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

BackgroundReflectance 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 Sassafras tzumu 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 (R2) [cross-validation ($${\text{R}}^{2}_{\text{CV}}$$RCV2) and validation ($${\text{R}}^{2}_{\text{V}}$$RV2)] and root mean square error (RMSE) [cross-validation (RMSECV) and validation (RMSEV)] 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.ResultsLeaf color parameters (a*, b* and L*) and chlorophyll content were well predicted by NIR reflectance spectroscopy on fresh leaves in vivo. The mean $${\text{R}}^{2}_{\text{CV}}$$RCV2 and RMSECV 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 (h2 = 0.77, 0.89, 0.78, 0.81 respectively) were estimated. Several families with bright red color or bright yellow color were selected.ConclusionsNIR 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.

[1]  Lutgarde M. C. Buydens,et al.  Interpretation of variable importance in Partial Least Squares with Significance Multivariate Correlation (sMC) , 2014 .

[2]  W. B. Hemsley Sassafras in China. (Sassafras Tzumu, Hemsl.) , 1907 .

[3]  Andrew K. Skidmore,et al.  Hyperspectral reflectance of leaves and flowers of an outbreak species discriminates season and successional stage of vegetation , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[4]  M. Ketudat-Cairns,et al.  Genetic analysis for anthocyanin and chlorophyll contents in rapeseed , 2016 .

[5]  A. Wellburn The Spectral Determination of Chlorophylls a and b, as well as Total Carotenoids, Using Various Solvents with Spectrophotometers of Different Resolution* , 1994 .

[6]  A. M. Townsend,et al.  Variation Among Full-Sib Progenies of Red Maple in Growth, Autumn Leaf Color, and Leafhopper Injury , 1993 .

[7]  W. Huisman,et al.  Effect of Drying on the Color of Tarragon (Artemisia dracunculus L.) Leaves , 2011 .

[8]  J. Mayer,et al.  Genetic potential and stability of carotene content in cassava roots , 2004, Euphytica.

[9]  B. Datt Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves , 1998 .

[10]  Average Genetic Correlations Among Offspring From Open-Pollinated Forest Trees , 2005 .

[12]  Thomas N. Sherratt,et al.  The adaptive significance of autumn leaf colours , 2002 .

[13]  P. Townsend,et al.  Spectroscopic determination of ecologically relevant plant secondary metabolites , 2016 .

[14]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[15]  K. Vogel,et al.  Heritability Estimates for Height, Color, Erectness, Leafiness, and Vigor in Indiangrass1 , 1981 .

[16]  Elena Tamburini,et al.  Development of FT-NIR Models for the Simultaneous Estimation of Chlorophyll and Nitrogen Content in Fresh Apple (Malus Domestica) Leaves , 2015, Sensors.

[17]  Bin Chen,et al.  Leaf chlorophyll content as a proxy for leaf photosynthetic capacity , 2017, Global change biology.

[18]  William H. Press,et al.  Numerical recipes , 1990 .

[19]  Chi-Hyuck Jun,et al.  Kernel-based calibration methods combined with multivariate feature selection to improve accuracy of near-infrared spectroscopic analysis , 2015 .

[20]  Yong He,et al.  Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks , 2008 .

[21]  F. M. Salleh,et al.  Genetic Variation, Heritability, and Diversity Analysis of Upland Rice (Oryza sativa L.) Genotypes Based on Quantitative Traits , 2015, BioMed research international.

[22]  Y. Takeuchi,et al.  New Physiological Effects of 5-Aminolevulinic Acid in Plants: The Increase of Photosynthesis, Chlorophyll Content, and Plant Growth. , 1997, Bioscience, biotechnology, and biochemistry.

[23]  J. Varco,et al.  Dependency of Cotton Leaf Nitrogen, Chlorophyll, and Reflectance on Nitrogen and Potassium Availability , 2004, Agronomy Journal.

[24]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[25]  L. A. Stone,et al.  Computer Aided Design of Experiments , 1969 .

[26]  S. Tsuchikawa,et al.  Application of Near Infrared Spectroscopy (NIR) to light-irradiated wood , 2003, Holz als Roh- und Werkstoff.

[27]  R. Wimmer,et al.  Prediction of natural durability of commercial available European and Siberian larch by near-infrared spectroscopy , 2006 .

[28]  Henning Buddenbaum,et al.  Using VNIR and SWIR field imaging spectroscopy for drought stress monitoring of beech seedlings , 2015 .

[29]  A. Takafuji,et al.  Genetic basis of color variation in leaf scars induced by the Kanzawa spider mite , 2003 .

[30]  Karin Fackler,et al.  A Review of Band Assignments in near Infrared Spectra of Wood and Wood Components , 2011 .

[31]  Ji-chun Tian,et al.  Genetic analysis of grain yield and leaf chlorophyll content in common wheat , 2009 .

[32]  Yong He,et al.  Color Measurement of Tea Leaves at Different Drying Periods Using Hyperspectral Imaging Technique , 2014, PloS one.

[33]  W. P. Judkins,et al.  CORRELATION BETWEEN LEAF COLOR, LEAF NITROGEN CONTENT, AND GROWTH OF APPLE, PEACH, AND GRAPE PLANTS. , 1950, Plant physiology.

[34]  W. Press,et al.  Savitzky-Golay Smoothing Filters , 2022 .

[35]  Jiewen Zhao,et al.  Determination of total flavonoids content in fresh Ginkgo biloba leaf with different colors using near infrared spectroscopy. , 2012, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[36]  E. Hunt,et al.  Estimating near-infrared leaf reflectance from leaf structural characteristics. , 2001, American journal of botany.

[37]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[38]  K. K. Mydin,et al.  Heritability of Yield and Secondary Traits in two populations of Para Rubber Tree (Hevea brasiliensis) , 2011 .

[39]  Linzhang Yang,et al.  Deriving leaf chlorophyll content of green-leafy vegetables from hyperspectral reflectance , 2009 .

[40]  J. H. Eley,et al.  Changes in the Chlorophyll a: b Ratio during Autumn Coloration of Populus sargentii , 1981 .

[41]  Daniela de Carvalho Lopes,et al.  Vis/NIR spectroscopy and chemometrics for non-destructive estimation of water and chlorophyll status in sunflower leaves , 2017 .

[42]  Heiko Balzter,et al.  Plant Family-Specific Impacts of Petroleum Pollution on Biodiversity and Leaf Chlorophyll Content in the Amazon Rainforest of Ecuador , 2017, PloS one.

[43]  Holly Croft,et al.  Leaf Pigment Content , 2018 .