Li Yanjie, Sun Yang, Jiang Jingmin, Liu Jun
Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400 Zhejiang Province China.
Plant Methods. 2019 Jul 11;15:73. doi: 10.1186/s13007-019-0458-0. eCollection 2019.
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.
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.
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.
反射光谱技术，如红外、可见 - 近红外光谱，结合化学计量学，已广泛应用于植物叶片化学分析。但近红外反射光谱在植物叶片颜色和色素分析中的应用以及用于遗传育种选择的可能性研究较少。在此，我们研究了近红外反射光谱测定植物叶片颜色和叶绿素含量的能力，并将预测结果用于遗传选择。使用新鲜的活树叶进行近红外光谱采集，用标准分析方法测量叶片颜色参数（a*、b和L）和叶绿素含量，采用偏最小二乘回归（PLSR）构建模型，用决定系数（R）[交叉验证（ ）和验证（ ）]以及均方根误差（RMSE）[交叉验证（RMSE）和验证（RMSE）]评估模型性能，应用显著多元相关算法改进模型，找出与叶片颜色参数和叶绿素模型相关的最重要区域，已对其进行100次模拟以估计准确性。
近红外反射光谱能很好地预测活体新鲜叶片的颜色参数（a*、b和L）和叶绿素含量。a*、b*、L和叶绿素含量的平均 和RMSE分别为（0.82，4.43）、（0.63，3.72）、（0.61，2.35）和（0.86，0.13%）。发现了三个与a、b*、L和叶绿素含量高度相关的最重要近红外区域，分别为1087、1215和2219 nm。近红外反射光谱技术可成功用于遗传育种计划。估计a、b*、L*和叶绿素含量的遗传力较高（分别为 = 0.77、0.89、0.78、0.81）。选择了几个具有鲜红色或鲜黄色的家系。
近红外光谱在快速预测活体新鲜叶片的颜色和叶绿素含量方面很有前景。它有能力同时测量多种植物叶片性状，可能允许进行快速且经济的原位预测。
