Spectroscopic determination of leaf chlorophyll content and color for genetic selection on .

BACKGROUND

Reflectance spectroscopy, like IR, VIS-NIR, combined with chemometric, has been widely used in plant leaf chemical analysis. But less studies have been made on the application of NIR reflectance spectroscopy to plant leaf color and pigments analysis and the possibility of using it for genetic breeding selection. Here, we examine the ability of NIR reflectance spectroscopy to determine the plant leaf color and chlorophyll content in leaves and use the prediction results for genetic selection. Fresh and living tree leaves were used for NIR spectra collection, leaf color parameters (a*, b* and L*) and chlorophyll content were measured with standard analytical methods, partial least squares regression (PLSR) were used for model construction, the coefficient of determination (R) [cross-validation ( ) and validation ( )] and root mean square error (RMSE) [cross-validation (RMSE) and validation (RMSE)] were used for model performance evaluation, significant Multivariate Correlation algorithm was applied for model improvement, to find out the most important region related to the leaf color parameters and chlorophyll model, which have been simulated 100 times for accuracy estimation.

RESULTS

Leaf color parameters (a*, b* and L*) and chlorophyll content were well predicted by NIR reflectance spectroscopy on fresh leaves in vivo. The mean and RMSE of a*, b*, L* and chlorophyll content were (0.82, 4.43), (0.63, 3.72), (0.61, 2.35) and (0.86, 0.13%) respectively. Three most important NIR regions, including 1087, 1215 and 2219 nm, which were highly related to a*, b*, L* and chlorophyll content were found. NIR reflectance spectra technology can be successfully used for genetic breeding program. High heritability of a*, b*, L* and chlorophyll content (  = 0.77, 0.89, 0.78, 0.81 respectively) were estimated. Several families with bright red color or bright yellow color were selected.

CONCLUSIONS

NIR spectroscopy is promising for the rapid prediction of leaf color and chlorophyll content of living fresh leaves. It has the ability to simultaneously measure multiple plant leaf traits, potentially allowing for quick and economic prediction in situ.