Not all apparently gynandromorphic butterflies are gynandrous: The case of Polyommatus icarus and its relatives (Lepidoptera: Lycaenidae)

Beside the more than two thousand normal specimens of Polyommatus icarus (Rottemburg, 1775) yielded by rearing experiments, there was one perfectly bilateral dichromatic individual first considered to be gynandrous. On the basis of analysing genitalia traits, wing surface covering scale micromorphology, and the spectral characteristics of the blue colour generated by the cover scales, the gender of the specimen has been identified as female. This exemplar was investigated in comparison with gynandrous specimens from the collections of the Hungarian Natural History Museum exhibiting various degrees of intermixing of blue and brown coloration. Focus stacking microscopy for detailed scale morphology and UV-visible reflectance spectroscopy was used for the characterization of the optical properties. Inspecting literature references and the Lycaenidae collection of the museum, further examples have been found for female bilateral dichromatism in the closely related polyommatine lycaenid species Lysandra bellargus (Rottemburg, 1775) and Lysandra coridon (Poda, 1761) what suggests that polyommatine female dichromaticity may be displayed by the manner of bilaterality and mosaicism, phenomena hitherto solely connected to gynandromorphy.

Detectability of Surface Biosignatures for Directly Imaged Rocky Exoplanets

Modeling the detection of life has never been more opportune. With next-generation space telescopes, such as the currently developing Habitable Worlds Observatory (HWO) concept, we will begin to characterize rocky exoplanets potentially similar to Earth. However, few realistic planetary spectra containing surface biosignatures have been paired with direct imaging telescope instrument models. Therefore, we use a HWO instrument noise model to assess the detection of surface biosignatures affiliated with oxygenic, anoxygenic, and nonphotosynthetic extremophiles. We pair the HWO telescope model to a one-dimensional radiative transfer model to estimate the required exposure times necessary for detecting each biosignature on planets with global microbial coverage and varying atmospheric water vapor concentrations. For modeled planets with 0-50% cloud coverage, we determine pigments and the red edge could be detected within 1000 hr (100 hr) at distances within 15 pc (11 pc). However, tighter telescope inner working angles (2.5 λ/D) would allow surface biosignature detection at further distances. Anoxygenic photosynthetic biosignatures could also be more easily detectable than nonphotosynthetic pigments and the photosynthetic red edge when compared against a false positive iron oxide slope. Future life detection missions should evaluate the influence of false positives on the detection of multiple surface biosignatures.

Tracing sediment sources in a plain river network area by using optimized experimental design and reflectance spectroscopy

Soil erosion in a plain river network area with dense rivers, fertile land, and agricultural development is easily causes river siltation, agricultural non-point source pollution, and water eutrophication. Therefore, the negative impact of the sediment on the environment cannot be underestimated. Most traditional sediment fingerprint tracing studies have focused on mountain basins and lack a scheme suitable for plain river network sediment tracing. Here, a typical plain river network in the Taihu Basin was selected as the study area. The flow structure and characteristics were analysed, and a sampling scheme for the stream segment and a two-step model of sediment tracing in a plain river network were proposed to quantitatively distinguish the types of sediment sources. The results indicated that the traditional discriminant function analysis adequately distinguishes the contribution rate of basin soil and has a good validation accuracy (R2 = 0.96, root mean square error of calibration = 5.91 %), whereas Random Forest obtains better discrimination results by mining non-linear information in the soil spectra of different land types, with R2 values of 0.89, 0.83, and 0.80 for farmland, forest, and grassland, respectively. The average proportion of soil in the sediment in the watershed was 23 %, and the proportion of soil in the watershed increased from upstream to downstream. The sediment sources of the Caoqiao, Yincun, and Shaoxiang Rivers mainly came from grassland (44 %), forest (39 %), and farmland (42 %), respectively. Land-use distribution, water conservation facilities, and soil particle size were the main factors affecting these sources. Each river adopts measures to remove the corresponding pollutants, optimise water and soil conservation measures for riverbank green belts and forest, and regularly clean up silt in water conservancy ditches and rivers, which can reduce the pollution impact caused by sediment.

Dataset on the crystallographic, vibrational, and electronic properties of 1M-phlogopite K(Mg,Fe)3(Si3Al)O10(OH)2 obtained from Density Functional Theory investigations

The present work reports a dataset on the crystal structure, optical properties (complex dielectric function and refractive index), infrared, reflectance and Raman spectra, and electronic properties (band structure and density of states) of the 1M-polytype of phlogopite [1]. This phyllosilicate presents chemical formula K(Mg,Fe)3(Si3Al)O10(OH)2, with Mg/Fe ratio ≥ 2. The dataset was obtained from Density Functional Theory (DFT) simulations at B3LYP-D* level, i.e., with the hybrid functional B3LYP corrected with an ad hoc DFT-D2 scheme, and all-electron Gaussian-type orbitals basis sets for all atoms in the unit cell. Furthermore, experimental confocal Raman micro-spectrometry data (spectra) collected on a single crystal phlogopite specimen are reported. The quality of the dataset was assessed by comparing the results with available X-ray diffraction and IR/Raman spectroscopy data reported in literature. The reported complete dataset is a reference for future studies in fundamental georesource exploration and exploitation, applied mineralogy, geology, and material science.

Trace metal content prediction along an AMD (acid mine drainage)-contaminated stream draining a coal mine using VNIR-SWIR spectroscopy

The current study investigated the use of VNIR-SWIR (visible/near infrared to short-wavelength infrared: 400-2500 nm) spectroscopy for predicting trace metals in overbank sediments collected in the study site. Here, we (i) derived spectral absorption feature parameters (SAFPs) from measured ground spectra for correlation with trace metal (Pb, Cd, As, and Cu) contents in overbank sediments, (ii) built univariate regression models to predict trace metal concentrations using the SAFPs, and (iii) evaluated the predictive capacities of the regression models. The derived SAFPs associated with goethite in overbank sediments were Depth433b, Asym433b, and Width433b, and those associated with kaolinite in overbank sediments were Depth1366b, Asym1366b, Width1366b, Depth2208b, Asym2208b, and Width2208b. Cadmium in the overbank sediments showed the strongest correlations with the goethite-related SAFPs, whereas Pb, As, and Cu showed strong correlations with goethite- and kaolinite-related SAFPs. The best predictive models were obtained for Cu (R2 = 0.73, SEE = 0.15) and Pb (R2 = 0.73, SEE = 0.21), while weaker models were obtained for As (R2 = 0.46, SEE = 0.31) and Cd (R2 = 0.17, SEE = 0.81). The results suggest that trace metals can be predicted indirectly using the SAFPs associated with goethite and kaolinite. This is an important benefit of VNIR-SWIR spectroscopy considering the difficulty in analyzing "trace" metal concentrations, on large scales, using conventional geochemical methods.

Reflectance spectroscopy as a promising tool for 'sensing' metals in hyperaccumulator plants

MAIN CONCLUSION

The VNIR reflectance spectra of nickel hyperaccumulator plant leaves have spectral variations due to high nickel concentrations and this property could potentially be used for discovery of these plants.  Hyperaccumulator plants accumulate high concentrations of certain metals, including manganese, cobalt, or nickel. Of these metals, the divalent ions of nickel have three absorption bands in the visible to near-infrared region which may cause variations in the spectral reflectance of nickel hyperaccumulator plant leaves, but this has not been investigated previously. In this shortproof-of-concept study, the spectral reflectance of eight different nickel hyperaccumulator plant species leaves were subjected to visible and near-infrared and shortwave infrared (VNIR-SWIR) reflectance spectrum measurements in dehydrated state, and for one species, it was also assessed in hydrated state. Nickel concentrations in the plant leaves were determined with other methods and then correlated to the spectral reflectance data. Spectral variations centred at 1000 ± 150 nm were observed and had R-values varying from 0.46 to 0.96 with nickel concentrations. The extremely high nickel concentrations in nickel hyperaccumulator leaves reshape their spectral reflectance features, and the electronic transition of nickel-ions directly contributes to absorption at ~ 1000 nm. Given that spectral variations are correlated with nickel concentrations it make VNIR-SWIR reflectance spectrometry a potential promising technique for discovery of hyperaccumulator plants, not only in the laboratory or herbarium, but also in the field using drone-based platforms. This is a preliminary study which we hope will instigate further detailed research on this topic to validate the findings and to explore possible applications.

Detection of amylase activity and moisture content in rice by reflectance spectroscopy combined with spectral data transformation

In this study, reflectance spectroscopy was used to achieve rapid and non-destructive detection of amylase activity and moisture content in rice. Since rice husk can interfere with spectral measurements, spectral data transformation was used to remove the husk interference. Reflectance spectra of rice were transformed by direct standardization, convolutional autoencoder network, and kernel regression (KR). Then, random frog and elliptical envelope were adopted to select effective wavelengths, and partial least squares regression (PLSR) and support vector regression were used to establish analysis models. The optimal transformation was from KR, and PLSR and effective wavelengths of the transformed spectra obtained excellent performance with coefficient of determination of test of 0.6987 and 0.8317 and root-mean-square error of test of 0.3359 and 2.2239, respectively. The result was better than that of the rice spectra and was close to that of the husked rice spectra. When the moisture content was integrated into the regression model of amylase activity, a better result was obtained. Thus, the proposed method can detect amylase activity and moisture content in rice accurately.

Rapid proximate analysis of coal based on reflectance spectroscopy and deep learning

Proximate analysis of coal is of profound significance for understanding coal quality and promoting rational utilization of coal resources. Traditional coal proximate analysis mainly uses chemical analysis methods, which have the disadvantages of slow speed and high cost. This paper proposed an approach combining reflectance spectroscopy with deep learning (DL) for rapid proximate analysis of coal. First, 80 sets of coal spectral data are enhanced by data augmentation, outlier detection, and dimensional transformation to improve the number and quality of samples. Then, an analytical model combining dilated convolution, multi-level residual connection, and a two-hidden-layer extreme learning machine (TELM), named DR_TELM, was proposed. The model extracted effective features from coal spectral data by a convolutional neural network (CNN) and utilized TELM as a regressor to achieve feature identification and content prediction. The experimental results showed that DR_TELM achieved coefficients of determination (R²) of 0.981, 0.989, 0.990, 0.985, 0.989 and root mean square errors (RMSE) of 0.533, 1.833, 1.111, 1.808, 0.723 for the content prediction of moisture, ash, volatile matter, fixed carbon and higher heating value (HHV), respectively. And while ensuring high accuracy, the test time is only 0.034 s. It is fully demonstrated that DR_TELM can rapidly and accurately analyze coal.

Optimization of diffuse reflectance spectroscopy measurements for direct and rapid screening of pesticides: A case study of spinach

Although diffuse reflectance spectroscopy (DRS) measurements can be collected rapidly and simultaneously, the resulting datasets are imbalanced and redundant due to the highly correlated spectral features collected on relatively few samples. Consequently, modelling these datasets using machine learning (ML) techniques is challenging and necessitates longer training times and more computational resources. Furthermore, models developed with such data are frequently prone to overfitting, resulting in promising but often non-reproducible results. We demonstrate the advantage of using an eigenvector decomposition principal component analysis (PCA) in reducing the dimensionality and data mining of DRS measurements in the short near-infrared region (750-900 nm). A total of 547 DRS measurements consisting of 151 wavelengths were acquired from spinach samples sprayed with two different pesticides and control samples. The measurements were later preprocessed with a Savitzky-Golay filter and multiplicative scatter analysis. After performing PCA on the preprocessed data, two principal components (PCs) that explained 77% of the cumulative variance and maximized the interclass variation were extracted and used as inputs to three ML models namely; artificial neural networks, support vector machine and random forest, to classify the samples. Re-sampling was used to tune the models and avoid overfitting. The performance of the models was compared using raw DRS data, pre-processed (PP) DRS data, and PCs data. The results show that pesticide classification using PCs data requires the least amount of training time (average 2.4 s) for all the models, and achieves 100% classification accuracy. In addition, it was observed that spectral data pre-processing improves accuracy and training time when compared to using raw spectral data. These findings are particularly encouraging since they demonstrate the possibility of developing rapid and accurate classification models for screening pesticide residues in fresh produce based on DRS measurements with minimal computational resources.

Coal identification based on a deep network and reflectance spectroscopy

The rapid identification of coal types in the field is an important task. This research combines spectroscopy with deep learning algorithms and proposes a method for quickly identifying coal types in the field. First, we collect field spectral data of various coals and preprocess the spectra. Then, a coal identification model that uses a convolutional neural network in combination with an extreme learning machine is proposed. The two-dimensional spectral features of coal are extracted through the convolutional neural network, and the extreme learning machine is used as a classifier to identify the features. To further improve the identification performance of the model, we use the whale optimization algorithm to optimize the parameters of the model. The experimental results show that the proposed method can quickly and accurately identify types of coal. It provides a low-cost, convenient, and effective method for the rapid identification of coal in the field.

Reflectance spectroscopy with operator difference for determination of behenic acid in edible vegetable oils by using convolutional neural network and polynomial correction

A novel polynomial correction method, order-adaptive polynomial correction (OAPC), was proposed to correct reflectance spectra with operator differences, and convolutional neural network (CNN) was used to develop analysis model to predict behenic acid in edible oils. With application of OAPC, CNN performed well with coefficient of determination of correction (R²_cor) of 0.8843 and root mean square error of correction (RMSE_cor) of 0.1182, outperforming partial least squares regression, support vector regression and random forest with OAPC, as well as the cases without OAPC. Based on 16 effective wavelengths selected by combination of bootstrapping soft shrinkage, random frog and Pearson's correlation, CNN and OAPC exhibited excellent performance with R²_cor of 0.9560 and RMSE_cor of 0.0730. Meanwhile, only 5% correction samples were selected by Kennard-Stone for OAPC. Overall, the proposed method could alleviate the impact of operator differences on spectral analysis, thereby providing potential to correct differences from measurement instruments or environments.

Evaluation of oral potentially malignant disorders with autoflorescence, reflectance spectroscopy and vital staining and their correlation with histopathology - Hospital based prospective study

OBJECTIVES

To evaluate diagnostic efficacy of Identafi® and toluidine blue in screening of Oral Potentially Malignant Disorders (OPMDs).

MATERIALS AND METHODS

Forty nine patients (63 lesions) with OPMDs visiting Department of Oral Medicine and Radiology, B.P.Koirala Institute of Health Science (BPKIHS) were included in the study. After thorough history and oral examination, screening was done by Identafi® followed by toluidine blue staining. Then incisional biopsy under local anesthesia was performed from the site showing positive change followed by histopathological examination.

RESULTS

The overall sensitivity and specificity of Identafi®^'s violet light was 73% and 46.2%. The overall sensitivity and specificity of Identafi®^'s green-amber light were 78.4% and 15.4% respectively. The overall sensitivity and specificity of toluidine blue were 51.4 and 84.6 respectively. A statistically-significant association was observed between the toluidine blue and histopathology results, (p = 0.04), p value set at ≤ 0.05.

CONCLUSION

Identafi®'s violet light and green amber light are more sensitive in detecting true positives. Toluidine blue is highly specific in ruling out true negative cases. The screening of OPMDs will help in selecting proper biopsy site followed by early diagnosis and intervention, hence increasing the prognosis, outcome and decrease complications associated with it.

Technical non-invasive study of the novo-hispanic painting the Pentecost by Baltasar de Echave Orio by spectroscopic techniques and hyperspectral imaging: In quest for the painter's hand

This paper presents a combination of noninvasive techniques for the study of superimposed painting stages in The Pentecost, a Mexican Colonial panel painting attributed to Baltasar de Echave Orio (1558 - 1619). The application of reflected hyperspectral imaging (HSI) analysis for mapping the distribution of the pigments in the paint surface and the use of ultraviolet (UV) fluorescence photography and X-ray radiography as complementary imaging techniques provide new insights into the making process of the artwork, its manufacturing and conservation state. For a better understanding of the in situ results gathered, we studied a series of paint mock-up samples created following recipes and studio practices from art treatises. The use of spot analytical methods such as fiber optic reflectance spectroscopy (FORS) and X-ray fluorescence spectroscopy (XRF) allowed for a robust identification of the artist's materials.

VIRS based detection in combination with machine learning for mapping soil pollution

Widespread soil contamination threatens living standards and weakens global efforts towards the Sustainable Development Goals (SDGs). Detailed soil mapping is needed to guide effective countermeasures and sustainable remediation operations. Here, we review visible and infrared reflectance spectroscopy (VIRS) based detection methods in combination with machine learning. To date, proximal, airborne and spaceborne carrier devices have been employed for soil contamination detection, allowing large areas to be covered at low cost and with minimal secondary environmental impact. In this way, soil contaminants can be monitored remotely, either directly or through correlation with soil components (e.g. Fe-oxides, soil organic matter, clay minerals). Observed vegetation reflectance spectra has also been proven an effective indicator for mapping soil pollution. Calibration models based on machine learning are used to interpret spectral data and predict soil contamination levels. The algorithms used for this include partial least squares regression, neural networks, and random forest. The processes underlying each of these approaches are outlined in this review. Finally, current challenges and future research directions are explored and discussed.

Hyperspectral characteristics and quantitative analysis of leaf chlorophyll by reflectance spectroscopy based on a genetic algorithm in combination with partial least squares regression

The precise and nondestructive detection of leaf chlorophyll content is one key to assessing the health status of crops. The objective of this study was to develop a precision method for determining the leaf chlorophyll content in rape. A genetic algorithm (GA) combined with the partial least squares (PLS) method was used to establish a chlorophyll content PLS regression estimation model based on screening the characteristic spectral regions of chlorophyll. The results show that the characteristic bands of chlorophyll in rape are 510-535, 675-695, 905-965, 1025-1225, 1165-1175, 1295-1385, 1495-1765, 1875-1895, 1970-2145, and 2179-2185 nm. Based on the characteristics of each input spectrum, the Rv² and RPD values of the best model reached 0.97 and 5.41, respectively. This represented an increase of 0.20 and 3.42, respectively, over these values for the original full-spectrum model. The best model also achieved an RMSEP of 2.63 mg g^-1, which was only 3.59% of the total sample average and was 3.78 mg g^-1 less than that of the original full-spectrum model. Therefore, the best model provided good prediction accuracy for the chlorophyll content of rape. The model based on the Log (1/R) spectral transformation performed best in terms of prediction accuracy. The genetic algorithm combined with the partial least squares method (GA-PLS) can effectively screen the characteristic bands of rape chlorophyll, reduce the number of variables in the model, and produce high estimation accuracy.

Modelling potentially toxic elements in forest soils with vis-NIR spectra and learning algorithms

The surface organic horizons in forest soils have been affected by air and soil pollutants, including potentially toxic elements (PTEs). Monitoring of PTEs requires a large number of samples and adequate analysis. Visible-near infrared (vis-NIR: 350-2500 nm) spectroscopy provides an alternative method to conventional laboratory measurements, which are time-consuming and expensive. However, vis-NIR spectroscopy relies on an empirical calibration of the target attribute to the spectra. This study examined the capability of vis-NIR spectra coupled with machine learning (ML) techniques (partial least squares regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and a deep learning (DL) approach called fully connected neural network (FNN) to assess selected PTEs (Cr, Cu, Pb, Zn, and Al) in forest organic horizons. The dataset consists of 2160 samples from 1080 sites in the forests over all the Czech Republic. At each site, we collected two samples from the fragmented (F) and humus (H) organic layers. The content of all PTEs was higher in horizon H compared to F horizon. Our results indicate that the reflectance of samples tended to decrease with increased PTEs concentration. Cr was the most accurately predicted element, regardless of the algorithm used. SVMR provided the best results for assessing the H horizon (R² = 0.88 and RMSE = 3.01 mg/kg for Cr). FNN produced the best predictions of Cr in the combined F + H layers (R² = 0.89 and RMSE = 2.95 mg/kg) possibly due to the larger number of samples. In the F horizon, the PTEs were not predicted adequately. The study shows that PTEs in forest soils of the Czech Republic can be accurately estimated with vis-NIR spectra and ML approaches. Results hint in availability of a large sample size, FNN provides better results.

Monitoring oil contamination in vegetated areas with optical remote sensing: A comprehensive review

The monitoring of soil contamination deriving from oil and gas industry remains difficult in vegetated areas. Over the last decade, optical remote sensing has proved helpful for this purpose. By tracking alterations in vegetation biochemistry through its optical properties, multi- and hyperspectral remote sensing allow detecting and quantifying crude oil and petroleum products leaked following accidental leakages or bad cessation practices. Recent advances in this field have led to the development of various methods that can be applied either in the field using portable spectroradiometers or at large scale on airborne and satellite images. Experiments carried out under controlled conditions have largely contributed to identifying the most important factors influencing the detection of oil (plant species, mixture composition, etc.). In a perspective of operational use, an important effort is still required to make optical remote sensing a reliable tool for oil and gas companies. The current methods used on imagery should extend their scope to a wide range of contexts and their application to upcoming satellite-embedded hyperspectral sensors should be considered in future studies.

Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning

Understanding plant disease resistance is important in the integrated management of Phytophthora infestans, causal agent of potato late blight. Advanced field-based methods of disease detection that can identify infection before the onset of visual symptoms would improve management by greatly reducing disease potential and spread as well as improve both the financial and environmental sustainability of potato farms. In-vivo foliar spectroscopy offers the capacity to rapidly and non-destructively characterize plant physiological status, which can be used to detect the effects of necrotizing pathogens on plant condition prior to the appearance of visual symptoms. Here, we tested differences in spectral response of four potato cultivars, including two cultivars with a shared genotypic background except for a single copy of a resistance gene, to inoculation with Phytophthora infestans clonal lineage US-23 using three statistical approaches: random forest discrimination (RF), partial least squares discrimination analysis (PLS-DA), and normalized difference spectral index (NDSI). We find that cultivar, or plant genotype, has a significant impact on spectral reflectance of plants undergoing P. infestans infection. The spectral response of four potato cultivars to infection by Phytophthora infestans clonal lineage US-23 was highly variable, yet with important shared characteristics that facilitated discrimination. Early disease physiology was found to be variable across cultivars as well using non-destructively derived PLS-regression trait models. This work lays the foundation to better understand host-pathogen interactions across a variety of genotypic backgrounds, and establishes that host genotype has a significant impact on spectral reflectance, and hence on biochemical and physiological traits, of plants undergoing pathogen infection.

Oxidations in white grape (Vitis vinifera L.) skins: Comparison between ripening process and photooxidative sunburn symptoms

Oxidations in grape berries are gaining major interest as they affect grape characteristics and quality. Considering berries, Reactive Oxygen Species are involved in the responses to both ripening process and stresses, including photooxidative sunburn. Redox metabolism involves a multitude of chemical and enzymatic reactions. In this study, four white grape cultivars were examined for natural ripening and photooxidative sunburn effects (obtained in artificial conditions) on berry pigmentation, chemical composition and enzymatic activity. The measured parameters included reflectance spectra, pigmentation (including berry browning), content of photosynthetic pigments, organic acid profiles, antioxidant activity, concentrations of antioxidants (total phenolics, ascorbic acid and reduced glutathione), enzymatic activities (guaiacol peroxidases, ascorbate peroxidase and catalase). The effects of the treatment (natural ripening and artificial photooxidative sunburn) on each considered parameter are described in the paper. Photooxidative sunburn strongly affected the contents of antioxidants and chlorophylls, increased the browning index and modulated the enzymatic activities investigated. Samples clearly clustered depending on the oxidation status. Furthermore, the PCA highlighted the similarities and differences in the responses to oxidative stress during ripening and photooxidative sunburn. PCA produced five functions with eigenvalues higher than 1, representing 87.03% of the total variability. In particular, the scores of the function 1 discriminated the samples based on the oxidation status, while the function 2 separated the samples based on the sampling date, representing the physiological responses characteristic of ripening. Our work sheds light on this topic, and will allow a more conscious vineyard management, thus supporting the agricultural adaptation to climate changes.

Linking physiological parameters with visible/near-infrared leaf reflectance in the incubation period of vascular wilt disease

The photosynthetic pigments are mainly responsible for absorbing the light intended to promote photosynthesis on the chloroplast of the leaves. Different studies have related the spectral response in the leaves of plants with the biotic stress generated by pathogens. In general, maximum differences in reflectance have been found in the range of 380-750 nm between plants subjected to biotic stress and healthy plants. In this study, it was possible to characterize and relate the spectral variance in leaves of S. lycopersicum infected with F. oxysporum with this physiological variation and pathogen concentration in tomato plants during the asymptomatic period of vascular wilt. Photosynthetic parameters derived from gaseous exchange analysis in the tomato leaves correlated related with four bands in the visible range (Vis). Additionally, five specific bands also present a high correlation with the increase in the concentration of F. oxysporum conidia measured at the root: 448-523 nm, 624-696 nm, 740-960 nm, 973-976 nm, and 992-995 nm. These wavelengths allowed a 100% correct classification of the plants inoculated with F. oxysporum from the plants subjected to hydric stress and the control plants in the asymptomatic period of the disease. The spectral response to biotic and abiotic stress in the measured Vis/NIR range can be explained by the general tendency to change the concentration of chlorophyll and carotene in tomato leaves. These studies also highlight the importance of the implementation of robust multivariate analysis over the multiple univariate analysis used in the applied biological sciences and specifically in the agricultural sciences. These results demonstrate that specific wavelength responses are due to physiological changes in plants subjected to stress, and can be used in indexes and algorithms applied to the early detection of diseases in plants on different pathosystems.

Estimating persistent oil contamination in tropical region using vegetation indices and random forest regression

The persistence of soil contamination after cessation of oil activities remains a major environmental issue in tropical regions. The assessment of the contamination is particularly difficult on vegetated sites, but promising advances in reflectance spectroscopy have recently emerged for this purpose. This study aimed to exploit vegetation reflectance for estimating low concentrations of Total Petroleum Hydrocarbons (TPH) in soils. A greenhouse experiment was carried out for 42 days on Cenchrus alopecuroides (L.) under realistic tropical conditions. The species was grown on oil-contaminated mud pit soils from industrial sites, with various concentrations of TPH. After 42 days, a significant decrease in plant growth and leaf chlorophyll and carotenoid contents was observed for plants exposed to 5-19 g kg^-1 TPH in comparison to the controls (p < 0.05). Conversely, pigment contents were higher for plants exposed to 1 g kg^-1 TPH (hormesis phenomenon). These modifications proportionally affected the reflectance of C. alopecuroides at leaf and plant scales, especially in the visible region around 550 and 700 nm. 33 vegetation indices were used for linking the biochemical and spectral responses of the species to oil using elastic net regressions. The established models indicated that chlorophylls a and b and β-carotene were the main pigments involved in the modifications of reflectance (R² > 0.7). The same indices also succeeded in estimating the concentrations of TPH using random forest regression, at leaf and plant scales (RMSE = 1.46 and 1.63 g kg^-1 and RPD = 5.09 and 4.44, respectively). Four out of the 33 indices contributed the most to the models (>75%). This study opens up encouraging perspectives for monitoring the cessation of oil activities in tropical regions. Further researches will focus on the application of our approach at larger scale, on airborne and satellite imagery.

Titanium dioxide nanoparticles-based colorimetric sensors for determination of hydrogen peroxide and triacetone triperoxide (TATP)

Due to its relatively simple preparation and readily available precursors, determination of triacetone triperoxide (TATP) by portable devices has become important. In this work, two different titanium dioxide nanoparticles (TiO₂NPs)-based colorimetric sensors based on complex formation on the solid surface were developed for determination of H₂O₂ and TATP. The first sensor, (3-aminopropyl)triethoxysilane (APTES) modified-TiO₂NPs-based paper sensor (APTES@TiO₂NPs), exploits peroxo-titanate binary complex formation between APTES@TiO₂NPs and H₂O₂ on chromatographic paper. The second sensor, 4-(2-pyridylazo)-resorcinol-modified-TiO₂NPs-based solid sensor (PAR@TiO₂NPs), relies on the formation of a ternary complex between Ti(IV), PAR and H₂O₂. The developed sensors were also applied to TATP determination after acidic hydrolysis of samples to H₂O₂. The limits of detection (LODs) of APTES@TiO₂NPs-based paper sensor were 3.14 × 10^-4 and 5.13 × 10^-4 mol L^-1 for H₂O₂ and TATP, respectively, whereas the LODs of PAR@TiO₂NPs solid sensor were 6.06 × 10^-7 and 3.54 × 10^-7 mol L^-1 for H₂O₂ and TATP, respectively. Possible interferences of common soil ions, passenger belongings used as camouflage materials during public transport (e.g., detergent, sweetener, acetylsalicylic acid and paracetamol-caffeine based analgesic drugs) and of other explosives were examined. The developed methods were statistically validated using t- and F- tests against the titanyl sulfate (TiOSO₄) colorimetric literature method.

Accumulation of volatile phenol glycoconjugates in grapes following grapevine exposure to smoke and potential mitigation of smoke taint by foliar application of kaolin

The accumulation of volatile phenol glycoconjugates in smoke-exposed grapes was monitored following grapevine exposure to smoke, with different glycoconjugate profiles observed for fruit sampled 1 and 7 days after smoke exposure, and at maturity. Foliar application of kaolin reduced the concentration of volatile phenol glycoconjugates in smoke-exposed fruit, but efficacy depended on the rate of application and extent of coverage. Smoke taint can be found in wines made from grapes exposed to smoke from bushfires or prescribed burns. It is characterized by objectionable smoky and ashy aromas and flavors, which have been attributed to the presence of smoke-derived volatile phenols, in free and glycoconjugate forms. This study investigated: (1) the accumulation of volatile phenol glycoconjugates in grapes following the application of smoke to Sauvignon Blanc, Chardonnay and Merlot grapevines at approximately 10 days post-veraison; and (2) the potential mitigation of smoke taint as a consequence of foliar applications of kaolin (a clay-based protective film) prior to grapevine smoke exposure. Varietal differences were observed in the glycoconjugate profiles of smoke-exposed grapes; the highest glycoconjugate levels were found in Merlot grapes, being pentose-glucosides of guaiacol, cresols, and phenol, and gentiobiosides of guaiacol and syringol. Changes in volatile phenol glycoconjugate profiles were also observed with time, i.e., between fruit sampled 1 day after smoke exposure and at maturity. The application of kaolin did not significantly affect the glycoconjugate profiles of Sauvignon Blanc and Chardonnay grapes, but significantly lower volatile phenol glycoconjugate levels were observed in Merlot fruit that was treated with kaolin prior to smoke exposure. The potential for control and smoke-exposed grapes to be differentiated by measurement of spectral reflectance was also demonstrated.

A Catalog of Spectra, Albedos, and Colors of Solar System Bodies for Exoplanet Comparison

We present a catalog of spectra and geometric albedos, representative of the different types of solar system bodies, from 0.45 to 2.5 μm. We analyzed published calibrated, uncalibrated spectra, and albedos for solar system objects and derived a set of reference spectra and reference albedos for 19 objects that are representative of the diversity of bodies in our solar system. We also identified previously published data that appear contaminated. Our catalog provides a baseline for comparison of exoplanet observations to 19 bodies in our own solar system, which can assist in the prioritization of exoplanets for time intensive follow-up with next-generation extremely large telescopes and space-based direct observation missions. Using high- and low-resolution spectra of these solar system objects, we also derive colors for these bodies and explore how a color-color diagram could be used to initially distinguish between rocky, icy, and gaseous exoplanets. We explore how the colors of solar system analog bodies would change when orbiting different host stars. This catalog of solar system reference spectra and albedos is available for download through the Carl Sagan Institute.

Non-invasive study of natural dyes on historical textiles from the collection of Michelangelo Guggenheim

A selection of historical textile fragments from the Venetian art dealer Moisè Michelangelo Guggenheim collection, ranging from XV to XVIII century, has been investigated by means of non-invasive techniques in order to reveal the coloring materials. Imaging was preliminarily used to visually investigate the selected artwork fragments in order to investigate their structure and conservation conditions; Fiber Optics Reflectance Spectroscopy (FORS) allowed recognizing the main natural dyestuffs, such as indigotin and anthraquinones-based compounds, except the yellow ones, difficultly identifiable when using this non-invasive technique. Collected spectroscopic data have been also elaborated by using a clustering algorithm that permitted to group collected spectra on the basis of similar properties and evidencing their inflection point wavelength as the most influencing feature.

Multi-parameter characterization of water stress tolerance in Vitis hybrids for new rootstock selection

Drought in grapevine could be faced using tolerant rootstocks. The present work aims at the evaluation of 25 new genotypes potentially tolerant to drought by using recent methods of phenotypical screening (thermography and on-solid reaction spectroscopy). Plants were grown in well-watered and stressed field conditions. Proxi for transpiration, wood hydrophobicity and starch content were used to characterize and classify the genotypes. The predominant role of the environment was highlighted, nevertheless genotype and genotype × environment interaction showed significant variations as well. Hybrids were classified based on their steady, susceptible or adaptable behavior. The 14 most promising genotypes were identified, 5 of them showing two tolerance mechanisms. In the future, results from this experiment will support viticulture in water limited areas releasing new drought-tolerant interspecific hybrids to be tested after grafting with different scions.

The Vegetation Red Edge Biosignature Through Time on Earth and Exoplanets

The high reflection of land vegetation in the near-infrared, the vegetation red edge (VRE), is often cited as a spectral biosignature for surface vegetation on exoplanets. The VRE involves only a few percentage change in reflectivity for a disk-integrated observation of present-day Earth. Here we show that the strength of Earth's VRE has increased over the past ∼500 million years of land plant evolution and may continue to increase as solar luminosity increases and the planet warms, until either vegetation coverage is reduced, or the planet's atmosphere becomes opaque to light reflected off the surface. Early plants such as mosses and liverworts, which dominated the land 500-400 million years ago, produce a weaker VRE, approximately half as strong as that of modern vegetation. We explore how the changes in land plants, as well as geological changes such as ice coverage during ice ages and interglacial periods, influence the detectability of the VRE through Earth's geological past. Our results show that the VRE has varied through the evolutionary history of land plants on Earth and could continue to change into the future if hotter climate conditions became dominant, encouraging the spread of vegetation. Our findings suggest that older and hotter Earth-like planets are good targets for the search for a VRE signature. In addition, hot exoplanets and dry exoplanets with some water could be the best targets for a successful vegetation biosignature detection. As well as a strong red edge, lower cloud fractions and low levels of atmospheric water vapor on such planets could make it easier to detect surface features in general.

Estimating soil heavy metals concentration at large scale using visible and near-infrared reflectance spectroscopy

This study was aimed (i) to examine using diffuse reflectance spectra within VNIR region to estimate soil heavy metals concentrations at large scale, (ii) to compare the influence of different pre-processing models on predictive model accuracy, and (iii) to explore the best predictive models. A number of 325 topsoil samples were collected and their spectral data, pH, clay content, organic matter, Ni, and Cu concentrations were determined. To improve spectral data, various pre-processing methods including Savitzky-Golay smoothing filter, Savitzky-Golay smoothing filter with first and second derivatives, and standard normal variant (SNV) were used. Partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) models were employed to build calibration models for estimating soil heavy metals concentration followed by evaluation of provided predictive models. Results indicated that Cu had stronger correlation coefficients with spectral bands compared to Ni. Cu and Ni demonstrated strongest correlations at wavelengths 1925 and 1393 nm, respectively. Based on RMSE, R², and RPD statistics, the PLSR model with Savitzky-Golay filter pretreatment provided the most accurate predictions for both Cu and Ni (R² = 0.905, RMSE = 0.00123, RPD = 2.80 for Ni; R² = 0.825, RMSE = 0.00467, RPD = 2.04 for Cu) where such prediction was much better for Ni than for Cu. Reasonable results with lower accuracy and stability were obtained for PCR (R² = 0.742, RMSE = 0.00181, RPD = 1.91 for Ni; R² = 0.731, RMSE = 0.00578, RPD = 1.65 for Cu) and SVMR (R² = 0.643, RMSE = 0.00091, RPD = 3.80 for Ni; R² = 0.505, RMSE = 0.00296, RPD = 3.22 for Cu). We concluded that reflectance spectroscopy technique could be applied as a reliable tool for detection and prediction of soil heavy metals.

Hapke-based computational method to enable unmixing of hyperspectral data of common salts

Environmental scientists are currently assessing the ability of hyper-spectral remote sensing to detect, identify, and analyze natural components, including minerals, rocks, vegetation and soil. This paper discusses the use of a nonlinear reflectance model to distinguish multicomponent particulate mixtures. Analysis of the data presented in this paper shows that, although the identity of the components can often be found from diagnostic wavelengths of absorption bands, the quantitative abundance determination requires knowledge of the complex refractive indices and average particle scattering albedo, phase function and size. The present study developed a method for spectrally unmixing halite and gypsum combinations. Using the known refractive indexes of the components, and with the assistance of Hapke theory and Legendre polynomials, the authors develop a method to find the component particle sizes and mixing coefficients for blends of halite and gypsum. Material factors in the method include phase function parameters, bidirectional reflectance, imaginary index, grain sizes, and iterative polynomial fitting. The obtained Hapke parameters from the best-fit approach were comparable to those reported in the literature. After the optical constants (n, the so-called real index of refraction and k, the coefficient of the imaginary index of refraction) are derived, and the geometric parameters are determined, single-scattering albedo (or ω) can be calculated and spectral unmixing becomes possible.

Characterization of outdoor bronze monument patinas: the potentialities of near-infrared spectroscopic analysis

The corrosion products usually found on outdoor bronzes are generated by the interaction between the metal alloy and the atmospheric pollutants. To protect the external surface of bronzes, different organic materials (natural or synthetic) can be applied, creating over time a patina consisting of a complex mixture of inorganic and organic degraded components. The correct chemical characterization of patina constituents is fundamental to define the state of conservation of a metal artwork and address proper restoration actions. In this paper, we evaluated the potentialities of near-infrared (NIR) reflectance microscopy (4000-7500 cm^-1) as complementary method to mid-infrared (MIR) analyses for the characterization of bronze patinas. Although NIR spectroscopy has been already used in the field of heritage science, its application for the characterization of bronze patinas is almost unexplored. In this paper, several corrosion products usually found on the surface of outdoor bronze sculptures were synthesized, characterized, and submitted to the NIR-MIR total reflection analysis to build up a reference spectral database. We devoted particular attention to the NIR features of copper hydroxychlorides, such as atacamite and paratacamite, which have not been studied in detail up to now. A selection of organic-based formulations, commonly used by restorers to protect the bronze surface against the outdoor aggressive environment, were also considered as references. Successively, NIR-MIR reflectance microscopy was successfully employed for the analysis of patina micro-samples collected from the bronze statues of the Neptune Fountain (sixteenth century) located in Bologna. The obtained results demonstrate the ability of NIR spectroscopy to identify organic and inorganic patina constituents, even in mixtures. In addition, the study can be considered as a proof of concept for the possible future application of the technique for in situ diagnostic campaigns on bronze sculptures.

Multi-technique characterisation of commercial alizarin-based lakes

The characterization of ancient and modern alizarin-based lakes is a largely studied topic in the literature. Analytical data on contemporary alizarin-based lakes, however, are still poor, though of primary importance, since these lakes might be indeed present in contemporary and fake paintings as well as in retouchings. In this work we systematically investigate the chemical composition and the optical features of fifteen alizarin-based lakes, by a multi-analytical technique approach combining spectroscopic methods (i.e. Energy Dispersive X-ray Fluorescence Spectroscopy, EDXRF; Attenuated Total Reflectance Fourier-Transform Infrared Spectroscopy, ATR-FTIR; X-ray Powder Diffraction, XRD; UV induced fluorescence and reflectance spectroscopies) and chromatography (i.e. High-performance Liquid Chromatography coupled with a Photodiode Array Detector, HPLC-PDA). Most of the samples contain typical compounds from the natural roots of madder, as occurring in ancient and modern lakes, but in two samples (23600-Kremer-Pigmente and alizarin crimson-Zecchi) any anthraquinonic structures were identified, thus leading to hypothesize the presence of synthetic dyes. The detection of lucidin primeveroside and ruberythrique acid in some lakes suggest the use of Rubia tinctorum. One sample (23610-Kremer-Pigmente) presents alizarin as the sole compound, thereby revealing to be a synthetic dye. Moreover, gibbsite, alunite and kaolinite were found to be used as substrates and/or mordants. Visible absorption spectra of the anthraquinonic lakes show two main absorption bands at about 494-511nm and 537-564nm, along with a shoulder at about 473-479nm in presence of high amounts of purpurin. Finally, from the results obtained by UV induced fluorescence spectroscopy it is possible to figure out that, although it is commonly assumed that the madder lake presents an orange-pink fluorescence, the inorganic compounds, added to the recipe, could induce a quenching phenomenon or an inhibition of the fluorescence, as occurring in some commercial alizarin-based lakes.

Reflectance spectroscopy: a novel approach to better understand and monitor the impact of air pollution on Mediterranean plants

The Mediterranean basin can be considered a hot spot not only in terms of climate change (CC) but also for air quality. Assessing the impact of CC and air pollution on ecosystem functions is a challenging task, and adequate monitoring techniques are needed. This paper summarizes the present knowledge on the use of reflectance spectroscopy for the evaluation of the effects of air pollution on plants. First, the history of this technique is outlined. Next, we describe the vegetation reflectance spectrum, how it can be scaled from leaf to landscape levels, what information it contains, and how it can be exploited to understand plant and ecosystem functions. Finally, we review the literature concerning this topic, with special attention to Mediterranean air pollutants, showing the increasing interest in this technique. The ability of spectroscopy to detect the influence of air pollution on plant function of all major and minor Mediterranean pollutants has been evaluated, and ozone and its interaction with other gases (carbon dioxide, nitrogen oxides, and sulfur dioxide) have been the most studied. In the recent years, novel air pollutants, such as particulate matter, nitrogen deposition, and heavy metals, have drawn attention. Although various vegetation types have been studied, few of these species are representative of the Mediterranean environment. Thus, major emphasis should be placed on using vegetation spectroscopy for better understanding and monitoring the impact of air pollution on Mediterranean plants in the CC era.

Assessment of hyperspectral imaging system for colour measurement

A novel technique of reflective spectroscopy, hyperspectral imaging (HSI), was used to develop a methodology for colour measurement, which is very important in the field of art conservation - especially in the analysis of documents. The major focus of this work is an examination of the repeatability and reproducibility of colour parameters calculated from the hyperspectral data. The study was performed with commercial colour standards and pen ink lines, corresponding to large and small-scale colour areas, respectively. In some cases, the error of parameter determination indicated significant differences between the examined sample sets, which may be attributed to the low population of pixels from which the colour data were obtained. Our study allowed the development of an optimal hyperspectral image acquisition method for applications requiring accurate determination of the object's spectral characteristics. Besides determining the proper measurement procedure and the colour accuracy of the HSI, our study is also used to test this method for colour change monitoring of a real-life sample - a document treated with low-temperature plasma as a cleaning agent. The results proved that, by using hyperspectral imaging, colour change can be precisely determined and monitored within a selected area on the object. The application of HSI presented in our study was found to be an important alternative to conventional colorimeters.

Genesis and properties of wetland soils by VIS-NIR-SWIR as a technique for environmental monitoring

Wetlands are important ecosystems characterized by redoximorphic environments producing typical soil forming processes and organic carbon accumulation. Assessments and management of these areas are dependent on knowledge about soil characteristics and variability. By reflectance spectroscopy, information about soils can be obtained since their spectral behaviors are directly related to their chemical, physical, and mineralogical properties reflecting the pedogenetic processes and environment conditions. Our aims were: (a) to characterize the main soil classes of wetlands regarding their spectral behaviors in VIS-NIR-SWIR (350-2500 nm) and relate them to pedogenesis and environmental conditions, (b) to determine spectral ranges (bands) with greater expression of the main soil properties, (c) to identify spectral variations and similarities between hydromorphic soils from wetlands and other soils under different moisture conditions, and (d) to propose spectral models to quantify some chemical and physical soil properties used as environmental quality indicators. Nine soil profiles from the Pantanal region (Mato Grosso State, Brazil) and one from the Serra do Espinhaço Meridional (Minas Gerais State, Brazil) were investigated. Spectral morphology interpretation allowed identifying horizon differences regarding shape, absorption features and reflectance intensity. Some pedogenetic processes of wetland soils related to organic carbon accumulation and oxide iron variation were identified by spectra. Principal Component Analysis allowed discriminating soils from wetland and outside this area (oxidic environment). Quantification of organic carbon was possible with R² of 0.90 and low error. Quantification of clay content was masked by soils with organic carbon content over 2% where it was not possible to quantify with high R² and low error both properties when dataset has soil samples with high organic carbon content. By reflectance spectroscopy, important characteristics of wetland soils can be identified and used to distinguish from soils of different environments at low costs, reduced time, and with environmental quality.

Concentration estimation of heavy metal in soils from typical sewage irrigation area of Shandong Province, China using reflectance spectroscopy

Since sewage irrigation can markedly disturb the status of heavy metals in soils, a convenient and accurate technique for heavy metal concentration estimation is of utmost importance in the cropland using wastewater for irrigation. This study therefore assessed the feasibility of visible and near infrared reflectance (VINR) spectroscopy for predicting heavy metal contents including Cr, Cu, Ni, Pb, Zn, As, Cd, and Hg in the north plain of Longkou city, Shandong Province, China. A total of 70 topsoil samples were taken for in situ spectra measurement and chemical analysis. Stepwise multiple linear regression (SMLR) and principal component regression (PCR) algorithms were applied to establish the associations between heavy metals and reflectance spectral data pretreated by different transformation methods. Based on the criteria that minimal root mean square error (RMSE), maximal coefficient of determination (R ²) for calibration, and greater ratio of standard error of performance to standard deviation (RPD) is related to the optimal model, SMLR model using first deviation data (RD₁) provided the best prediction for the contents of Ni, Pb, As, Cd, and Hg, calibration using SNV data for Cr and continuum removal spectra for Zn, while PCR equation employed RD₁ values was fit for prediction of the contents of Cu. The determination coefficients of all the reasonable models were beyond 0.6, and RPD indicated a fair or good result. In general, first deviation preprocessing tool outperformed other methods in this study, while raw spectra reflectance performed unsatisfactory in all models. Overall, VINR reflectance spectroscopy technique could be applicable to the rapid concentration assessment of heavy metals in soils of the study area.

Dispersion analysis with inverse dielectric function modelling

We investigate how dispersion analysis can profit from the use of a Lorentz-type description of the inverse dielectric function. In particular at higher angles of incidence, reflectance spectra using p-polarized light are dominated by bands from modes that have their transition moments perpendicular to the surface. Accordingly, the spectra increasingly resemble inverse dielectric functions. A corresponding description can therefore eliminate the complex dependencies of the dispersion parameters, allow their determination and facilitate a more accurate description of the optical properties of single crystals.

Combined reflectance and Raman spectroscopy to assess degree of in vivo angiogenesis after tissue injury

BACKGROUND

Angiogenesis, the formation of blood vessels, is a critical aspect of wound healing. Disorders of wound healing are often characterized by lack of angiogenesis, a condition frequently observed in aging and diabetic patients. Current techniques for assessing blood at injury sites are limited to contrast-imaging, including angiography. However, these techniques do not directly observe oxygenation of blood and are not amenable to serial evaluation. A multimodal noninvasive reflectance and Raman spectrometer have been proposed to help clinicians as a point-of-care tool to interrogate local angiogenesis and tissue architecture, respectively. The spectrometer system is a rapid, noninvasive, and label-free technology well-suited for the clinical environment.

MATERIALS AND METHODS

To demonstrate feasibility, the spectrometer system was used to interrogate angiogenesis serially over 9 wk as a result of heterotopic ossification (HO) development in a validated murine model. End-stage HO was confirmed by micro-computed tomography.

RESULTS

Our preliminary results suggest that reflectance spectroscopy can be used to delineate vessel formation and that pathologic wounds may be characterized by unique spectra. In our model, HO formed at sites 1-3, whereas sites 4 and 5 did not have radiographic evidence of HO.

CONCLUSIONS

A point-of-care system like that demonstrated here shows potential as a noninvasive tool to assess local angiogenesis and tissue architecture that may allow for timely intervention in a clinical setting.

Pink berry grape (Vitis vinifera L.) characterization: Reflectance spectroscopy, HPLC and molecular markers

Color has a fundamental role for the qualitative evaluation and cultivar characterization of fruits. In grape, a normally functional pigment biosynthesis leads to the accumulation of a high quantity of anthocyanins. In this work, 28 Vitis vinifera L. cultivars accumulating low anthocyanins in berries were studied to characterize the biosynthetic dysfunctions in both a phenotypic and genotypic point of view. Reflectance spectroscopy, HPLC profiles and molecular markers related to VvMybA1 and VvMybA2 genes allowed a detailed description of the pigment-related characteristics of these cultivars. Data were consistent concerning the heterozygosity of the non-functional allele in both investigated genes, resulting in a low colored phenotype as described by reflectance. However, the variability in berry colour among our samples was not fully explained by MybA locus, probably due to specific interferences among the biosynthetic pathways, as suggested by the anthocyanin profile variations detected among our samples. The results presented in this work confirmed the importance of the genetic background: grapes accumulating high levels of cyanidin-3-O-glucosides (di-substituted anthocyanin) are generally originated by white cultivar retro-mutations and they seem to preserve the anomalies in the flavonoid hydroxylases enzymes which negatively affect the synthesis of tri-substituted anthocyanins.

Correction for tissue optical properties enables quantitative skin fluorescence measurements using multi-diameter single fiber reflectance spectroscopy

BACKGROUND AND OBJECTIVE

Fluorescence measurements in the skin are very much affected by absorption and scattering but existing methods to correct for this are not applicable to superficial skin measurements.

STUDY DESIGN/MATERIALS AND METHODS

The first use of multiple-diameter single fiber reflectance (MDSFR) and single fiber fluorescence (SFF) spectroscopy in human skin was investigated. MDSFR spectroscopy allows a quantification of the full optical properties in superficial skin (μa, μs' and γ), which can next be used to retrieve the corrected - intrinsic - fluorescence of a fluorophore Qμa,x(f). Our goal was to investigate the importance of such correction for individual patients. We studied this in 22 patients undergoing photodynamic therapy (PDT) for actinic keratosis.

RESULTS

The magnitude of correction of fluorescence was around 4 (for both autofluorescence and protoporphyrin IX). Moreover, it was variable between patients, but also within patients over the course of fractionated aminolevulinic acid PDT (range 2.7-7.5). Patients also varied in the amount of protoporphyrin IX synthesis, photobleaching percentages and resynthesis (>100× difference between the lowest and highest PpIX synthesis). The autofluorescence was lower in actinic keratosis than contralateral normal skin (0.0032 versus 0.0052; P<0.0005).

CONCLUSIONS

Our results clearly demonstrate the importance of correcting the measured fluorescence for optical properties, because these vary considerably between individual patients and also during PDT. Protoporphyrin IX synthesis and photobleaching kinetics allow monitoring clinical PDT which facilitates individual-based PDT dosing and improvement of clinical treatment protocols. Furthermore, the skin autofluorescence can be relevant for diagnostic use in the skin, but it may also be interesting because of its association with several internal diseases.

Engineering optical properties of gold-coated nanoporous anodic alumina for biosensing

The effect in the Fabry-Pérot optical interferences of nanoporous anodic alumina films coated with gold is studied as a function of the porosity and of the gold thickness by means of reflectance spectroscopy. Samples with porosities between 14 and 70% and gold thicknesses (10 and 20 nm) were considered. The sputtering of gold on the nanoporous anodic alumina (NAA) films results in an increase of the fringe intensity of the oscillations in the spectra resulting from Fabry-Pérot interferences in the porous layer, with a reduction in the maximum reflectance in the UV-visible region. For the thicker gold layer, sharp valleys appear in the near-infrared (IR) range that can be useful for accurate spectral shift measurements in optical biosensing. A theoretical model for the optical behavior has also been proposed. The model shows a very good agreement with the experimental measurements, what makes it useful for design and optimization of devices based on this material. This material capability is enormous for using it as an accurate and sensitive optical sensor, since gold owns a well-known surface chemistry with certain molecules, most of them biomolecules.

Investigating viability of intestine using spectroscopy: a pilot study

BACKGROUND

The differentiation of "viable" from "nonviable" bowel remains a challenge in the treatment of acute mesenteric ischemia. In this study, diffuse reflectance spectroscopy (DRS) was used to investigate the viability of bowel tissue after ischemia and reperfusion in an animal model in vivo and in real time.

METHODS

A total of 25 females Sprague-Dawley rats were divided into five groups based on different bowel ischemia times. In each study group for four of them, the superior mesenteric artery was occluded using a vascular clamp for a different period (i.e., 30, 45, 60, and 90 min; n = 5 for each group). Intestinal reperfusion was accomplished by releasing the clamps after the given occlusion period for each group. Spectra were acquired by gently touching the optical fiber probe to the bowel tissue before the induce ischemia, at the end of the induced ischemia, and after the reperfusion. The data acquired before the ischemia were used as a control group. Without occluding the superior mesenteric artery, the spectra were acquired on the bowel with the same time intervals of the experiments were used as a sham group (n = 5). Subsequently, the same bowel segments were sent for histopathologic examination.

RESULTS

Based on the correlation between the spectra acquired from the bowel segments and the results from the histopathologic investigation, DRS is able to differentiate the histopathologic grading that appears when the Chiu/Park score ≥5 (i.e., high-level ischemic injury) than Chiu/Park score <5. Eight out of nine low-level ischemic injury tissue samples were correctly defined using the spectroscopic classification system. All eleven high-level ischemic injury tissues that were histopathologically assigned grade 5 and above were correctly defined using the spectroscopic classification system in the ischemia-reperfusion groups.

CONCLUSIONS

DRS could potentially be used intraoperatively for the assessment of bowel viability in real time. These preliminary findings suggest that DRS has the potential to reduce unnecessary resection of viable tissue or insufficient resection of nonviable tissues may reduce the mortality and morbidity rates of intestinal ischemia-reperfusion as acute mesenteric ischemia.

Effect of nanostructured lipid vehicles on percutaneous absorption of curcumin

The present study describes the production and characterization of monoolein aqueous dispersions (MAD) and lecithin organogels (ORG) as percutaneous delivery systems for curcumin (CUR). In particular, MAD stabilized by sodium cholate/poloxamer and w0 3 ORG lipid carriers, both in the presence and absence of CUR, have been considered: MAD morphology and dimensional distribution have been investigated by Cryogenic Transmission Electron Microscopy (cryo-TEM) and Photon Correlation Spectroscopy (PCS), while the inner structure of MAD and ORG has been studied by X-ray scattering techniques. As a general result, CUR chemical stability has been found to be better controlled by MAD, probably because CUR is more protected in the case of CUR-MAD with respect to CUR-ORG. To investigate the performance of differently composed lipid formulations as CUR delivery system, in vitro studies, based on Franz cell and stratum corneum-epidermis (SCE) membranes, and in vivo studies, based on skin reflectance spectrophotometry and tape stripping, were then performed. The results indicated that ORG induces a rapid and intense initial penetration of CUR probably due to a strong interaction between the peculiar supramolecular aggregation structure of phospholipids in the vehicle and the lipids present in the stratum corneum. Conversely, CUR incorporated into MAD can be released in a controlled fashion possibly because of the formation of a CUR depot in the stratum corneum. In this respect ORG could be employed in pathologies requiring rapid CUR action, while MAD could be proposed for assuring a prolonged CUR activity.