Drought is a major abiotic stress that adversely affects the growth and productivity of plants. Malondialdehyde (MDA), a substance produced by membrane lipids in response to reactive oxygen species (ROS), can be used as a drought indicator to evaluate the degree of plasma membrane damage and the ability of plants to drought stress tolerance. Still measuring MDA is usually a labor- and time-consuming task. In this study, near-infrared (NIR) spectroscopy combined with partial least squares (PLS) was used to obtain rapid and high-throughput measurements of MDA, and the application of this technique to plant drought stress experiments was also investigated. Two exotic conifer tree species, namely, slash pine (Pinus elliottii) and loblolly pine (Pinus taeda), were used as plant material exposed to drought stress; different types of spectral preprocessing methods and important feature-selection algorithms were applied to the PLS model to calibrate it and obtain the best MDA-predicting model. The results show that the best PLS model is established via the combined treatment of detrended variable–significant multivariate correlation algorithm (DET-sMC), where latent variables (LVs) were 6. This model has a respectable predictive capability, with a correlation coefficient (R2) of 0.66, a root mean square error (RMSE) of 2.28%, and a residual prediction deviation (RPD) of 1.51, and it was successfully implemented in drought stress experiments as a reliable and non-destructive method to detect the MDA content in real time.

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 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 (R 2 ) [cross-validation ( ) and validation ( )] and root mean square error (RMSE) [cross-validation (RMSE CV ) and validation (RMSE V )] 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 CV 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 ( h 2 = 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.

The quality of Chinese quince fruit is a significant factor for medicinal materials, influencing the quality of the medicine. However, it is difficult to distinguish different types of Chinese quince fruit. The main objective of this work was to use near-infrared (NIR) spectroscopy, which is a rapid and non-destructive analysis method, to classify the varieties of Chinese quince fruits. Raw spectra in the range of 1000 to 2500 nm were combined with linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machines (SVMs) for classification. The first three principal component analysis (PCA) scores were used as input variables to build LDA, QDA, and SVM discriminant models. The results indicate that all three of these methods are effective for distinguishing the different types of Chinese quince fruit. The classification accuracies for LDA, QDA, and SVM are 94, 96, and 98 %, respectively. QDA led to high-level classification accuracy of Chinese quince fruit.

ABSTRACT. This is the first comprehensive study of the genetic analysis of the majority of oleoresin components of slash pine (Pinus elliottii). Pine oleoresin, the resin secreted from the pine tree, is a raw material widely used in industrial products. The objective of this study was to explore the genetic variation and correlation between the major oleoresin components of 50 open pollinated families of slash pine. The individual narrow-sense heritability of the 23 oleoresin components and genetic correlations between them were estimated using the residual maximum likelihood in the flexible mixed modeling program, ASReml-R. A high heritability of 0.424 was observed for β-pinene. Moderate levels of heritability were estimated for β-phellandrene, methyl abietate, estragole, 15-hydroxy-dehydroabietic acid, and isopimaric acid methyl ester at 0.303, 0.294, 0.27, 0.258, and 0.2, respectively. The heritabilities for pimaric acid methyl ester, abieta-8, 13-diene-18-oic acid methyl ester, sandaracopimaric acid, methyl ester, and camphene were relatively low and ranged from 0.11 to 0.17. Many negative genetic correlations were observed as unfavorable while the corresponding phenotypic correlations presented no significant relationships or positive phenotypic correlations. However, the heritabilities and genetic correlations showed that single or multiple component selections and improvement, directly or indirectly, were effective. We postulate that genetic parameters estimated in this study will work as a reference in breeding programs of oleoresin components, especially in slash pine.

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