Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants.

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

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.

Comparative Insights into Photosynthetic, Biochemical, and Ultrastructural Mechanisms in Hibiscus and Pelargonium Plants.

Understanding photosynthetic mechanisms in different plant species is crucial for advancing agricultural productivity and ecological restoration. This study presents a detailed physiological and ultrastructural comparison of photosynthetic mechanisms between Hibiscus (Hibiscus rosa-sinensis L.) and Pelargonium (Pelargonium zonale (L.) L'Hér. Ex Aiton) plants. The data collection encompassed daily photosynthetic profiles, responses to light and CO2, leaf optical properties, fluorescence data (OJIP transients), biochemical analyses, and anatomical observations. The findings reveal distinct morphological, optical, and biochemical adaptations between the two species. These adaptations were associated with differences in photochemical (AMAX, E, Ci, iWUE, and α) and carboxylative parameters (VCMAX, ΓCO2, gs, gm, Cc, and AJMAX), along with variations in fluorescence and concentrations of chlorophylls and carotenoids. Such factors modulate the efficiency of photosynthesis. Energy dissipation mechanisms, including thermal and fluorescence pathways (ΦPSII, ETR, NPQ), and JIP test-derived metrics highlighted differences in electron transport, particularly between PSII and PSI. At the ultrastructural level, Hibiscus exhibited optimised cellular and chloroplast architecture, characterised by increased chloroplast density and robust grana structures. In contrast, Pelargonium displayed suboptimal photosynthetic parameters, possibly due to reduced thylakoid counts and a higher proportion of mitochondria. In conclusion, while Hibiscus appears primed for efficient photosynthesis and energy storage, Pelargonium may prioritise alternative cellular functions, engaging in a metabolic trade-off.

Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants.

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

Decreased Photosynthetic Efficiency in Nicotiana tabacum L. under Transient Heat Stress.

Heat stress is an abiotic factor that affects the photosynthetic parameters of plants. In this study, we examined the photosynthetic mechanisms underlying the rapid response of tobacco plants to heat stress in a controlled environment. To evaluate transient heat stress conditions, changes in photochemical, carboxylative, and fluorescence efficiencies were measured using an infrared gas analyser (IRGA Licor 6800) coupled with chlorophyll a fluorescence measurements. Our findings indicated that significant disruptions in the photosynthetic machinery occurred at 45 °C for 6 h following transient heat treatment, as explained by 76.2% in the principal component analysis. The photosynthetic mechanism analysis revealed that the dark respiration rate (Rd and Rd*CO2) increased, indicating a reduced potential for carbon fixation during plant growth and development. When the light compensation point (LCP) increased as the light saturation point (LSP) decreased, this indicated potential damage to the photosystem membrane of the thylakoids. Other photosynthetic parameters, such as AMAX, VCMAX, JMAX, and ΦCO2, also decreased, compromising both photochemical and carboxylative efficiencies in the Calvin-Benson cycle. The energy dissipation mechanism, as indicated by the NPQ, qN, and thermal values, suggested that a photoprotective strategy may have been employed. However, the observed transitory damage was a result of disruption of the electron transport rate (ETR) between the PSII and PSI photosystems, which was initially caused by high temperatures. Our study highlights the impact of rapid temperature changes on plant physiology and the potential acclimatisation mechanisms under rapid heat stress. Future research should focus on exploring the adaptive mechanisms involved in distinguishing mutants to improve crop resilience against environmental stressors.

Biophysical, Biochemical, and Photochemical Analyses Using Reflectance Hyperspectroscopy and Chlorophyll a Fluorescence Kinetics in Variegated Leaves.

The adjustments that occur during photosynthesis are correlated with morphological, biochemical, and photochemical changes during leaf development. Therefore, monitoring leaves, especially when pigment accumulation occurs, is crucial for monitoring organelles, cells, tissue, and whole-plant levels. However, accurately measuring these changes can be challenging. Thus, this study tests three hypotheses, whereby reflectance hyperspectroscopy and chlorophyll a fluorescence kinetics analyses can improve our understanding of the photosynthetic process in Codiaeum variegatum (L.) A. Juss, a plant with variegated leaves and different pigments. The analyses include morphological and pigment profiling, hyperspectral data, chlorophyll a fluorescence curves, and multivariate analyses using 23 JIP test parameters and 34 different vegetation indexes. The results show that photochemical reflectance index (PRI) is a useful vegetation index (VI) for monitoring biochemical and photochemical changes in leaves, as it strongly correlates with chlorophyll and nonphotochemical dissipation (Kn) parameters in chloroplasts. In addition, some vegetation indexes, such as the pigment-specific simple ratio (PSSRc), anthocyanin reflectance index (ARI1), ratio analysis of reflectance spectra (RARS), and structurally insensitive pigment index (SIPI), are highly correlated with morphological parameters and pigment levels, while PRI, moisture stress index (MSI), normalized difference photosynthetic (PVR), fluorescence ratio (FR), and normalized difference vegetation index (NDVI) are associated with photochemical components of photosynthesis. Combined with the JIP test analysis, our results showed that decreased damage to energy transfer in the electron transport chain is correlated with the accumulation of carotenoids, anthocyanins, flavonoids, and phenolic compounds in the leaves. Phenomenological energy flux modelling shows the highest changes in the photosynthetic apparatus based on PRI and SIPI when analyzed with Pearson's correlation, the hyperspectral vegetation index (HVI) algorithm, and the partial least squares (PLS) to select the most responsive wavelengths. These findings are significant for monitoring nonuniform leaves, particularly when leaves display high variation in pigment profiling in variegated and colorful leaves. This is the first study on the rapid and precise detection of morphological, biochemical, and photochemical changes combined with vegetation indexes for different optical spectroscopy techniques.

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops.

Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R2 values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C3 and C4 plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, ß-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms.

In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R2 and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.

Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy.

Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV-VIS-NIR-SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA3) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA3 concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R2CV values ranging from 0.81 to 0.87 and RPDP values exceeding 2.09 for all parameters. Based on Pearson's coefficient XYZ interpolations and HVI algorithms, the NIR-SWIR band combination proved the most effective for predicting height and leaf area, while VIS-NIR was optimal for optimal energy yield, and VIS-VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s-PLS were most significant for SWIR1 and SWIR2, while i-PLS showed a more uniform distribution in VIS-NIR-SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy.

Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms-such as PLS, VIP, iPLS-VIP, GA, RF, and CARS-the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation.

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.

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.

Cell wall structure and composition is affected by light quality in tomato seedlings.

Light affects many aspects of cell development. Tomato seedlings growing at different light qualities (white, blue, green, red, far-red) and in the dark displayed alterations in cell wall structure and composition. A strong and negative correlation was found between cell wall thickness and hypocotyl growth. Cell walls was thicker under blue and white lights and thinner under far-red light and in the dark, while intermediate values was observed for red or green lights. Additionally, the inside layer surface of cell wall presented random deposited microfibrillae angles under far-red light and in the dark. However, longitudinal transmission electron microscopy indicates a high frequency of microfibrils close to parallels related to the elongation axis in the outer layer. This was confirmed by ultra-high resolution small angle X-ray scattering. These data suggest that cellulose microfibrils would be passively reoriented in the longitudinal direction. As the cell expands, the most recently deposited layers (inside) behave differentially oriented compared to older (outer) layers in the dark or under FR lights, agreeing with the multinet growth hypothesis. High Ca and pectin levels were found in the cell wall of seedlings growing under blue and white light, also contributing to the low extensibility of the cell wall. Low Ca and pectin contents were found in the dark and under far-red light. Auxins marginally stimulated growth in thin cell wall circumstances. Hypocotyl growth was stimulated by gibberellins under blue light.

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

A nitrogen supply is necessary for all plants. The multifaceted reasons why this nutrient stimulates plant dry weight accumulation are assessed herein. We compared tomato plants grown in full sunlight and in low light environments under four N doses and evaluated plant growth, photosynthetic and calorimetric parameters, leaf anatomy, chloroplast transmission electron microscopy (TEM) and a high resolution profile of optical leaf properties. Increases in N supplies allow tomato plants to grow faster in low light environments (91.5% shading), displaying a robust light harvesting machinery and, consequently, improved light harvesting efficiency. Ultrastructurally, high N doses were associated to a high number of grana per chloroplast and greater thylakoid stacking, as well as high electrodensity by TEM. Robust photosynthetic machinery improves green light absorption, but not blue or red. In addition, low construction and dark respiration costs were related to improved total dry weight accumulation in shade conditions. By applying multivariate analyses, we conclude that improved green light absorbance, improved quantum yield and greater palisade parenchyma cell area are the primary components that drive increased plant growth under natural light-limited photosynthesis.

Increased Gibberellins and Light Levels Promotes Cell Wall Thickness and Enhance Lignin Deposition in Xylem Fibers.

Light intensity and hormones (gibberellins; GAs) alter plant growth and development. A fine regulation triggered by light and GAs induces changes in stem cell walls (CW). Cross-talk between light-stimulated and GAs-induced processes as well as the phenolic compounds metabolism leads to modifications in lignin formation and deposition on cell walls. How these factors (light and GAs) promote changes in lignin content and composition. In addition, structural changes were evaluated in the stem anatomy of tobacco plants. GA3 was sprayed onto the leaves and paclobutrazol (PAC), a GA biosynthesis inhibitor, via soil, at different irradiance levels. Fluorescence microscopy techniques were applied to detect lignin, and electron microscopy (SEM and TEM) was used to obtain details on cell wall structure. Furthermore, determination of total lignin and monomer contents were analyzed. Both light and GAs induces increased lignin content and CW thickening as well as greater number of fiber-like cells but not tracheary elements. The assays demonstrate that light exerts a role in lignification under GA3 supplementation. In addition, the existence of an exclusive response mechanism to light was detected, that GAs are not able to replace.