Citation:
Dutta D,, Das P,K,et al. Retrieval of tea polyphenol at leaf level using spectral transformation and multi-variate statistical approach[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,36