Retrieval of tea polyphenol at leaf level using spectral transformation and multi-variate statistical approach |
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| Institution: | 1. Regional Remote Sensing Centre-East (NRSC), ISRO, Kolkata, West Bengal, India;2. National Remote Sensing Centre, Balanagar, Hyderabad, India;1. Science and Technology on Multi-Spectral Information Processing Laboratory, Huazhong University of Science and Technology, Wuhan 430074, China;2. Department of Physics, Guangdong University of Education, Guangzhou 510303, China;3. The National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China;1. WestCHEM, Department of Pure and Applied Chemistry and Centre for Process Analytics and Control Technology, University of Strathclyde, 295 Cathedral Street, Glasgow, G1 1XL, United Kingdom;2. Hyperspectral Imaging Centre, Department of Electronic and Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow, G1 1XW, United Kingdom;3. Unilever R&D Colworth, Colworth House, Sharnbrook, Bedford, MK44 1LQ, United Kingdom;4. Department of Chemical and Process Engineering, University of Surrey, Guildford GU2 7XH, United Kingdom |
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| Abstract: | 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. |
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| Keywords: | Tea polyphenol Hyperspectral Discriminant analysis Stepwise multiple linear regression Partial least square regression Principal component analysis |
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