Nitrogen-improved photosynthesis quantum yield is driven by increased thylakoid density, enhancing green light absorption

Reaction-diffusion modeling provides insights into biophysical carbon-concentrating mechanisms in land plants

Carbon-concentrating mechanisms (CCMs) have evolved numerous times in photosynthetic organisms. They elevate the concentration of CO2 around the carbon-fixing enzyme rubisco, thereby increasing CO2 assimilatory flux and reducing photorespiration. Biophysical CCMs, like the pyrenoid-based CCM (PCCM) of Chlamydomonas reinhardtii or carboxysome systems of cyanobacteria, are common in aquatic photosynthetic microbes, but in land plants appear only among the hornworts. To predict the likely efficiency of biophysical CCMs in C3 plants, we used spatially resolved reaction-diffusion models to predict rubisco saturation and light use efficiency. We found that the energy efficiency of adding individual CCM components to a C3 land plant is highly dependent on the permeability of lipid membranes to CO2, with values in the range reported in the literature that are higher than those used in previous modeling studies resulting in low light use efficiency. Adding a complete PCCM into the leaf cells of a C3 land plant was predicted to boost net CO2 fixation, but at higher energetic costs than those incurred by photorespiratory losses without a CCM. Two notable exceptions were when substomatal CO2 levels are as low as those found in land plants that already use biochemical CCMs and when gas exchange is limited, such as with hornworts, making the use of a biophysical CCM necessary to achieve net positive CO2 fixation under atmospheric CO2 levels. This provides an explanation for the uniqueness of hornworts' CCM among land plants and the evolution of pyrenoids multiple times.

Nitrogen and Silicon Contribute to Wheat Defense's to Pyrenophora tritici-repentis, but in an Independent Manner

Nitrogen (N) and silicon (Si) are mineral elements that have shown a reduction in the damage caused by tan spot (Pyrenophora tritici-repentis (Ptr)) in wheat. However, the effects of these elements were studied separately, and the N and Si interaction effect on wheat resistance to tan spot remains elusive. Histocytological and biochemical defense responses against Ptr in wheat leaves treated with Si (+Si) at low (LN) and high N (HN) inputs were investigated. Soil amendment with Si reduced the tan spot severity in 18% due to the increase in the leaf Si concentration (around 30%), but it was affected by the N level used. The superoxide dismutase (SOD) activity was higher in +Si plants and inoculated with Ptr, leading to early and higher H2O2 and callose accumulation in wheat leaf. Interestedly, phenylalanine ammonia-lyase (PAL) activity was induced by the Si supplying, being negatively affected by the HN rate. Meanwhile, catalase (CAT), and peroxidase (POX) activities showed differential response patterns according to the Si and N rates used. Tan spot severity was reduced by both elements, but their interaction does not evidence synergic effects in this disease's control. Wheat plants from -Si and HN and +Si and LN treatments recorded lower tan spot severity.

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500-600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440-485 nm) and red (626-700 nm) regions had a minor impact. Strong correlations were found between absorbance (R2 = 0.87 and 0.91) and reflectance (R2 = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R2C = 0.91, R2cv = 0.85, and R2P = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.

Kinetic parameters related to nitrogen uptake in 'Okinawa' peach rootstocks are altered by 'Chimarrita' scion Nitrogen uptake in 'Okinawa' peach rootstock

BACKGROUND

The kinetic parameters of peach rootstock are a major factor contributing to its nitrogen (N) uptake efficiency, which directly affects the yield and quality parameters. However, the impact of grafting on N kinetic parameters is not sufficiently known as rootstocks are grafted with other scion cultivars before being transplanted to the field. The objective of this study was therefore to evaluate whether the grafting of 'Okinawa' rootstock with the scion cultivar 'Chimarrita' can alter the kinetic parameters related to N uptake.

RESULTS

The plants were acclimatized in Hoagland solution for 21 days and, sequentially, were tested to assess the depletion of the internal reserves at 15 and 30 days in CaSO4 solution. Morphological and physiological parameters were evaluated, and kinetic parameters were calculated. The depletion period for to reach the minimum concentration (C_min ) of NO₃ - and NH₄ + in the plant was 62 h and 56 h, respectively. The optimal time for internal reserve depletion for 'Okinawa' rootstock with and without the 'Chimarrita' scion was 30 days.

CONCLUSION

'Okinawa' rootstock grafted with 'Chimarrita' scion cultivar was more efficient in NO₃ ^- and NH₄ ⁺ uptake. © 2022 Society of Chemical Industry.

Classification and Prediction by Pigment Content in Lettuce (Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy

Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.

Intra-canopy leaf trait variation facilitates high leaf area index and compensatory growth in a clonal woody encroaching shrub

Leaf trait variation enables plants to utilize large gradients of light availability that exist across canopies of high leaf area index (LAI), allowing for greater net carbon gain while reducing light availability for understory competitors. While these canopy dynamics are well understood in forest ecosystems, studies of canopy structure of woody shrubs in grasslands are lacking. To evaluate the investment strategy used by these shrubs, we investigated the vertical distribution of leaf traits and physiology across canopies of Cornus drummondii, the predominant woody encroaching shrub in the Kansas tallgrass prairie. We also examined the impact of disturbance by browsing and grazing on these factors. Our results reveal that leaf mass per area (LMA) and leaf nitrogen per area (Na) varied approximately threefold across canopies of C. drummondii, resulting in major differences in the physiological functioning of leaves. High LMA leaves had high photosynthetic capacity, while low LMA leaves had a novel strategy for maintaining light compensation points below ambient light levels. The vertical allocation of leaf traits in C. drummondii canopies was also modified in response to browsing, which increased light availability at deeper canopy depths. As a result, LMA and Na increased at lower canopy depths, leading to a greater photosynthetic capacity deeper in browsed canopies compared to control canopies. This response, along with increased light availability, facilitated greater photosynthesis and resource-use efficiency deeper in browsed canopies compared to control canopies. Our results illustrate how C. drummondii facilitates high LAI canopies and a compensatory growth response to browsing-both of which are key factors contributing to the success of C. drummondii and other species responsible for grassland woody encroachment.

Rapid Quantification Method for Yield, Calorimetric Energy and Chlorophyll a Fluorescence Parameters in Nicotiana tabacum L. Using Vis-NIR-SWIR Hyperspectroscopy

High-throughput and large-scale data are part of a new era of plant remote sensing science. Quantification of the yield, energetic content, and chlorophyll a fluorescence (ChlF) remains laborious and is of great interest to physiologists and photobiologists. We propose a new method that is efficient and applicable for estimating photosynthetic performance and photosystem status using remote sensing hyperspectroscopy with visible, near-infrared and shortwave spectroscopy (Vis-NIR-SWIR) based on rapid multivariate partial least squares regression (PLSR) as a tool to estimate biomass production, calorimetric energy content and chlorophyll a fluorescence parameters. The results showed the presence of typical inflections associated with chemical and structural components present in plants, enabling us to obtain PLSR models with R2P and RPDP values greater than >0.82 and 3.33, respectively. The most important wavelengths were well distributed into 400 (violet), 440 (blue), 550 (green), 670 (red), 700−750 (red edge), 1330 (NIR), 1450 (SWIR), 1940 (SWIR) and 2200 (SWIR) nm operating ranges of the spectrum. Thus, we report a methodology to simultaneously determine fifteen attributes (i.e., yield (biomass), ΔH°area, ΔH°mass, Fv/Fm, Fv’/Fm’, ETR, NPQ, qP, qN, ΦPSII, P, D, SFI, PI(abs), D.F.) with high accuracy and precision and with excellent predictive capacity for most of them. These results are promising for plant physiology studies and will provide a better understanding of photosystem dynamics in tobacco plants when a large number of samples must be evaluated within a short period and with remote acquisition data.

Simple, Fast and Efficient Methods for Analysing the Structural, Ultrastructural and Cellular Components of the Cell Wall

Plant cell walls are a fundamental component of plant biology and play an essential role in plant growth and development. The metabolic components of the cell wall can be investigated in a fast, simple, and highly efficient manner using various and distinct microscopy techniques. Here, we report implementing a flowchart to analyse tobacco plants’ structural, ultrastructural, and metabolic components supplemented with far-red light. In addition, biochemical components, such as lignin, cellulose, phenolic compounds, and reducing sugars, present in the plant cell walls were quantified using light, fluorescence, and electron microscopy. Our data were generated from samples prepared via tissue fixation, incorporation in resins, and slicing using microtomes. Moreover, we have used routine staining and contrast techniques to characterise plant cell walls. Here, we describe several protocols that use classic and modern techniques as well as qualitative and quantitative analytical methods to study cell walls, enabling the plant research community to understand and select the most suitable methods for the microscopic analysis of metabolic components. Finally, we discuss specific ideas aimed at new students of plant anatomy and microscopy. This research not only described the structural, ultrastructural, and metabolic components of the plant cell wall, but also explained the strategies for understanding cellular development.

Responses of Linear and Cyclic Electron Flow to Nitrogen Stress in an N-Sensitive Species Panax notoginseng

Nitrogen (N) is a primary factor limiting leaf photosynthesis. However, the mechanism of N-stress-driven photoinhibition of the photosystem I (PSI) and photosystem II (PSII) is still unclear in the N-sensitive species such as Panax notoginseng, and thus the role of electron transport in PSII and PSI photoinhibition needs to be further understood. We comparatively analyzed photosystem activity, photosynthetic rate, excitation energy distribution, electron transport, OJIP kinetic curve, P700 dark reduction, and antioxidant enzyme activities in low N (LN), moderate N (MN), and high N (HN) leaves treated with linear electron flow (LEF) inhibitor [3-(3,4-dichlorophenyl)-1,1-dimethyl urea (DCMU)] and cyclic electron flow (CEF) inhibitor (methyl viologen, MV). The results showed that the increased application of N fertilizer significantly enhance leaf N contents and specific leaf N (SLN). Net photosynthetic rate (P n) was lower in HN and LN plants than in MN ones. Maximum photochemistry efficiency of PSII (F v/F m), maximum photo-oxidation P700+ (P m), electron transport rate of PSI (ETRI), electron transport rate of PSII (ETRII), and plastoquinone (PQ) pool size were lower in the LN plants. More importantly, K phase and CEF were higher in the LN plants. Additionally, there was not a significant difference in the activity of antioxidant enzyme between the MV- and H2O-treated plants. The results obtained suggest that the lower LEF leads to the hindrance of the formation of ΔpH and ATP in LN plants, thereby damaging the donor side of the PSII oxygen-evolving complex (OEC). The over-reduction of PSI acceptor side is the main cause of PSI photoinhibition under LN condition. Higher CEF and antioxidant enzyme activity not only protected PSI from photodamage but also slowed down the damage rate of PSII in P. notoginseng grown under LN.

Application of artificial neural networks and genetic algorithm to predict and optimize greenhouse banana fruit yield through nitrogen, potassium and magnesium

The excess of the chemical fertilizers not only causes the environmental pollution but also has many deteriorating effects including global warming and alteration of soil microbial diversity. In conventional researches, chemical fertilizers and their concentrations are selected based on the knowledge of experts involved in the projects, which this kind of models are usually subjective. Therefore, the present study aimed to introduce the optimal concentrations of three macro elements including nitrogen (0, 100, and 200 g), potassium (0, 100, 200, and 300 g), and magnesium (0, 50, and 100 g) on fruit yield (FY), fruit length (FL), and number of rows per spike (NRPS) of greenhouse banana using analysis of variance (ANOVA) followed by post hoc LSD test and two well-known artificial neural networks (ANNs) including multilayer perceptron (MLP) and generalized regression neural network (GRNN). According to the results of ANOVA, the highest mean value of the FY was obtained with 200 g of N, 300 g of K, and 50 g of Mg. Based on the results of the present study, the both ANNs models had high predictive accuracy (R2 = 0.66-0.99) in the both training and testing data for the FY, FL, and NRPS. However, the GRNN model had better performance than MLP model for modeling and predicting the three characters of greenhouse banana. Therefore, genetic algorithm (GA) was subjected to the GRNN model in order to find the optimal amounts of N, K, and Mg for achieving the high amounts of the FY, FL, and NRPS. The GRNN-GA hybrid model confirmed that high yield of the plant could be achieved by reducing chemical fertilizers including nitrogen, potassium, and magnesium by 65, 44, and 62%, respectively, in compared to traditional method.

Effects of Different Nitrogen Forms and Competitive Treatments on the Growth and Antioxidant System of Wedelia trilobata and Wedelia chinensis Under High Nitrogen Concentrations

Nitrogen (N) is one of the essential nutrients for plant growth. Appropriate application of N can improve the N use efficiency (NUE) and significantly promote plants' growth. However, under N toxic conditions, the relationship between the growth and antioxidant system of invasive plants under different N forms and competitive treatments is not fully understood. Therefore, in this study, the performance of invasive species Wedelia trilobata and its native species Wedelia chinensis was evaluated under two sets of N forms and ratios, namely, NH₄ ⁺-N(AN)/NO₃ ^--N(NN) = 2:1 and NH₄ ⁺-N(AN)/NO₃ ^--N(NN) = 1:2 along with two intraspecific and interspecific competitions under without N and high N level of 15 g N⋅m^-2 year^-1, respectively. Data regarding the growth indices, antioxidant enzyme activities, including peroxidase (POD) and catalase (CAT), malondialdehyde (MDA), and proline contents were determined. Results showed that for competitive treatments, growth status was better for interspecific competition than intraspecific competition. The plant biomass of W. trilobata was significantly higher than that of W. chinensis. N significantly promoted the plants' growth in terms of leaf area and biomass yield, and the antioxidant enzyme activities were significantly increased under a high N treatment than that of the control. Among N forms/ratios, ammonium N (AN)/nitrate N (NN) = 2:1 significantly enhanced the enzyme activity, particularly in W. trilobata. Furthermore, for intraspecific competition, MDA contents of W. trilobata were significantly decreased compared to that of W. chinensis. In conclusion, our results showed that W. trilobata adapted well under competitive conditions through better growth and antioxidant defense system.

Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression

Soybean grain yield has regularly been impaired by drought periods, and the future climatic scenarios for soybean production might drastically impact yields worldwide. In this context, the knowledge of soybean yield is extremely important to subsidize government and corporative decisions over technical issues. This paper aimed to predict grain yield in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least square regression (PLSR). Field experiments were undertaken at Embrapa Soja (Brazilian Agricultural Research Corporation) in the 2016/2017, 2017/2018 and 2018/2019 cropping seasons. The data collected were analyzed following a split plot model in a randomized complete block design, with four blocks. The following water conditions were distributed in the field plots: irrigated (IRR), non-irrigated (NIRR) and water deficit induced at the vegetative (WDV) and reproductive stages (WDR) using rainout shelters. Soybean genotypes with different responses to water deficit were distributed in the subplots. Soil moisture and weather data were monitored daily. A total of 7216 leaf reflectance (from 400 to 2500 nm, measured by the FieldSpec 3 Jr spectroradiometer) was collected at 24 days in the three cropping seasons. The PLSR (p ≤ 0.05) was performed to predict soybean grain yield by its leaf-based reflectance spectroscopy. The results demonstrated the highest accuracy in soybean grain yield prediction at the R5 phenological stage, corresponding to the period when grains are being formed (R2 ranging from 0.731 to 0.924 and the RMSE from 334 to 403 kg ha−1—7.77 to 11.33%). Analyzing the three cropping seasons into a single PLSR model at R5 stage, R2 equal to 0.775, 0.730 and 0.688 were obtained at the calibration, cross-validation and external validation stages, with RMSE lower than 634 kg ha−1 (13.34%). The PLSR demonstrated higher accuracy in plants submitted to water deficit both at the vegetative and reproductive periods in comparison to plants under natural rainfall or irrigation.

High nitrogen inhibits photosynthetic performance in a shade-tolerant and N-sensitive species Panax notoginseng

Nitrogen (N) is a primary factor limiting leaf photosynthesis. However, the mechanism of high-N-driven inhibition on photosynthetic efficiency and photoprotection is still unclear in the shade-tolerant and N-sensitive species such as Panax notoginseng. Leaf chlorophyll (Chl) content, Ribulose-1,5-bisphosphate carboxylase oxygenase (Rubisco) activity and content, N allocation in the photosynthetic apparatus, photosynthetic performance and Chl fluorescence were comparatively analyzed in a shade-tolerant and N-sensitive species P. notoginseng grown under the levels of moderate nitrogen (MN) and high nitrogen (HN). The results showed that Rubisco content, Chl content and specific leaf nitrogen (SLN) were greater in the HN individuals. Rubisco activity, net photosynthetic rate (A_net), photosynthetic N use efficiency (PNUE), maximum carboxylation rate (V_cmax) and maximum electron transport rate (J_max) were lower when plants were exposed to HN as compared with ones to MN. A large proportion of leaf N was allocated to the carboxylation component under the levels of MN. More N was only served as a form of N storage and not contributed to photosynthesis in HN individuals. Compared with the MN plants, the maximum quantum yield of photosystem II (F_v/F_m), non-photochemical quenching of PSII (NPQ), effective quantum yield and electron transport rate were obviously reduced in the HN plants. Cycle electron flow (CEF) was considerably enhanced in the MN individuals. There was not a significant difference in maximum photo-oxidation P₇₀₀₊ (P_m) between the HN and MN individuals. Most importantly, the HN individuals showed higher K phase in the fast chlorophyll fluorescence induction kinetic curve (OJIP kinetic curve) than the MN ones. The results obtained suggest that photosynthetic capacity might be primarily inhibited by the inactivated Rubisco in the HN individuals, and HN-induced depression of photoprotection might be caused by the photodamage to the donor side of PSII oxygen-evolving complex.

Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf-Based Hyperspectral Reflectance

Monitoring of soybean genotypes is important because of intellectual property over seed technology, better management over seed genetics, and more efficient strategies for its agricultural production process. This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf-based hyperspectral reflectance. Leaf reflectance spectra were collected using a hyperspectral proximal sensor. Two experiments were conducted as field trials: one experiment was at Embrapa Soja in the 2016/2017, 2017/2018, and 2018/2019 cropping seasons, where ten soybean genotypes were grown under four water conditions; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season, where nine soybean genotypes were evaluated. The spectral data collected was divided into nine spectral datasets, comprising single and multiple cropping seasons (from 2016 to 2019), and two contrasting crop-growing environments. Principal component analysis, applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset, explained over 94% of the spectral variance in the first three principal components. Linear discriminant analysis, used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype, achieved accuracy between 61% and 100% in the calibration procedure and between 50% and 100% in the validation procedure. Misclassification was observed only between genotypes from the same genetic background. The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf-scale, regardless of the phenological stages or water status to which plants were submitted.

Interactive Effects of Light and Nitrogen on Pakchoi (Brassica chinensis L.) Growth and Soil Enzyme Activity in an Underground Environment

Light conditions and nitrogen fertilizer are crucial for plant growth, especially in the underground situations without sunlight and nitrogen deposition. In this paper, the effects of photoperiod (12 h and 16 h lighting time per day), light intensity (200, 300 and 400 μmol m−2 s−1) and nitrogen addition (0, 0.15, 0.3 and 0.45 g N kg−1 soil) on pakchoi growth and specific soil enzyme activity were investigated. The results demonstrated that there were strong interactive effects of light intensity and nitrogen addition on plant yield. The plant yield changed parabolically with increasing nitrogen addition when a light intensity was given between 200 and 300 μmol m−2 s−1, while the yield decreased linearly with increasing nitrogen application under the light intensity of 400 μmol m−2 s−1. The combination of 16 h photoperiod, 300 μmol m−2 s−1 light intensity and 0.3 g N kg−1 soil nitrogen addition was the best for pakchoi growth. The investigation of soil enzyme showed that the activity of urease responded negatively to nitrogen addition, whereas the activity of phosphatase had positive correlation with light intensity but was not affected by nitrogen addition. Our results suggested that the toxic effect of excessive nitrogen was a better explanation for the interactive effects of light and nitrogen than the plant-microbe interaction framework. The critical toxicity level of nitrogen for pakchoi was determined and showed negative correlation with light intensity.

Effect of ammonium and nitrate supplies on nitrogen and sucrose metabolism of Cabernet Sauvignon (Vitis vinifera cv.)

BACKGROUND

The quality of wine is highly dependent on the quality of berries. Development of berries is influenced by the type and ratio of different nitrogen supplies in the soil. To understand the impact of varying sources and levels of nitrate and ammonium on sucrose and nitrogen metabolism of Vitis vinifera cv. Cabernet Sauvignon, we tested nutrient solutions with four NO₃ ^- -N:NH₄ ⁺ -N ratios (100:0, 75:25, 50:50, 0:100) through the root system.

RESULTS

The form and quantity of nitrogen affected berries and leaves with source-sink relationships. Soluble sugar levels were significantly higher in plants treated with mixed nitrogen sources (75:25 and 50:50) compared to single nitrogen sources (100:0 and 0:100). In particular, treating plants with mixed nitrogen source at a 75:25 ratio resulted in 22% higher fructose levels in berries compared to the 50:50 treatment. In addition, mixed nitrogen treatments resulted in significantly higher amino acid levels and protein content. Mixed nitrogen substrates also increased the expression of enzymes involved in both nitrogen and sucrose metabolism.

CONCLUSION

Plants did not maximize the nitrogen supply when single form nitrogen was provided, and the mixed nitrogen substrates consistently increased the amount of available carbon and nitrogen in the berries and leaves. We found that NO₃ ^- -N:NH₄ ⁺ -N ratio of 75:25 was the optimum formula for improving nitrogen and sucrose metabolism, and reducing the competition between nitrogen and sucrose. By examining the nutrient utilization of plants cultivated with different nitrogen forms, the present study provides insights into improving cultivation and production practices. © 2020 Society of Chemical Industry.

High resolution leaf spectral signature as a tool for foliar pigment estimation displaying potential for species differentiation

Optical leaf profiles depend on foliar pigment type and content, as well as anatomical aspects and cellular ultrastructure, whose effects are shown in several species. Monocotyledon and Dicotyledon plants presenting natural pigment content variations and anatomical alterations were analyzed. Each plant species displays its own spectral signatures, which are, in turn, influenced by foliar pigment class (composition) and concentration, as well as anatomical and ultrastructural plant cell characteristics. Plants with no anthocyanin displayed increased reflectance and transmittance in the green spectral region (501-565 nm), while values decreased in the presence of anthocyanin. At wavelengths below 500 nm (350-500 nm), strong overlapping signatures of phenolics, carotenoids, chlorophylls, flavonoids and anthocyanins were observed. Using a partial least squares regression applied to 350-700 nm spectral data allowed for accurate estimations of different foliar pigment levels. In addition, a PCA and discriminant analysis were able to efficiently discriminate different species displaying spectra overlapping. The use of absorbance spectra only was able to discriminate species with 100 % confidence. Finally, a discussion on how different wavelengths are absorbed and on anatomical interference of light interaction in leaf profiles is presented.