A non-destructive, low cost and high throughput colorimetric method for chlorophyll estimation in rice (Oryza sativa L.)

Leaf chlorophyll is an important biochemical parameter used to assess plant health under stress. Chlorophyll is invariably estimated using destructive method of acetone or ethanol extraction. Reflectance spectroscopy such as IR, VIS–NIR, combined with chemometric, has been widely used in plant leaf chemical analysis. Here we report a cheap and reliable spectroscopic method of chlorophyll estimation using a low-cost handheld spectrometer. We standardized the method in a rice diversity panel of 264 genotypes using L*, a* and b* parameters using a randomly taken calibration and validation sample. The parameters used for fitting the regression analysis were L*, a*, b* and δ E that indicates deviation of L*, a* and b* values of sample from the chlorophyll solution of a known concentration. We fitted the regression of total chlorophyll in calibration sample with L*, a*, b* and δ E separately as well as multiple regression with all parameters. The best R2 value (0.671) was observed for a* and the lowest for L* (0.436). Using multiple regression, the R2 value increased to 0.778 (p < 0.005). We used the regression equation for validation sample and obtained R2 value of 0.724 indicating reliable explanatory potential of variables. The parametric test for model reliability indicates good model fit of the observed relationship between chlorophyll content and L*, a*, b* and δ E. The method is cost effective, non-destructive and takes less time (< 10 s) and can be conveniently used for screening large diversity panels especially in case of repeated measurements across growth stages under stress.