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

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.

Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration

Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220-1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.

Morphological, Biochemical, and Molecular Diversity Assessment of Egyptian Bottle Gourd Cultivars

The genetic variability and relationships between ten bottle gourd cultivars were evaluated based on morphological, biochemical, and molecular parameters. The results displayed high variability among selected cultivars in terms of photosynthetic pigments, total free amino acids, total phenol content, isozymes pattern, and protein electrophoresis. Furthermore, differences in molecular markers were revealed by the SCoT technique. The peroxidase (POD) and polyphenyl oxidase (PPO) isozymes patterns did not detect significant differences in bands among cultivars. The protein patterns revealed seventeen bands ranging from 126 to 9 kDa and five polymorphic bands representing 29.41%. On the other hand, eight SCoT primers were used to evaluate the genetic variability and relationships between the ten Egyptian bottle gourd cultivars. The results of SCoT analysis detected 44 amplicons with 50% polymorphism. In addition, the results of the phylogenetic tree that is constructed based on the similarity coefficient revealed by SCoT analysis confirm the results of biochemical analysis indicating a genetic relationship between the most efficient bottle gourd cultivars (S1 and S2 cultivars). In addition, there is a genetic relationship among the less efficient bottle gourd cultivars (S4 and S5 cultivars). These results could be beneficial to distinguish among bottle gourd cultivars in the plant breeding programs.

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, β 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 β 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 β 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.

Insight into the molecular mechanisms of leaf coloration in Cymbidium ensifolium

Cymbidiumensifolium L. is a significant ornamental plant in Orchidaceae. Aside from its attractive flowers, its leaf coloration is also an important ornamental trait. However, there is an apparent lack of studies concerning the intricate mechanism of leaf coloration in C. ensifolium. In this study, we report a systematic evaluation of leaf coloration utilizing transcriptome and metabolome profiles of purple, yellow, and green leaves. In total, 40 anthocyanins and 67 flavonoids were quantified along with chlorophyll content. The tissue–transcriptome profile identified 26,499 differentially expressed genes (DEGs). The highest chlorophyll contents were identified in green leaves, followed by yellow and purple leaves. We identified key anthocyanins and flavonoids associated with leaf coloration, including cyanidin-3-O-sophoroside, naringenin-7-O-glucoside, delphinidin, cyanidin, petunidin, and quercetin, diosmetin, sinensetin, and naringenin chalcone. Moreover, genes encoding UDP-glucoronosyl, UDP-glucosyl transferase, chalcone synthesis, flavodoxin, cytochrome P450, and AMP-binding enzyme were identified as key structural genes affecting leaf coloration in C. ensifolium. In summary, copigmentation resulting from several key metabolites modulated by structural genes was identified as governing leaf coloration in C. ensifolium. Further functional verification of the identified DEGs and co-accumulation of metabolites can provide a tool to modify leaf color and improve the aesthetic value of C. ensifolium.

Effect of the Co-Application of Eucalyptus Wood Biochar and Chemical Fertilizer for the Remediation of Multimetal (Cr, Zn, Ni, and Co) Contaminated Soil

Contamination of soil with heavy metals is a worldwide problem, which causes heavy metals to release into the environment. Remediation of such contaminated soil is essential to protect the environment. The aims of this study are: first, to compare the effect of biochar and the joint application of biochar with fertilizer for the phytoremediation of heavy metals-contaminated soil using Acacia auriculiformis; second, to study the effect of the application rate of biochar in improving the physicochemical properties of the soil. The soil samples were collected from an active coal mine dump and assessed for their physicochemical properties and heavy metals toxicity. Initial results indicated that the soil has poor physicochemical properties and was contaminated with the presence of heavy metals such as Zn, Ni, Cu, Cr, and Co. Later, the heavy metals-contaminated soil was mixed with the 400 and 600 °C biochar, as well as the respective biochar–fertilizer combination in varying mixing ratios from 0.5 to 5% (w/w) and subjected to a pot-culture study. The results showed that the application of both varieties of biochar in combination with fertilizer substantially improved the physicochemical properties and reduced the heavy metals toxicity in the soil. The biochar and fertilizer joint application also substantially improved the soil physiochemical properties by increasing the application rate of both varieties of biochar from 0.5 to 5%. The soil fertility index (SFI) of the biochar and biochar–fertilizer amended soil increased by 49.46 and 52.22%, respectively. The plant’s physiological analysis results indicated a substantial increase in the plant’s shoot and root biomass through the application of biochar and biochar–fertilizer compared to the control. On the other hand, it significantly reduced the heavy metals accumulation and, hence, the secretion of proline and glutathione hormones in the plant cells. Therefore, it can be concluded that the joint application of biochar with the application rate varying between 2.5 to 5% (w/w) with the fertilizer significantly improved the physicochemical properties of the soil and reduced the heavy metals toxicity compared to the controlled study.

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.

Determination of Quality Parameters in Mangetout (Pisum sativum L. ssp. arvense) by Using Vis/Near-Infrared Reflectance Spectroscopy

Pisum sativum L. ssp. arvense, is colloquially called tirabeque or mangetout because it is eaten whole; its pods are recognized as a delicatessen in cooking due to its crunch on the palate and high sweetness. Furthermore, this legume is an important source of protein and antioxidant compounds. Quality control in this species requires the analysis of a large number of samples using costly and laborious conventional methods. For this reason, a non-chemical and rapid technique as near-infrared reflectance spectroscopy (NIRS) was explored to determine its physicochemical quality (color, firmness, total soluble solids, pH, total polyphenols, ascorbic acid and protein content). Pod samples from different cultivars and grown under different fertigation treatments were added to the NIRS analysis to increase spectral and chemical variability in the calibration set. Modified partial least squares regression was used for obtaining the calibration models of these parameters. The coefficients of determination in the external validation ranged from 0.50 to 0.88. The RPD (standard deviation to standard error of prediction ratio) and RER (standard deviation to range) were variable for quality parameters and showed values that were characteristic of equations suitable for quantitative prediction and screening purposes, except for the total soluble solid calibration model.

Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus

Plants produce a wide diversity of specialized metabolites, which fulfill a wide range of biological functions, helping plants to interact with biotic and abiotic factors. In this study, an integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed to examine the extent of heritable variation of foliar spectral reflectance and to predict the leaf hydrogen cyanide content in a genetically structured population of a cyanogenic eucalyptus (Eucalyptus cladocalyx F. Muell). In addition, the heritable variation (based on pedigree and genomic data) of more of 100 common spectral reflectance indices was examined. The first profile of heritable variation along the spectral reflectance curve indicated the highest estimate of genomic heritability ( h g 2 =0.41) within the visible region of the spectrum, suggesting that several physiological and biological responses of trees to environmental stimuli (ex., light) are under moderate genetic control. The spectral reflectance index with the highest genomic-based heritability was leaf rust disease severity index 1 ( h g 2 =0.58), followed by the anthocyanin reflectance index and the Browning reflectance index ( h g 2 =0.54). Among the Bayesian prediction models based on spectral reflectance data, Bayes B had a better goodness of fit than the Bayes-C and Bayesian ridge regression models (in terms of the deviance information criterion). All models that included spectral reflectance data outperformed conventional genomic prediction models in their predictive ability and goodness-of-fit measures. Finally, we confirmed the proposed hypothesis that high-throughput phenotyping indirectly capture endophenotypic variants related to specialized metabolites (defense chemistry), and therefore, generally more accurate predictions can be made integrating phenomics and genomics.

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.

Impact of Plasma-Activated Water (PAW) on Seed Germination of Soybean

The present study reports the generation of plasma-activated water (PAW) using dielectric barrier discharge (DBD), its physicochemical properties, and its potential impact on the seed germination and seedling growth of soybean. The results revealed significant changes in physical parameters, such as pH, total dissolved solids, total suspended solids, turbidity, conductivity, dissolved oxygen, and chemical parameters, such as calcium, chromium, sodium, manganese, nitrate, nitrites, phosphorus, and sulfur and biological parameter such as E. coli in water after plasma treatment. The concentration of dissolved oxygen, conductivity, nitrate, nitrite, and sulfur was increased with an increase in water treatment time, and the amounts of the other analyzed parameters decreased with the increase in water treatment time. The effects of untreated water and plasma-activated water treated for 20 minutes on soybean germination and growth were studied. The germination rate was found to be higher with plasma-treated water. Shoot lengths, seedlings length, vigor index, and germination rates were increased as compared to those germinated by normal water irrigation. The seedlings irrigated with PAW responded to the abundance of nitrogen by producing intensely green leaves because of their increased chlorophyll a as compared to seedlings irrigated with normal water. However, the content of chlorophyll b and carotenoids was found to decrease in the case of seedlings irrigated with PAW. Based on this report, we conclude that PAW could be used to substantially enhance seed germination and seedling growth.

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.

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.

A robust spectral angle index for remotely assessing soybean canopy chlorophyll content in different growing stages

BACKGROUND

Timely and accurate estimates of canopy chlorophyll (Chl) a and b content are crucial for crop growth monitoring and agricultural management. Crop canopy reflectance depends on many factors, which can be divided into the following categories: (i) leaf effects (e.g., leaf pigments), (ii) canopy effects (e.g., Leaf Area Index [LAI]), and (iii) soil background reflectance (e.g., soil reflectance). The estimation of leaf variables, such as Chl contents, from reflectance at the canopy scale is usually less accurate than that at the leaf scale. In this study, we propose a Visible and Near-infrared (NIR) Angle Index (VNAI) to estimate the Chl content of soybean canopy, and soybean canopy Chl maps are produced using visible and NIR unmanned aerial vehicle (UAV) remote sensing images. The VNAI is insensitive to LAI and can be used for the multi-stage estimation of crop canopy Chl content.

RESULTS

Eleven previously used vegetation indices (VIs) (e.g., Pigment-specific Normalized Difference Index) were selected for performance comparison. The results showed that (i) most previously used Chl VIs were significantly correlated with LAI, and the proposed VNAI was more sensitive to Chl content than LAI; (ii) the VNAI-based estimates of Chl content were more accurate than those based on the other investigated VIs using (1) simulated, (2) real (field), and (3) real (UAV) datasets.

CONCLUSIONS

Most previously used Chl VIs were significantly correlated with LAI whereas the proposed VNAI was more sensitive to Chl content than to LAI, indicating that the VNAI may be more strongly correlated with Chl content than these previously used VIs. Multi-stage estimations of the Chl content of cropland obtained using the VNAI and broadband remote sensing images may help to obtain Chl maps with high temporal and spatial resolution.

Inheritance and Correlation Analysis of Pulpwood Properties, Wood Density, and Growth Traits of Slash Pine

Slash pine (Pinus elliottii) is the most important and a fast-growing material that is used for industrial timber and pulp production. A breeding program of slash pine that aims to improve wood properties has been employed for the past decade. This study analysed the genetics and correlation of growth traits and wood properties of a total of 1059 individual plants from 49 families of P. elliottii. Heritability, family ranking, genetic gain, and the relationship between these traits were estimated. The results showed that there was a significant negative genetic correlation between the holocellulose and lignin content. The heritabilities of these four traits were ranked from 0.18 to 0.32. The chemical wood traits did not show a strong correlation with diameter at breast height (DBH) and wood density. However, it is still possible to combine wood traits for selection. It was suggested that the genetic breeding selection could improve the growth and quality of P. elliottii.