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155. Tea polyphenols 2%
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| RMSEP = 4.30
os | P=08 |
RMSEP/mean = 3.0%
120 ‘
120 130 140 150 160
Measured (mg g ‘)
Figure 3. Relationships between the predicted and measured
total tea polyphenols using a hybrid of neural networks and
SPA variable selections, according to the test dataset (n=16).
Total tea polyphenols
10 +
i **^— enm that are significantly differt svith the
~~ 9 minimum value through F-test (p«0.05i
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È i minimum value through F-test (p» 0.05)
2 7 1 (O Optimal number of wavelength
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i T
+ min
3 : ; :
e 5 10 15 20 25
Figure 4. Choice ofthe optimal number of wavelength (circled
positions) by successive projections algorithm for the prediction
of tea polyphenols. The criterion is to find a minimum number
of wavelengths for which the errors is not significantly larger
than the lowest one. This is determined by an F-test.
4. DISCUSSION AND CONCLUSION
The utility of reflectance spectroscopy for predicting tea
quality-related biochemicals at canopy level is demonstrated in
this paper (figure 2 and 3). In previous studies, some phenolic
substances of tea, including total tea polyphenols, catechins,
epigallocatechin gallate (EGCG) and epicatechin (EC) have
been successfully estimated using near infrared spectra for dried
tea powders and dried leaves (Chen et al., 2008; Luypaert et al.,
2003). Our results revealed that foliar chemical concentrations
of tea (total tea polyphenols) can be retrieved not only from the
spectra of dried powders, but also for living tea plant material.
Prediction accuracy decreased using canopy spectra, compared
to reported results using powder spectra. The variability of
spectra reflectance of dried tea powders are mainly correlated
with the amounts of chemical compounds, as the effect of
absorption by water is reduced considerably and the effect of
leaf cell structure could be impaired (Curran et al, 2001;
Kokaly and Clark, 1999). At the canopy level, reflectance
variability is due to additional factors such as LAI (leaf area
index), foliar water content and canopy architecture (Gitelson
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
et al., 2003; Kokaly et al., 2009). This may be the main reason
of relatively lower prediction accuracy for tea plants.
Our study showed that partial least squares regression is an
effective method to retrieve biochemical parameters from
canopy spectral reflectance of tea plant (figure 2). Predictive
models based on partial least squares regression produced
satisfactory accuracy. This results is consistent with those of
Darvishzadeh et al. (2008) who in a field experimental study on
green grass reported a better predictive performance of PLS
regression analysis compared with for biophysical and
biochemical parameters estimation. PLS has the potential to
exploit the rich information content of hyperspectral data.
It also demonstrated that the quality of tea can be predicted with
satisfactory accuracy from the hyperspectral data at canopy
level, using artificial neural networks in combination with
successive projections algorithm (figure 3). Based on the
optimal wavelengths selected by the successive projections
algorithm, neural networks worked well for the prediction of
total tea polyphenols using the canopy spectra of tea plants: the
relative root mean square errors (RMSEP/mean) were less than
10% on an independent test dataset. The goal of SPA solution
is to find a small representative set of spectral variables with an
emphasis on the minimization of collinearity (figure 4). Our
results confirm recent studies that have successfully applied the
successive projections algorithm for the predicting biochemical
concentrations in vegetation science (Liu and He, 2009).
Considering that data collected in the field or in a greenhouse
were under natural atmospheric and illumination conditions, this
research has demonstrated that there is potential to use
reflectance spectra to predict in situ tea quality in space and
time. As our experiment was carried out at canopy level using
field spectrometer, when using airborne or spaceborne
hyperspectral remote sensing, the retrieval of biochemical
parameters for tea plants may be more difficult, as biochemical
absorption features may be affected by complex environmental
factors such as atmospheric and topographic effects .
The following conclusions were drawn from this study:
(1) Our results suggest that biochemical components (total tea
polyphenols) of tea quality can be quantitatively estimated from
canopy spectroscopy. The canopy spectra may have the
potential to predict the foliar biochemical concentration of tea.
(2) When up-scaling to canopy level, predicting total tea
polyphenols was achieved with lower accuracy compared to
reported results in literatures in which dried or ground powder
spectra have been used.
(3) partial least squares regression is able to locate surrogate
spectral features for estimating the concentration of fresh leaf
biochemicals of tea.
(4) A novel integrated approach proposed in our study,
involving a forward selection algorithm (successive projections
algorithm) to choose the optimal number of wavelengths and
neural networks can be used for a better simulation of nonlinear
relationship between biochemical concentration and spectral
signatures of tea canopy.
In summary, the successful chemical estimation from canopy
spectra shows the possibility of using hyperspectral remote
sensing (air or space-borne sensors) to predict tea quality
quantitatively and non-destructively at landscape or regional
scales before its plucking, based on the methodology described
in this paper.
Inte
Referenc
Araujo,
Card, D.I
Cho, M.
Curran, F
Curran, F
Darvishz:
Davey, !
Gitelson,
Knox, N.
Kokaly,
Lera, G
Lin, G.F
Liu, F. ai
More,
Mutanga,
