Prediction of cyanidin 3-rutinoside content in Michelia crassipes based on near-infrared spectroscopic techniques.

Currently the determination of cyanidin 3-rutinoside content in plant petals usually requires chemical assays or high performance liquid chromatography (HPLC), which are time-consuming and laborious. In this study, we aimed to develop a low-cost, high-throughput method to predict cyanidin 3-rutinoside content, and developed a cyanidin 3-rutinoside prediction model using near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). We collected spectral data from Michelia crassipes (Magnoliaceae) tepals and used five different preprocessing methods and four variable selection algorithms to calibrate the PLSR model to determine the best prediction model. The results showed that (1) the PLSR model built by combining the blockScale (BS) preprocessing method and the Significance multivariate correlation (sMC) algorithm performed the best; (2) The model has a reliable prediction ability, with a coefficient of determination (R2) of 0.72, a root mean square error (RMSE) of 1.04%, and a residual prediction deviation (RPD) of 2.06. The model can be effectively used to predict the cyanidin 3-rutinoside content of the perianth slices of M. crassipes, providing an efficient method for the rapid determination of cyanidin 3-rutinoside content.

Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery.

Genomic selection (GS) is an option for plant domestication that offers high efficiency in improving genetics. However, GS is often not feasible for long-lived tree species with large and complex genomes. In this paper, we investigated UAV multispectral imagery in time series to evaluate genetic variation in tree growth and developed a new predictive approach that is independent of sequencing or pedigrees based on multispectral imagery plus vegetation indices (VIs) for slash pine. Results show that temporal factors have a strong influence on the h2 of tree growth traits. High genetic correlations were found in most months, and genetic gain also showed a slight influence on the time series. Using a consistent ranking of family breeding values, optimal slash pine families were selected, obtaining a promising and reliable predictive ability based on multispectral+VIs (MV) alone or on the combination of pedigree and MV. The highest predictive value, ranging from 0.52 to 0.56, was found in July. The methods described in this paper provide new approaches for phenotypic selection (PS) using high-throughput multispectral unmanned aerial vehicle (UAV) technology, which could potentially be used to reduce the generation time for conifer species and increase the genetic granularity independent of sequencing or pedigrees.

Prediction of Needle Physiological Traits Using UAV Imagery for Breeding Selection of Slash Pine.

Leaf nitrogen (N) content and nonstructural carbohydrate (NSC) content are 2 important physiological indicators that reflect the growth state of trees. Rapid and accurate measurement of these 2 traits multitemporally enables dynamic monitoring of tree growth and efficient tree breeding selection. Traditional methods to monitor N and NSC are time-consuming, are mostly used on a small scale, and are nonrepeatable. In this paper, the performance of unmanned aerial vehicle multispectral imaging was evaluated over 11 months of 2021 on the estimation of canopy N and NSC contents from 383 slash pine trees. Four machine learning methods were compared to generate the optimal model for N and NSC prediction. In addition, the temporal scale of heritable variation for N and NSC was evaluated. The results show that the gradient boosting machine model yields the best prediction results on N and NSC, with R 2 values of 0.60 and 0.65 on the validation set (20%), respectively. The heritability (h 2) of all traits in 11 months ranged from 0 to 0.49, with the highest h 2 for N and NSC found in July and March (0.26 and 0.49, respectively). Finally, 5 families with high N and NSC breeding values were selected. To the best of our knowledge, this is the first study to predict N and NSC contents in trees using time-series unmanned aerial vehicle multispectral imaging and estimating the genetic variation of N and NSC along a temporal scale, which provides more reliable information about the overall performance of families in a breeding program.

Genetic analysis and elite tree selection of the main resin components of slash pine.

Pine resin, as a natural material, has been widely used in food, pharmaceutical, and chemical industries. Slash pine (Pinus elliottii Engelm var. elliottii) is the primary tree species for resin tapping due to its high resin yield, low resin crystallization rate, and high turpentine content. Current researches focuse on the targeted improvement of several significant components to meet industrial needs rather than just resin yield. The objective of this study was to examine the genetic variation and correlation of genetic and phenotype for four main resin components (α pinene, ß pinene, abietic acid, and levoprimaric acid) of 219 half-sib progenies from 59 families. The results showed that the levopimaric acid had the largest content (mean value = 21.63%), while the ß pinene content had the largest variation coefficient (CV = 0.42). The α pinene content has the highest heritability (h2 = 0.67), while levopimaric acid has the lowest heritability (h2 = 0.51). There was a significant negative correlation between α pinene and the other three components and a significant positive correlation between ß pinene and the two diterpenes. The family ranking and genetic gain suggested that it is possible to improve the contents of main resin components of slash pine through genetic breeding selection.

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery.

Rapid and accurate distinction between young and old leaves of Toona sinensis in the wild is of great significance to the selection of T. sinensis varieties and the evaluation of relative yield. In this study, UAV hyperspectral imaging technology was used to obtain canopy hyperspectral data of biennial seedlings of different varieties of T. sinensis to distinguish young and old leaves. Five classification models were trained, namely Random Forest (RF), Artificial Neural Network (ANN), Decision Tree (DT), Partial Least Squares Discriminant Analysis (PLSDA), and Support Vector Machine (SVM). Raw spectra and six preprocessing methods were used to fit the best classification model. Satisfactory accuracy was obtained from all the five models using the raw spectra. The SVM model showed good performance on raw spectra and all preprocessing methods, and yielded higher accuracy, sensitivity, precision, and specificity than other models. In the end, the SVM model based on the raw spectra produced the most reliable and robust prediction results (99.62% accuracy and 99.23% sensitivity on the validation set only, and 100.00% for the rest). Three important spectral regions of 422.7~503.2, 549.2, and 646.2~687.2 nm were found to be highly correlated with the identification of young leaves of T. sinensis. In this study, a fast and effective method for identifying young leaves of T. sinensis was found, which provided a reference for the rapid identification of young leaves of T. sinensis in the wild.

Variation in Growth, Wood Density, and Stem Taper Along the Stem in Self-Thinning Stands of Sassafras tzumu.

Silvicultural practices greatly improve the economic value of wood products from forests. Stem dimensions, wood density, and stem form are closely linked to end-product performance. This research aimed to examine the effects of stand density and stem height on variables that reflect ring growth and wood properties of Sassafras tzumu stands during the self-thinning phase. Between the ages of 10 and 40 years, the number of stems per hectare has declined from 1,068 to 964 due to density-dependent mortality. As the relative stand density decreased, there were significant reductions in the average tree ring width (5.07-3.51 mm) and increases in latewood proportions (49.88-53.49%) and the density of the annual growth ring (165.60-708.58 kg/m3). Therefore, ring density, earlywood density, and latewood density increased with decreasing relative stand density after self-thinning occurred. Ring width, earlywood width, and latewood width significantly increased from the base to the apex of the stem. Stand density and stem height had additive effects on S. tzumu wood properties during the self-thinning phase. A shift in the growth allocation along the longitudinal stem in response to self-thinning resulted in decreasing radial growth, increasing wood density, and improved stem form. In summary, we found a significant influence of stand density on tree ring growth, wood quality, and stem form of S. tzumu trees during the self-thinning phase.

Enabling Breeding Selection for Biomass in Slash Pine Using UAV-Based Imaging.

Traditional methods used to monitor the aboveground biomass (AGB) and belowground biomass (BGB) of slash pine (Pinus elliottii) rely on on-ground measurements, which are time- and cost-consuming and suited only for small spatial scales. In this paper, we successfully applied unmanned aerial vehicle (UAV) integrated with structure from motion (UAV-SfM) data to estimate the tree height, crown area (CA), AGB, and BGB of slash pine for in slash pine breeding plantations sites. The CA of each tree was segmented by using marker-controlled watershed segmentation with a treetop and a set of minimum three meters heights. Moreover, the genetic variation of these traits has been analyzed and employed to estimate heritability (h 2). The results showed a promising correlation between UAV and ground truth data with a range of R 2 from 0.58 to 0.85 at 70 m flying heights and a moderate estimate of h 2 for all traits ranges from 0.13 to 0.47, where site influenced the h 2 value of slash pine trees, where h 2 in site 1 ranged from 0.13~0.25 lower than that in site 2 (range: 0.38~0.47). Similar genetic gains were obtained with both UAV and ground truth data; thus, breeding selection is still possible. The method described in this paper provides faster, more high-throughput, and more cost-effective UAV-SfM surveys to monitor a larger area of breeding plantations than traditional ground surveys while maintaining data accuracy.

Prediction and Comparisons of Turpentine Content in Slash Pine at Different Slope Positions Using Near-Infrared Spectroscopy.

Pine resin is one of the best known and most exploited non-wood products. Resin is a complex mixture of terpenes produced by specialized cells that are dedicated to tree defense. Chemical defenses are plastic properties, and concentrations of chemical defenses can be adjusted based on environmental factors, such as resource availability. The slope orientation (south/sunny or north/shady) and the altitude of the plantation site have significant effects on the soil nutrient and the plant performance, whereas little is known about how the slope affects the pine resin yield and its components. In total, 1180 slash pines in 18 plots at different slope positions were established to determine the effects on the α- and ß-pinene content and resin production of the slash pine. The near-infrared spectroscopy (NIR) technique was developed to rapidly and economically predict the turpentine content for each sample. The results showed that the best partial least squares regression (PLS) models for α- and ß-pinene content prediction were established via the combined treatment of multiplicative scatter correction-significant multivariate correlation (MSC-sMC). The prediction models based on sMC spectra for α- and ß-pinene content have an R2 of 0.82 and 0.85 and an RMSE of 0.96 and 0.82, respectively, and they were successfully implemented in turpentine prediction in this research. The results also showed that a barren slope position (especially mid-slope) could improve the α-pinene and ß-pinene content and resin productivity of slash pine, and the ß-pinene content in the resin had more variances in this research.

A transcriptome-based association study of growth, wood quality, and oleoresin traits in a slash pine breeding population.

Slash pine (Pinus elliottii Engelm.) is an important timber and resin species in the United States, China, Brazil and other countries. Understanding the genetic basis of these traits will accelerate its breeding progress. We carried out a genome-wide association study (GWAS), transcriptome-wide association study (TWAS) and weighted gene co-expression network analysis (WGCNA) for growth, wood quality, and oleoresin traits using 240 unrelated individuals from a Chinese slash pine breeding population. We developed high quality 53,229 single nucleotide polymorphisms (SNPs). Our analysis reveals three main results: (1) the Chinese breeding population can be divided into three genetic groups with a mean inbreeding coefficient of 0.137; (2) 32 SNPs significantly were associated with growth and oleoresin traits, accounting for the phenotypic variance ranging from 12.3% to 21.8% and from 10.6% to 16.7%, respectively; and (3) six genes encoding PeTLP, PeAP2/ERF, PePUP9, PeSLP, PeHSP, and PeOCT1 proteins were identified and validated by quantitative real time polymerase chain reaction for their association with growth and oleoresin traits. These results could be useful for tree breeding and functional studies in advanced slash pine breeding program.

Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees.

The internal cycling of nitrogen (N) storage and consumption in trees is an important physiological mechanism associated with tree growth. Here, we examined the capability of near-infrared spectroscopy (NIR) to quantify the N concentration across tissue types (needle, trunk, branch, and root) without time and cost-consuming. The NIR spectral data of different tissues from slash pine trees were collected, and the N concentration in each tissue was determined using standard analytical method in laboratory. Partial least squares regression (PLSR) models were performed on a set of training data randomly selected. The full-length spectra and the significant multivariate correlation (sMC) variable selected spectra were used for model calibration. Branch, needle, and trunk PLSR models performed well for the N concentration using both full length and sMC selected NIR spectra. The generic model preformatted a reliable accuracy with R2 C and R2 CV of 0.62 and 0.66 using the full-length spectra, and 0.61 and 0.65 using sMC-selected spectra, respectively. Individual tissue models did not perform well when being used in other tissues. Five significantly important regions, i.e., 1480, 1650, 1744, 2170, and 2390 nm, were found highly related to the N content in plant tissues. This study evaluates a rapid and efficient method for the estimation of N content in different tissues that can help to serve as a tool for tree N storage and recompilation study.

Predicting the potential global distribution of Sapindus mukorossi under climate change based on MaxEnt modelling.

Sapindus mukorossi (S. mukorossi) is an important biological washing material and biomass energy tree species whose peel is rich in saponins, and its kernels have a high oil content. We used the maximum entropy model (MaxEnt) to predict the suitable habitats of S. mukorossi globally, screen the dominant environmental factors affecting its distribution and analyse the changes in its suitable habitats under climate change from prehistory to the future, and the results will provide a scientific basis for germplasm resource collection, protection, introduction and cultivation. Twenty-two environmental variables and global distribution data for S. mukorossi were used to construct the species distribution model, and the receiver operating characteristic (ROC) curve was used to verify the accuracy of the model. The dominant environmental factors were screened through the jackknife method, and then, the geographical information system (ArcGIS) was used to complete the grade of suitable habitat division and area calculation. The results showed that the MaxEnt model had an excellent predictive effect, and the area under the ROC curve (AUC) value was as high as 0.969. The precipitation of the warmest quarter (Bio18), the minimum temperature of the coldest month (Bio6), temperature seasonality (Bio4) and isothermality (Bio3) were the dominant environmental factors that affected the distribution of S. mukorossi. The largest area of the world's suitable habitats occurred during the last interglacial (LIG) (772.69 × 104 km2), and the area decreased sharply (614.46 × 104 km2) during the last glacial maximum (LGM). The area of suitable habitat showed an increasing trend during the Mid-Holocene (MH) and currently, with areas of 631.06 × 104 km2 and 706.82 × 104 km2, respectively. The area of suitable habitats for S. mukorossi globally was 718.35 × 104 km2 (SSP1-2.6), 636.85 × 104 km2 (SSP2-4.5), 657.64 × 104 km2 (SSP3-7.0) and 675.89 × 104 km2 (SSP5-8.5) under the four scenarios of the future climate. The area increased only in the SSP1 scenario. In summary, globally, the suitable area of S. mukorossi tended to migrate to higher latitudes and decrease in area with future climate change.

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy.

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 (R 2) 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.

Comparative transcriptomics provides a strategy for phylogenetic analysis and SSR marker development in Chaenomeles.

The genus Chaenomeles has long been considered an important ornamental, herbal and cash crop and is widely cultivated in East Asia. Traditional studies of Chaenomeles mainly focus on evolutionary relationships at the phenotypic level. In this study, we conducted RNA-seq on 10 Chaenomeles germplasms supplemented with one outgroup species, Docynia delavayi (D. delavayi), on the Illumina HiSeq2500 platform. After de novo assemblies, we generated from 40,084 to 49,571 unigenes for each germplasm. After pairwise comparison of the orthologous sequences, 9,659 orthologues within the 11 germplasms were obtained, with 6,154 orthologous genes identified as single-copy genes. The phylogenetic tree was visualized to reveal evolutionary relationships for these 11 germplasms. GO and KEGG analyses were performed for these common single-copy genes to compare their functional similarities and differences. Selective pressure analysis based on 6,154 common single-copy genes revealed that 45 genes were under positive selection. Most of these genes are involved in building the plant disease defence system. A total of 292 genes containing simple sequence repeats (SSRs) were used to develop SSR markers and compare their functions in secondary metabolism pathways. Finally, 10 primers were chosen as SSR marker candidates for Chaenomeles germplasms by comprehensive standards. Our research provides a new methodology and reference for future related research in Chaenomeles and is also useful for improvement, breeding and selection projects in other related species.

Spectrometric prediction of wood basic density by comparison of different grain angles and variable selection methods.

BACKGROUND: Wood basic density (WBD) is one of the most crucial wood property in tree and mainly determined the end use of wood for industry. However, the measurement WBD is time- and cost-consuming, which an alternatively fast and no-destructive measurement is needed. In this study, capability of NIR spectroscopy combined with partial least squares regression (PLSR) to quantify the WBD were examined in multiple wood species. To obtain more accurate and robust prediction models, the grain angle (0° (transverse surface), 45°, 90° (radial surface)) influence on the collection of solid wood spectra and a comparison of found variable selection methods for NIR spectral variables optimization were conducted, including significant Multivariate Correlation (sMC), Regularized elimination procedure (Rep), Iterative predictor weighting (Ipw) and Genetic algorithm (Ga). Models made by random calibration data selection were conducted 200 times performance evaluation. RESULTS: These results indicate that 90° angle models display relatively highest efficiency than other angle models, mixed angle model yield a satisfied WBD prediction results as well and could reduce the influence of grain angle. Rep method shows a higher efficiency than other methods which could eliminate the uninformative variables and enhance the predictive performance of 90° angle and mix angle models. CONCLUSIONS: This study is potentially shown that the WBD (g/cm3) on solid wood across grain angles and varies wood species could be measured in a rapid and efficient way using NIR technology. Combined with the PLSR model, our methodology could serve as a tool for wood properties breeding and silviculture study.

Association of spectroscopically determined leaf nutrition related traits and breeding selection in Sassafras tzumu.

BACKGROUND: Plant traits related to nutrition have an influential role in tree growth, tree production and nutrient cycling. Therefore, the breeding program should consider the genetics of the traits. However, the measurement methods could seriously affect the progress of breeding selection program. In this study, we tested the ability of spectroscopy to quantify the specific leaf nutrition traits including anthocyanins (ANTH), flavonoids (FLAV) and nitrogen balance index (NBI), and estimated the genetic variation of these leaf traits based on the spectroscopic predicted data. Fresh leaves of Sassafras tzumu were selected for spectral collection and ANTH, FLAV and NBI concentrations measurement by standard analytical methods. Partial least squares regression (PLSR), five spectra pre-processing methods, and four variable selection algorisms were conducted for the optimal model selection. Each trait model was simulated 200 times for error estimation. RESULTS: The standard normal variate (SNV) to the ANTH model and 1st derivatives to the FLAV and NBI models, combined with significant Multivariate Correlation (sMC) algorithm variable selection are finally regarded as the best performance models. The ANTH model produced the highest accuracy of prediction with a mean R2 of 0.72 and mean RMSE of 0.10%, followed by FLAV and NBI model (mean R2 of 0.58, mean RMSE of 0.11% and mean R2 of 0.44, mean RMSE of 0.04%). High heritability was found for ANTH, FLAV and NBI with h2 of 0.78, 0.58 and 0.61 respectively. It shows that it is beneficial and possible for breeding selection to the improvement of leaf nutrition traits. CONCLUSIONS: Spectroscopy can successfully characterize the leaf nutrition traits in living tree leaves and the ability to simultaneous multiple plant traits provides a promising and high-throughput tool for the quick analysis of large size samples and serves for genetic breeding program.

Effects of forest gap disturbance on forest ecosystem.

Forest gap disturbance has important consequences on plant species assemblage, stand structure and ecosystem functions of forests via changing micro-scale heterogeneity and community succession. Here, we reviwed research progress in the effects of forest gap disturbance on forest ecosystem. The effects of forest gap disturbance on plant species assemblage was analyzed based on the intrinsic biological characteristics and external environmental factors. The effects of forest gap disturbance on stand structure was discussed from the perspectives of texture and architecture of plant community. Forest gap disturbance effect on forest ecosystem functions was reviewed. After analyzing the theoretical shortcomings and the key bottleneck of forest ecosystem management practices, the following research directions were proposed, including the methods of determining threshold of forest gap, the mechanism of canopy closure, the effect of forest gap disturbance on forest ecosystem processes, and the relationship between forest gap disturbance and forest productivity. The advantage of forest gap disturbance in accelerating plant species regeneration and structure complexities could provide scientific evidence for enhancing the quality of low yield and low function plantations in China.

Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen Content Under Drought Stress Using Near Infrared Spectroscopy.

Drought is a climatic event that considerably impacts plant growth, reproduction and productivity. Toona sinensis is a tree species with high economic, edible and medicinal value, and has drought resistance. Thus, the objective of this study was to dynamically monitor the physiological indicators of T. sinensis in real time to ensure the selection of drought-resistant varieties of T. sinensis. In this study, we used near-infrared spectroscopy as a high-throughput method along with five preprocessing methods combined with four variable selection approaches to establish a cross-validated partial least squares regression model to establish the relationship between the near infrared reflectance spectroscopy (NIRS) spectrum and physiological characteristics (i.e., chlorophyll content and nitrogen content) of T. sinensis leaves. We also tested optimal model prediction for the dynamic changes in T. sinensis chlorophyll and nitrogen content under five separate watering regimes to mimic non-destructive and dynamic detection of plant leaf physiological changes. Among them, the accuracy of the chlorophyll content prediction model was as high as 72%, with root mean square error (RMSE) of 0.25, and the RPD index above 2.26. Ideal nitrogen content prediction model should have R 2 of 0.63, with RMSE of 0.87, and the RPD index of 1.12. The results showed that the PLSR model has a good prediction effect. Overall, under diverse drought stress treatments, the chlorophyll content of T. sinensis leaves showed a decreasing trend over time. Furthermore, the chlorophyll content was the most stable under the 75% field capacity treatment. However, the nitrogen content of the plant leaves was found to have a different and variable trend, with the greatest drop in content under the 10% field capacity treatment. This study showed that NIRS has great potential for analyzing chlorophyll nitrogen and other elements in plant leaf tissues in non-destructive dynamic monitoring.

An efficient method to reduce grain angle influence on NIR spectra for predicting extractives content from heartwood stem cores of Toona. sinensis.

BACKGROUND: A fast, reliable and non-destructive method is needed to qualify the extractives content (EC) in heartwood of T. sinensis cores in the breeding program for studying the genetic effect on EC. However, the influence of grain angle on near infrared (NIR) spectra prediction model for EC is unclear. In this study, NIR spectra were collected from both cross section and radial section of wood core samples in order to predict the EC in heartwood. RESULTS: The effect of grain angle on calibration EC model was studied. Several different spectra pre-processing methods were implemented for calibration. It was found that standard normal variation (SNV) followed by 1st derivative yielded the best calibration result for T. sinensis EC. Grain angle had a significant influence on the predicted model for EC when using the whole NIR spectra. However, after testing a certain point of the prior variables for EC that were selected by the significant multivariate correlation (sMC), the influence of grain angle was significantly eliminated. CONCLUSIONS: It is suggested that NIR spectroscopy is a promising method to predict EC in the solid wood without effecting grain angle.

The complete chloroplast genome sequences of two Chaenomeles species (Chaenomeles cathayensis and Chaenomeles thibetica).

The complete chloroplast genome sequence of Chaenomeles cathayensis and Chaenomeles thibetica was characterized from Illumina pair-end sequencing. The length of C. cathayensis and C. thibetica chloroplast genome of was 159,875 bp and 159,907 bp, respectively. The C. cathayensis chloroplast genome contained a large single-copy region (LSC) of 87,813bp, a small single-copy region (SSC) of 19,304 bp, and two inverted repeat (IR) regions of 26,379 bp, which is a quadripartite structure. Similarity, The C. thibetica chloroplast genome also contained a quadripartite structure, including a LSC region (87,851 bp), a SSC region (19,298 bp), and two IR regions (26,379 bp). The overall GC content of C. cathayensis and C. thibetica are both 36.57%. The C. cathayensis and C. thibetica chloroplast genome contains 130 complete genes, including 85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. The C. thibetica chloroplast genome contains 130 complete genes, including 85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. The neighbour-joining phylogenetic analysis showed that C. cathayensis and C. thibetica clustered together, indicated that C. cathayensis and C. thibetica have closed evolutionary relationship compared with other Chaenomeles species.

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

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 (R2) [cross-validation ( R CV 2 ) and validation ( R V 2 )] 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. 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 R CV 2 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 (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.