Spectral Readings Research Articles

Demonstration the effectiveness of NDVI with GGE biplot model in determination of high yielding durum wheat (Triticum durum Desf.) genotypes

The use of spectral indices such as normalised difference vegetation index (NDVI) is becoming an important element in the evaluation of physiological traits in cereal crops. Determining the correlation between spectral readings taken at different phenological stages of wheat plants and grain yield (GY) is crucial. This study aimed to demonstrate the effectiveness of NDVI in combination with the GGE biplot model in identifying high yielding durum wheat genotypes. Field experiments were conducted in multiple environments and growth stages to evaluate the relationship between NDVI and grain yield (GY). Twenty-five durum wheat genotypes were tested for two years under rainfed and supplementary irrigation conditions using a split plot experimental design. The results showed significant positive correlations between NDVI and GY, especially at the generative stages under rain-fed conditions. In contrast, NDVI at the grain-filling stage showed a weaker relationship with GY under supplemental irrigation. Genotypes exhibiting high NDVI values up to the stem elongation stage tended to have lower grain yields, emphasizing the importance of considering growth stage dynamics. GGE biplot analysis provided visual information on genotype-trait relationships, helping to identify genotypes with consistent performance in different environments. This study highlights that NDVI can be used to predict yield potential and guide selection in durum wheat breeding programs and the GGE biplot model serves as a valuable tool for genotype evaluation and selection.

Eucalyptus Species Discrimination Using Hyperspectral Sensor Data and Machine Learning

The identification of tree species is very useful for the management and monitoring of forest resources. When paired with machine learning (ML) algorithms, species identification based on spectral bands from a hyperspectral sensor can contribute to developing technologies that enable accurate forest inventories to be completed efficiently, reducing labor and time. This is the first study to evaluate the effectiveness of classification of five eucalyptus species (E. camaldulensis, Corymbia citriodora, E. saligna, E. grandis, and E. urophyla) using hyperspectral images and machine learning. Spectral readings were taken from 200 leaves of each species and divided into three dataset sizes: one set containing 50 samples per species, a second with 100 samples per species, and a third set with 200 samples per species. The ML algorithms tested were multilayer perceptron artificial neural network (ANN), decision trees (J48 and REPTree algorithms), and random forest (RF). As a control, a conventional approach by logistic regression (LR) was used. Eucalyptus species were classified by ML algorithms using a randomized stratified cross-validation with 10 folds. After obtaining the percentage of correct classification (CC) and F-measure accuracy metrics, the means were grouped by the Scott–Knott test at 5% probability. Our findings revealed the existence of distinct spectral curves between the species, with the differences being more marked from the 700 nm range onwards. The most accurate ML algorithm for identifying eucalyptus species was ANN. There was no statistical difference for CC between the three dataset sizes. Therefore, it was determined that 50 leaves would be sufficient to accurately differentiate the eucalyptus species evaluated. Our study represents an important scientific advance for forest inventories and breeding programs with applications in both forest plantations and native forest areas as it proposes a fast, accurate, and large-scale species-level classification approach.

Instrumentação de espectroscopia no infravermelho próximo para previsão bromatológica do farelo de trigo

Wheat bran samples were collected during 12 months in the Brazilian states (Ceará, Paraíba and Rio Grande do Sul) and sent to Embrapa Caprinos e Ovinos, for analytical procedures. The samples received (300 copies) were destined to a first spectral reading in an NIR Perten instrument. Within this reading, 107 samples were selected, classified as (in natura samples read in NIR Perten). Then, the reference analyzes were carried out simultaneously with a second reading again in a NIR Perten instrument, classified as (dry and ground samples read in NIR Perten) and a third reading, in a NIR FOSS instrument, classified as (dry and ground samples read in NIR FOSS). Models for crude protein (CP) and in vitro digestibility of dry matter (IVDMD) and organic matter (IVOMD) were developed for both instruments with the three spectral readings performed. NIR instrumentation, associated with chemometric techniques, accurately predicted crude protein, in vitro dry matter digestibility and in vitro organic matter digestibility of wheat bran.

Use of Machine Learning Algorithms in the Classification of Forest Species

Optimization in the process of managing forest resources seeks alternatives that make data collection possible. One of them alternatives is spectroradiometry, which consists of measuring the spectral response, having as product the response of the target in relation to the incident radiation along the electromagnetic spectrum, and that, using machine learning, with pre-selected models, makes it possible to identify. Given the above, the study aimed to use machine learning algorithms to classify species by vegetation indices from reflectance data. The study was developed at the Federal University from Santa Maria, working with the species Ficus benjamina, Inga marginata, Handroanthus chrysotrichus, Psidium cattleianum, Salix humboldtiana, Corymbia citriodora and Myrcianthes pungens, and spectral readings of the leaves were taken using the FieldSpec®3 spectroradiometer connected to RTS-3ZC3 integrating sphere. The reflectance values with wavelength ranged in amplitude from 350 ƞm to 2,500 ƞm and spectral resolution of 1 ƞm. Vegetation indices were calculated using the software R Studio, being: NDVI, SAVI, RVI, GNDVI, NDWI, NDWI2, GEMI, DVI, TVI, RVI, MSAVI, WDVI. The algorithms used to develop machine learning were: Random Forest (RF), k-Nearest Neighbors (K-NN), Naive Bayes (NB) and Support Vector Machine (SVM). RF proves to be the most appropriate for data validation, with 85% global accuracy, followed by SVM, with 71%, K-NN with 64% and NB with 35%. The indices with the best performance to point the species were NDWI and SAVI.

Spectral Mixture Modeling of an ASTER Bare Soil Synthetic Image Using a Representative Spectral Library to Map Soils in Central-Brazil

Pedological maps in suitable scales are scarce in most countries due to the high costs involved in soil surveying. Therefore, methods for surveying and mapping must be developed to overpass the cartographic material obtention. In this sense, this work aims at assessing a digital soil map (DSM) built by multispectral data extrapolation from a source area to a target area using the ASTER time series modeling technique. For that process, eight representative toposequences were established in two contiguous micro-watersheds, with a total of 42 soil profiles for analyses and classification. We found Ferralsols, Plinthosols, Regosols, and a few Cambisols, Arenosols, Gleisols, and Histosols, typical of tropical regions. In the laboratory, surface soil samples were submitted to spectral readings from 0.40 µm to 2.50 µm. The soil spectra were morphologically interpreted, identifying shapes and main features typical of tropical soils. Soil texture grouped the curves by cluster analysis, forming a spectral library (SL). In parallel, an ASTER time series (2001, 2004, and 2006) was processed, generating a bare soil synthetic soil image (SySI) covering 39.7% of the target area. Multiple Endmember Spectral Mixture Analysis modeled the SL on the SySI generating DSM with 73% of Kappa index, in which identified about 77% is covered by rhodic Ferralsols. Besides the overestimation, the DSM represented the study area’s pedodiversity. Given the discussion raised, we consider including subsoil data and other features using other sensors in operations modeled by machine learning algorithms to improve results.

Spectral Method for Liming Recommendation in Oxisol Based on the Prediction of Chemical Characteristics Using Interval Partial Least Squares Regression

Thousands of chemical analyses are carried out annually with the aim of recommending soil correction; however, these analyses are expensive, destructive, time-consuming, and can be harmful to the environment. As an alternative to conventional analysis methods, diffuse reflectance spectroscopy has been proposed as an option for evaluating the chemical characteristics of soil. The selection of variables has also emerged as an alternative to improve the performance of PLSR (partial least squares regression), as it decreases the root mean square error (RMSE) and increases the accuracy of the models. However, few studies have used a previous selection of variables for the construction of PLSR models to estimate the chemical characteristics of soil. In this context, the hypothesis in this study was that it is possible to calculate the liming recommendation in Oxisol based on the chemical characteristics estimated by PLSR, with a previous selection of variables using iPLS (Interval PLS). The objective was to calculate the need for liming based on chemical characteristics estimated via iPLS selection and PLSR modeling of specific wavelengths of soil reflectance. The experimental area was treated with different application rates of limestone, with and without incorporation, and phosphogypsum was applied in additional treatments. Soil assessments were carried out 5, 12, 24, and 36 months after the application of the treatments, using six layers: 0.00–0.05, 0.05–0.10, 0.10–0.20, 0.20–0.30, 0.30–0.40 and 0.40–0.60 m. Samples were subjected to conventional laboratory analyses, and spectral readings (400–2500 nm) were obtained with a spectroradiometer. The spectral curves were subjected to the iPLS variable selection method to generate PLSR models of the chemical characteristics used to calculate the liming recommendation. The chemical characteristics of the soil, such as Ca2+, sum of bases (SB), effective cation exchange capacity (CTCe), cation exchange capacity (CTC), and base saturation (BS), could be estimated, with values of R2 ranging from 0.83 to 0.92 in the calibration and validation steps, and from 0.84 to 0.90 for the prediction step (in the fourth assessment). The liming recommendation calculated based on the chemical characteristics predicted from the PLSR models showed a strong correlation (r > 0.86) with the liming recommendation calculated by conventional laboratory techniques. The fourth soil assessment yielded the best correlation coefficient (r = 0.95).

Detection of adulterated coffee by fourier-transform infrared (FTIR) spectroscopy associated with sensory analysis

Because of its huge economic value, coffee has been the target of adulteration worldwide. Given the successful application of spectroscopic methods in detecting adulterants, this study aimed to employ attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) to detect adulterants in roasted coffee samples and compare the results with that of sensory analysis. In this study, twelve coffee samples were intentionally adulterated with varying concentrations, i.e., 10%, 30%, and 50%, of corn, beans, sawdust, and coffee husk. These adulterated samples were compared with one unadulterated coffee sample and four roasted and ground commercially available coffee samples; spectral readings of caffeine and chlorogenic acid (CGA) standards were performed for reference. The sensory analysis was performed by 17 tasters who were trained by a Q-grader. The infrared (IR) spectra (FTIR) data were processed by multiplicative signal correction (MSC) and subjected to a principal component analysis (PCA), along with the results of the sensory analysis. The combination of sensory analysis and IR spectrum allowed to differentiate samples of adulterated coffee and unadulterated coffee by PCA, with an explanation of 79% variance. The results demonstrated that the wavenumbers associated with CGA and caffeine contribute significantly in distinguishing adulterated coffee samples.Key words: Adulterants; coffee quality; chemometric; sensory analysis.

Measuring reflectance of tiny organisms: The promise of species level biocrust remote sensing

Abstract To understand how ecological communities will respond to global change we need new tools and datasets on species across large spatial and temporal scales. Hyperspectral reflectance ‘spectra’ capture a promising set of traits that show potential to be scaled up in time and space via remote sensing. Thus far, spectra have been shown to distinguish the taxa and trait responses of a substantial number of species within a plethora of vascular plant communities, but not yet for biological soil crust communities (biocrusts). Here, we assess if spectra can be applied to identify biocrust species and their trait variation. We collected biocrust specimens across an aridity gradient spanning 650 km within drylands of Eastern Australia and acquired their spectra, over 12,700 spectral readings, with a high‐resolution radiospectrometer. A machine learning method (random forests) was used to assess how well the spectra of biocrust specimens could distinguish their species and broader structural and chemical traits. Spectra were able to differentiate a substantial number of biocrust species (35) with considerable accuracy (~78.5%). Furthermore, spectral features related to chemical traits were found to primarily drive species spectral differences. Synthesis. Our findings establish that biocrust species hold unique and detectable spectral responses, providing an important basis for remote sensing applications on biocrust species and their trait responses across dryland systems.

Measuring Carbonate Rock Strength using Spectroscopy across the Optical and Thermal Region

We seek to define new empirical relations between the spectral signature of rock surfaces and their geomechanical characteristics. By this means, proxy tools can be developed to classify the quality of rock masses in situations where access is restricted or impossible. In rock mechanics research, remote sensing technologies are mainly used for mapping surface geometry and rock discontinuities. However, the characterization of intact rock’s mechanical properties via remote sensing is a technique that has not yet been developed. In this study, spectral information is implemented using optical and thermal spectral regions. This technique enables identifying and recognizing objects based on the spectral absorption features of specific chemical attributes. In general, spectral acquisition of rocks can be done using stationary or moving (airborne) platforms. Assuming the chemical and physical attributes of rocks correlate to the material physical and chemical properties we find the spectral-based model for assessing rock strength remotely. As part of a preliminary proof-of-concept stage, we collected multiple cylindrical samples of different carbonate formations from several rock outcrops in Israel. We used point and imaging hyperspectral remote sensors to measure the surfaces of the samples in the visible and infrared regions. We determined the uniaxial compressive strength of the rock samples and analyzed them, to generate a model describing the strength of the samples, solely from spectral readings at both ranges. In addition, we conducted in-situ measurements of rock strength using a Schmidt hammer and rock surface spectra in the visible region on rock outcrops. The results of this pioneering study show that the correlation between carbonated rock strength and its spectral signatures enabled the remote assessment of the carbonate rock strength and led the way to an airborne remote sensing application. We continue to extend the database and further refine the analytical method, using this to establish a solid spectral-based model to depict rock strength remotely through exploiting the hyperspectral technology from stationary and airborne platforms.

Using near-infrared spectroscopy to discriminate closely related species: a case study of neotropical ferns.

Identifying plant species requires considerable knowledge and can be difficult without complete specimens. Fourier-transform near-infrared spectroscopy (FT-NIR) is an effective technique for discriminating plant species, especially angiosperms. However, its efficacy has never been tested on ferns. Here we tested the accuracy of FT-NIR at discriminating species of the genus Microgramma. We obtained 16 spectral readings per individual from the adaxial and abaxial surfaces of 100 specimens belonging to 13 species. The analyses included all 1557 spectral variables. We tested different datasets (adaxial + abaxial, adaxial, and abaxial) to compare the correct identification of species through the construction of discriminant models (Linear discriminant analysis and partial least squares discriminant analysis) and cross-validation techniques (leave-one-out, K-fold). All analyses recovered an overall high percentage (> 90%) of correct predictions of specimen identifications for all datasets, regardless of the model or cross-validation used. On average, there was > 95% accuracy when using partial least squares discriminant analysis and both cross-validations. Our results show the high predictive power of FT-NIR at correctly discriminating fern species when using leaves of dried herbarium specimens. The technique is sensitive enough to reflect species delimitation problems and possible hybridization, and it has the potential of helping better delimit and identify fern species.

Strategies for the Development of Spectral Models for Soil Organic Matter Estimation

Visible (V), Near Infrared (NIR) and Short Waves Infrared (SWIR) spectroscopy has been indicated as a promising tool in soil studies, especially in the last decade. However, in order to apply this method, it is necessary to develop prediction models with the capacity to capture the intrinsic differences between agricultural areas and incorporate them in the modeling process. High quality estimates are generally obtained when these models are applied to soil samples displaying characteristics similar to the samples used in their construction. However, low quality predictions are noted when applied to samples from new areas presenting different characteristics. One way to solve this problem is by recalibrating the models using selected samples from the area of interest. Based on this premise, the aim of this study was to use the spiking technique and spiking associated with hybridization to expand prediction models and estimate organic matter content in a target area undergoing different uses and management. A total of 425 soil samples were used for the generation of the state model, as well as 200 samples from a target area to select the subsets (10 samples) used for model recalibration. The spectral readings of the samples were obtained in the laboratory using the ASD FieldSpec 3 Jr. Sensor from 350 to 2500 nm. The spectral curves of the samples were then associated to the soil attributes by means of a partial least squares regression (PLSR). The state model obtained better results when recalibrated with samples selected through a cluster analysis. The use of hybrid spectral curves did not generate significant improvements, presenting estimates, in most cases, lower than the state model applied without recalibration. The use of the isolated spiking technique was more effective in comparison with the spiked and hybridized state models, reaching r2, square root of mean prediction error (RMSEP) and ratio of performance to deviation (RPD) values of 0.43, 4.4 g dm−3, and 1.36, respectively.

Non-destructive determination of fatty acid composition of in-shell and shelled almonds using handheld NIRS sensors

One of the major compounds in almond kernels, which determines their nutritional quality, are lipids. The aim of this research was to determine the fatty acid profile in intact in-shell and shelled almonds (145 samples) using two new generation handheld near infrared spectroscopy (NIRS) sensors, of different optical design and technical specifications, adapted for in situ analysis in different stages in the food supply chain: in the industry after harvest, at the reception points and during postharvest storage. For both instruments, two procedures for taking near infrared (NIR) spectra were tested: (1) static, where point spectral readings were taken of almonds placed on trays; (2) dynamic, where spectra were taken by scanning the entire trays. Modified partial least squares (MPLS) regression models were developed using NIR spectra with different combinations of signal pre-treatments — derivative and scatter correction methods. The residual predictive deviation for cross validation (RPDcv) of the best models developed for the prediction of palmitic, stearic, oleic, and linoleic acids using shelled almonds were 2.40, 2.16, 3.98, and 3.77, respectively, and 1.73, 1.73, 2.02, and 2.11 for the in-shell almonds. These results confirm the feasibility of NIRS technology to measure the fatty acid profile in in-shell and shelled almonds. A comparison between the presentation mode (in-shell or shelled) and analysis mode (static or dynamic) showed that the best results were obtained for shelled almonds analysed in dynamic mode.

A SonarQube Static Analysis of the Spectral Workbench

The Spectral Workbench is an open-source, community driven software suite to obtain and disseminate spectral data. It consists of a client application that collects spectral readings and a server application that is an online database of spectral data. It is difficult to detect software defects in the Spectral Workbench application. A static analysis tool, SonarQube, was selected to find these defects. Numerous defects were detected and documented. SonarQube will increase the reliability of the Spectral Workbench, which provides numerous benefits including increased confidence in its data and effectiveness which will drive additional number of users for spectral repository data collection.

Revisit and optimisation of spectral data collection techniques from vegetation using handheld non-imaging spectroscopic sensor for minimising errors

Spectral data are now-a-days widely used for assessment of biochemical, biophysical and structural traits of vegetation by analysing their spectral signatures. In field, it is recorded by handheld spectroradiometer sensor. In doing so, the spectral data acquisition is influenced by several factors such as variations in light intensity during recording; number of spectral readings per plant; distance between sensor and plant; impact of heating due to sun, wind, and wetness of plants. Thestandard operating practices for such data acquisition are generally based on the similar work earlier carried out by researchers in bits and pieces. In the present study experimental set-ups have been laid for systematic studies to answer the influence of above factors on spectral data and required optimisations have been suggested. It was found that variations in light intensity influence the spectral readings, when the illumination difference was more than ∼20%. The 30 spectral readings were found optimum. The reflectance spectra recorded at distance of 20-35 cm were treated as pure spectra in case of 25 °FOV sensors. The heating of samples due to sun, speed and direction of wind, and wetness of samples influenced the plant spectral reflectance . It was also observed that averaging of spectra recorded in several observations (instead of single observation) for the same plant samples optimize the errors. It was concluded that in order to have a good quality of vegetation field spectral data, case to case calibrations, and care must be taken to eliminate influence of surrounding environmental variables while spectral data acquisition to avoid/minimize the errors.

Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms

Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers.

Final and Larval Spectrality in Albert Camus’ The Fall

This philosophical survey of literature introduces a spectral reading of Albert Camus’ multi-layered text The Fall. Readings on the spectral usually embark on the elements that haunt the subjectivity of the narrator, Jean-Baptiste Clamence. Within the affluence of potential meanings and allusions in the text, I focus on two places—Paris and Amsterdam—that have become “possessed,” or more specifically, places that have become “spaces of the spectral.” Amid the text’s enigmatic character, a spectral reading highlights the particular uncanny turn that disturbs in the plot. I argue that the mentions of Paris and Amsterdam in the text function within the ambit of the spectral. Reviewing the models for spectral readings of place, I make use of Giorgio Agamben’s lens of spectrality. For Agamben, there breed two types of spectrality in a place, namely, ‘Final’ and ‘Larval.’ Putting this perspective on the position of Clamence, this reading respectively exposes Paris as having final spectrality and Amsterdam as having larval spectrality.

Host-guest inclusion complex of β-cyclodextrin and 4,4′-(1,4-phenylenediisopropylidene)bisaniline: Spectral, structural and molecular modeling studies

The Host-Guest inclusion complex of β-Cyclodextrin (β-CD) and 4,4′-(1,4-phenylenediisopropylidene)bisaniline (PDB) was prepared in solid and solution state and characterized by UV-Visible, Fluorescence, FT-IR, Time resolved fluorescence spectroscopy, DSC, SEM, AFM, XRD and Molecular modeling approach. These studies were adopted to approve the formation of PDB: β-CD inclusion complex . Job's plot and Benesi-Hildebrand plot obtained by using UV and Fluorescence spectral readings confirmed the 1:2 PDB: β-CD complex formation.. FT-IR study revealed that aromatic rings of PDB are partially encapsulated in to the β-CD cavity. The interaction between PDB and β-CD is shown by enhancement of thermal stability of PDB after the complexation. AFM and SEM analysis were also confirmed inclusion complex formation in solid state. Molecular docking study revealed the fact that 1:2 inclusion complex possesses a good molecular shape complementarity score for its most probable structure.

Monitoring of Canopy Reflectance Change Based on Flowering Rate

Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy.

Simulating multispectral MSI bandsets (Sentinel-2) from hyperspectral observations via spectroradiometer for identifying soybean cultivars

Monitoring soybean areas by remote sensing is extremely useful, especially in Brazil, which has a large territorial extension and where soybean cultivation has spread to all regions of the country. In this sense, the development of remote sensing techniques that enable the quantification and discrimination of soybean areas and now in cultivated cultivar level is of crucial importance for the soybean production chain in Brazil. This study aimed to discriminate soybean cultivars as a function of different hyperspectral bands using the sensor-system MSI-Sentinel-2 (Vis-NIR-SWIR) as a simulation and sample sizes using multivariate statistics to determine if the specific bands of this sensor are capable of performing such discrimination. Four soybean cultivation areas in the Midwest region cultivated with four cultivars (BMX Potência, NA5909, Don Mario, and FT Campo Mourão) were assessed. Spectral readings from each sample soybean leaf were performed, and a total of 2400 vegetation spectral readings were obtained. Data were composed of 28 bands and 22 reflectance factor height (RID) values for each soybean cultivar. Multivariate statistical analysis was performed to verify the association between soybean cultivars and their relationship with hyperspectral bands, as well as to verify the possibility of cultivar differentiation based on hyperspectral bands. The results obtained demonstrated to be possible discriminate soybean cultivars by using multivariate techniques applied to multi and hyperspectral data. The bands that contributed significantly (>5%) to cultivar differentiation in order of importance were: B26, B27, A17, A21, A20 and A14. Discriminant analysis was efficient in the cultivar classification, and canonical variable analysis revealed bands associated with specific discrimination of each cultivar. Bands that most contributed to cultivar discrimination were also identified for MSI orbital sensor.

Binding of cationic Cm-E2O-Cm gemini surfactants with human serum albumin and the role of β-cyclodextrin

The non-covalent interaction of human serum albumin (HSA) with cationic Cm-E2O-Cm gemini surfactants has been investigated employing a host of techniques. The fluorescence emission experiments reveal remarkable blue shifts after addition of the geminis. Pyrene micropolarity data are indicative of steep decrease in polarity sensed by the probe which, in turn, point towards the closeness of hydrophobic moieties of Cm-E2O-Cm and the serum albumin. UV absorbance of HSA increased with sequential addition of each gemini surfactant. CD spectral readings indicate significant unfolding of α-helices. Molecular docking studies predict a spontaneous binding between the geminis and the serum albumin. When all the above mentioned studies were carried out in the presence of 1.679 mM of β-cyclodextrin (β-CD), significant alteration in the results was obtained. The blue shifts in the fluorescence emission spectra were reduced. The polarity curves were less steep and the UV absorbance values were modified. The α-helical content of HSA was also influenced. These changes are ascribed to the interaction of β-CD and Cm-E2O-Cm gemini surfactants which has influence on their interaction with HSA. This is in conformity with the molecular docking results where hydrogen bonding between β-CD and Cm-E2O-Cm gemini surfactants was found to be a significant possibility.