Showing papers in "Spectroscopy in 2018"

XPS and FT-IR Characterization of Selected Synthetic Corrosion Products of Zinc Expected in Neutral Environment Containing Chloride Ions

Abstract: ZnO, Zn(OH)2, Zn5(OH)8Cl2·H2O, ZnCO3, and Zn5(CO3)2(OH)6 synthetic powders were prepared by chemical or solid-state method. Their crystalline phase structure, thermal behavior, and morphology were examined. Characteristic infrared absorbance bands were estimated by means of FT-IR ATR spectroscopy. X-ray photoelectron spectroscopy (XPS) allowed to calculate the modified Auger parameters (α ′) thereof to 2010.2, 2009.3, 2009.4, 2009.7, and 2009.8 eV, respectively for ZnO, Zn(OH)2, Zn5(OH)8Cl2·H2O, ZnCO3, and Zn5(CO3)2(OH)6. Finally, comparison of surface composition may be crucial to evaluation of the unknown experimental spectra of corrosion products formed on the surface of zinc alloy coatings exposed in NaCl solution.

Visible and Near-Infrared Reflectance Spectroscopy for Investigating Soil Mineralogy: A Review

Abstract: Clay minerals are the most reactive and important inorganic components in soils, but soil mineralogy classifies as a minor topic in soil sciences. Revisiting soil mineralogy has been gradually required. Clay minerals in soils are more complex and less well crystallized than those in sedimentary rocks, and thus, they display more complicated X-ray diffraction (XRD) patterns. Traditional characterization methods such as XRD are usually expensive and time-consuming, and they are therefore inappropriate for large datasets, whereas visible and near-infrared reflectance spectroscopy (VNIR) is a quick, cost-efficient, and nondestructive technique for analyzing soil mineralogic properties of large datasets. The main objectives of this review are to bring readers up to date with information and understanding of VNIR as it relates to soil mineralogy and attracts more attention from a wide variety of readers to revisit soil mineralogy. We begin our review with a description of fundamentals of VNIR. We then review common methods to process soil VNIR spectra and summary spectral features of soil minerals with particular attention to those <2 μm fractions. We further critically review applications of chemometric methods and related model building in spectroscopic soil mineral studies. We then compare spectral measurement with multivariate calibration methods, and we suggest that they both produce excellent results depending on the situation. Finally, we suggest a few avenues of future research, including the development of theoretical calibrations of VNIR more suitable for various soil samples worldwide, better elucidation of clay mineral-soil organic carbon (SOC) interactions, and building the concept of integrated soil mapping through combined information (e.g., mineral composition, soil organic matter-SOM, SOC, pH, and moisture).

Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy

Abstract: Consumers concern about food adulteration. Pork meat is the principal adulterated species of beef and mutton. The conventional detection methods have their own limitations; therefore, we sought to develop an efficient and economical identification method using an infrared spectroscopy technique for meat. The Mahalanobis distance method was used to remove outliers in spectrum data. Interferences were eliminated using multiple scatter correction, standard normal variate, Savitzky-Golay smoothing, and normalization. The partial least square discriminant analysis (PLS-DA) and support vector machine (SVM) were used to establish identification models. In the Mahalanobis distance method, the coefficient of test sets was increased from 0.93 to 0.99; the RMSEC and RMSECV were decreased from 0.17 to 0.09 and 0.21 to 0.11 accordingly. The coefficient of determination in-between the calibration and testing sets in PLS-DA reached 0.99 and 0.99, RMSEC was 0.06, and both the RMSECV and RMSEP were 0.08. In contrast, in SVM, methods were 0.97 and 0.96. The RMSEC, RMSECV, and RMSEP were 0.15, 0.17, and 0.24, respectively. In summary, using a combination of infrared spectroscopy technology with PLS-DA was a better identification method than the SVM method that can be used as an effective method to identify pork, beef, and mutton meat samples.

Comparison of Methods for Estimating Mechanical Properties of Wood by NIR Spectroscopy

Abstract: Near-infrared (NIR) spectroscopy provides a rapid alternative to traditional methods of wood property assessment. For organizations who assess wood properties on a large scale, multisite, multispecies calibrations are of practical interest. We examined NIR spectroscopy for the estimation of density (at 12% moisture content), modulus of elasticity (MOE), and modulus of rupture (MOR) using clear wood samples obtained from several pine species (Pinus caribaea var. bahamensis, var. hondurensis, and var. caribaea, P. chiapensis, P. maximinoi, P. oocarpa, P. taeda, and P. tecunumanii). We compared different methodologies for collecting spectra, that is, benchtop instrument versus benchtop fiber-optic probe and field portable fiber-optic probe, and different wood surfaces (radial and transverse). Calibrations based on the benchtop instrument were superior to those obtained using the fiber-optic probe systems. Difficulty with adequately representing the sample when collecting spectra using a fiber-optic probe and lower quality spectra explain the differences among the data sets. Spectra collected from radial and transverse surfaces provided similar calibration statistics. The calibrations obtained for density (R2 = 0.81, SECV = 38.5 kg/m3) and MOE (R2 = 0.81, SECV = 1124 GPa) using benchtop instrument spectra demonstrate that it is possible to obtain general calibrations for estimating the wood properties of a number of tropical, subtropical, and temperate pine species.

Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

Abstract: Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIE , chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.

Preparation of Surface-Enhanced Raman Scattering Substrates Based on Immobilized Silver-Capped Nanoparticles

Abstract: Novel results concerning surface-enhanced Raman scattering mediated by thiol-immobilized capped silver nanoparticles attached to a silicon Si(100) substrate are presented. The attachment of the nanoparticles is achieved by chemically modifying the surface of Si(100) in order to provide sulfhydryl groups covalently linked to the substrate and then covering these surfaces with bare and polymer-capped silver nanoparticles. The modified silicon substrate, the nanoparticles, and the sensors are characterized by means of infrared and UV-vis spectroscopies and electronic microscopies. The surface-enhanced Raman scattering intensity of the new films based on polymer-capped nanoparticles is compared with that obtained with silver bare nanoparticles using rhodamine 6G as a common chromophore. These results open a new route to the design of reversible and spot-to-spot reproducible surface-enhanced Raman scattering-based sensors supported by silver nanoparticles.

Detection of Adulterated Honey by Fluorescence Excitation-Emission Matrices

Abstract: Honey is a frequent target of adulteration through inappropriate production practices and origin mislabelling. Current methods for the detection of adulterated honey are time and labor consuming, require highly skilled personnel, and lengthy sample preparation. Fluorescence spectroscopy overcomes such drawbacks, as it is fast and noncontact and requires minimal sample preparation. In this paper, the application of fluorescence spectroscopy coupled with statistical tools for the detection of adulterated honey is demonstrated. For this purpose, fluorescence excitation-emission matrices were measured for 99 samples of different types of natural honey and 15 adulterated honey samples (in 3 technical replicas for each sample). Statistical t-test showed that significant differences between fluorescence of natural and adulterated honey samples exist in 5 spectral regions: (1) excitation: 240–265 nm, emission: 370–495 nm; (2) excitation: 280–320 nm, emission: 390–470 nm; (3) excitation: 260–285 nm, emission: 320–370 nm; (4) excitation: 310–360 nm, emission: 370–470 nm; and (5) excitation: 375–435 nm, emission: 440–520 nm, in which majority of fluorescence comes from the aromatic amino acids, phenolic compounds, and fluorescent Maillard reaction products. Principal component analysis confirmed these findings and showed that 90% of variance in fluorescence is accumulated in the first two principal components, which can be used for the discrimination of fake honey samples. The classification of honey from fluorescence data is demonstrated with a linear discriminant analysis (LDA). When subjected to LDA, total fluorescence intensities of selected spectral regions provided classification of honey (natural or adulterated) with 100% accuracy. In addition, it is demonstrated that intensities of honey emissions in each of these spectral regions may serve as criteria for the discrimination between natural and fake honey.

Statistically Coherent Calibration of X-Ray Fluorescence Spectrometry for Major Elements in Rocks and Minerals

Abstract: We applied both the ordinary linear regression (OLR) and the new uncertainty weighted linear regression (UWLR) models for the calibration and comparison of a XRF machine through 59 geochemical reference materials (GRMs) and a procedure blank sample. The mean concentration and uncertainty data for the GRMs used for the calibrations (Supplementary Materials) (available here) filewere achieved from an up-to-date compilation of chemical data and their processing from well-known discordancy and significance tests. The drift-corrected XRF intensity and its uncertainty were determined from mostly duplicate pressed powder pellets. The comparison of the OLR (linear correlation coefficient ∼0.9523–0.9964 and 0.9771–0.9999, respectively, for before and after matrix correction) and UWLR models ( ∼0.9772–0.9976 and 0.9970–0.9999, respectively) clearly showed that the latter with generally higher values of is preferable for routine calibrations of analytical procedures. Both calibrations were successfully applied to rock matrices, and the results were generally consistent with those obtained in other laboratories although the UWLR model showed mostly narrower confidence limits of the mean (slope and intercept) or lower uncertainties than the OLR. Similar sensitivity (∼2.69–46.17 kc·s−1·%−1 for the OLR and ∼2.78–59.69 kc·s−1·%−1 for the UWLR) also indicated that the UWLR could advantageously replace the OLR model. Another novel aspect is that the total uncertainty can be reported for individual chemical data. If the analytical instruments were routinely calibrated from the UWLR model, this action would make the science of geochemistry more quantitative than at present.

DFT Study for the Spectroscopic and Structural Analysis of p-Dimethylaminoazobenzene

Abstract: The study under consideration represents the computational calculations of Azo-based direct dye named p-(dimethylamino)azobenzene (DMAB) under the effect of solvents with different relative permittivities. A density functional theory (DFT) method at the B3LYP level with 6-311G++ was applied for the spectroscopic and structural analysis of the title compound. Calculations of geometric parameters (bond orders, bond lengths, and dihedral angles), electron densities, thermodynamic parameters, and orbital energies were performed for the title compound. Mulliken population analysis (MPA) as well as natural population analysis (NPA) was also performed at the B3LYP level with different solvents for finding solvent effects. In order to predict the reactivity of DMAB, molecular electrostatic potential (MESP) calculations were carried out for it. For vibrational analysis, the infrared (IR) spectra were computed for the title compound at the B3LYP/6-311G++ level in the gas phase and in different solvents with good agreement to the experimental FT-IR spectrum. The different modes of vibrations were assigned using potential energy distribution (PED). The computed Raman spectra also showed appreciable agreement with the experimental recorded Raman spectrum. The electronic absorption spectra of the title compound have been computed employing DFT/B3LYP with the 6-311G++ basis set in the gas phase and in four different solvents, that is, DMSO, ethanol, acetonitrile, and water which were compared with the experimental spectra with appreciable agreement. NBO analysis was carried out for understanding the intramolecular and intermolecular bonding of the compound and the density transfer from completely filled to unfilled orbital was found. The HOMO-LUMO energies were determined for analyzing the mechanism of intramolecular charge transfer.

Long-Term Ripening Evaluation of Ewes’ Cheeses by Fourier-Transformed Infrared Spectroscopy under Real Industrial Conditions

Abstract: The ripening changes over time of special cheeses (Pecorino, ewes’ ripe, and Gouda) made with ewes’ milk were evaluated using FTIR/ATR spectroscopy during approximately one year. The midinfrared FTIR/ATR analyses were carried out in different ripening times between the cheese varieties and processed by means of multivariate statistical approaches. Overall, during the maturation, we observed a downward trend of the absorbance intensity of the amide group peaks (1700 to 1500 cm−1), which is linked to the breakdown of peptide bonds. Similar behavior was obtained for the lipidic region (3000 to 2800 cm−1 and 1765 to 1730 cm−1). Hierarchical cluster analysis and principal component analysis allowed the evaluation of the physicochemical changes of the cheeses. The proteolysis occurs in a fast pace during the first trimester of the ripening process, and the lipids are converted to smaller species as the times goes by. Our results indicate that infrared spectroscopy can be a useful tool in determining optimal temporal parameters in stages involving the development, production, and even a possible estimation of shelf life of cheeses.

Detailed Spectroscopic and Structural Analysis of TiO2/WO3 Composite Semiconductors

Abstract: WO3-TiO2 composite materials were obtained using commercial titania (Evonik Aeroxide P25) and hydrothermally crystallized WO3. Different ratios of TiO2/WO3 were investigated, starting at 1 wt.% of WO3 to 50 wt.%. The morphology of WO3 was of the star-like type, and its structure is basically composed of monoclinic crystalline phase. All spectroscopic characteristics of the composites and their derived data (band-gap energy value, light absorption threshold, and IR specific bands) directly varied with the increase of the WO3 content. However, the oxalic acid photodegradation achieved under UV light reached the highest yield for 24 wt.% WO3 content, a result that was attributed to the charge separation efficiency and the surface hydrophilicity. The latter mentioned reason points out the crucial importance of the surface quality of the investigated structure in photocatalytic tests.

Detection of Veterinary Antimicrobial Residues in Milk through Near-Infrared Absorption Spectroscopy

Abstract: This study focuses on detection of antimicrobial residues in milk through Fourier transform near-infrared spectroscopy. Simulated and real samples were considered. The simulated ones take into account veterinary drugs added in milk samples in the following concentrations: enrofloxacin 100 μg/L, terramycin 100 μg/L, and penicillin 4 μg/L. The statistical tool used to discriminate the samples was the principal component analysis (PCA). Our results show that, with this experimental procedure, it is possible to discriminate different types of antimicrobials dissolved in milk. Moreover, the methodology was able to detect real sample milked on different days after the injection of ceftiofur hydrochloride which is in principle a zero grace period antimicrobial. The methodology proved to be fast and accurate within the maximum residue limits allowed by European Agency for Medicinal Products and Ministry of Agriculture Livestock and Food Supply from Brazil.

Characterization and Identification of Coal and Carbonaceous Shale Using Visible and Near-Infrared Reflectance Spectroscopy

Abstract: Because of the high organic carbon concentration in carbonaceous shale, a large proportion of carbonaceous shales are often misclassified into coals using visible and near-infrared (VIS-NIR) reflectance spectroscopy in the field of coal-gangue identification of hyperspectral remote sensing of coal mine. In order to study spectral characterization of coal and carbonaceous shale, three bituminite samples and three carbonaceous shales were collected from a coal mine of China, and their spectral reflectance curves were obtained by a field spectrometer in the wavelength range of 350–2500 nm. Only one carbonaceous shale could be easily identified from the three bituminite samples according to obvious absorption valleys near 1400 nm, 1900 nm, and 2200 nm of its reflectance curve while the other two carbonaceous shales have similar reflectance curves to the three bituminite samples. The effect of carbon concentration on reflectance curve was simulated by the mixed powder of ultralow ash bituminite and clay in 0.5 mm grain size under various mixing ratios. It was found that absorption valleys near 1400 nm, 1900 nm, and 2200 nm of the mixed powder become not obvious when the bituminite content is more than 30%. In order to establish an effective identification method of coal and carbonaceous shale, 250 other samples collected from the same coal mine were divided into 150 training samples and 100 prediction samples. Principal component analysis (PCA) and Gauss radial basis kernel principal component analysis (GRB-KPCA) were employed to extract principal components (PCs) of continuum removed (CR) spectra of the training samples in eight selected wavelength regions which are related to the main mineral and organic compositions. Two support vector machine- (SVM-) based models PCA-SVM and GRB-KPCA-SVM were established. The results showed that the GRB-KPCA-SVM model had better identification accuracies of 94% and 92% for powder and nature block prediction samples, respectively.

Chronological Classification of Ancient Mortars Employing Spectroscopy and Spectrometry Techniques: Sagunto (Valencia, Spain) Case

Abstract: Forty-two mortar samples, from two archaeological excavations located in Sagunto (Valencian Community, Spain), were analysed by both portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF) and inductively coupled plasma mass spectrometry (ICP-MS) to determine major and minor elements and traces including rare earth elements (REEs). Collected data were crossed with those previously obtained from Sagunto Castle mortars, and principal component analysis (PCA) was applied to discriminate the construction phases of the unearthed buildings. REE permitted to ascribe most of the masonries to the Roman Imperial period. Moreover, a statistical model was built by employing partial least squares discriminant analysis (PLS-DA) in order to classify the mortars from Roman Imperial period and from Islamic period due to the problematic overlapping between these two phases. Results confirmed the effectiveness of the developed indirect chronology method, based on REE data, to discriminate among historic mortars from different construction periods on a wide scale including different Sagunto archaeological sites.

Dynamic Localized SNV, Peak SNV, and Partial Peak SNV: Novel Standardization Methods for Preprocessing of Spectroscopic Data Used in Predictive Modeling

Abstract: An essential part of multivariate analysis in spectroscopic context is preprocessing. The aim of preprocessing is to remove scattering phenomena or disturbances in the spectra due to measurement geometry in order to improve subsequent predictive models. Especially in vibrational spectroscopy, the Standard Normal Variate (SNV) transformation has become very popular and is widely used in many practical applications, but standardization is not always ideal when performed across the full spectrum. Herein, three different new standardization techniques are presented that apply SNV to defined regions rather than to the full spectrum: Dynamic Localized SNV (DLSNV), Peak SNV (PSNV) and Partial Peak SNV (PPSNV). DLSNV is an extension of the Localized SNV (LSNV), which allows a dynamic starting point of the localized windows on which the SNV is executed individually. Peak and Partial Peak SNV are based on picking regions from the spectra with a high correlation to the target value and perform SNV on these essential regions to ensure optimal scatter correction. All proposed methods are able to significantly improve the model performance in cross validation and robustness tests compared to SNV. The prediction errors could be reduced by up to 16% and 29% compared with LSNV for two regression models.

Use of Electrochemical Impedance Spectroscopy for the Evaluation of Performance of PEM Fuel Cells Based on Carbon Cloth Gas Diffusion Electrodes

Abstract: Polymer electrolyte membrane fuel cells (PEMFCs) have attracted great attention in the last two decades as valuable alternative energy generators because of their high efficiencies and low or null pollutant emissions. In the present work, two gas diffusion electrodes (GDEs) for PEMFCs were prepared by using an ink containing carbon-supported platinum in the catalytic phase which was sprayed onto a carbon cloth substrate. Two aerograph nozzles, with different sizes, were used. The prepared GDEs were assembled into a fuel cell lab prototype with commercial electrolyte and bipolar plates and tested alternately as anode and cathode. Polarization measurements and electrochemical impedance spectroscopy (EIS) were performed on the running hydrogen-fed PEMFC from open circuit voltage to high current density. Experimental impedance spectra were fitted with an equivalent circuit model by using ZView software which allowed to get crucial parameters for the evaluation of fuel cell performance, such as ohmic resistance, charge transfer, and mass transfer resistance, whose trends have been studied as a function of the applied current density.

Silver Chloride Encapsulation-Induced Modifications of Raman Modes of Metallicity-Sorted Semiconducting Single-Walled Carbon Nanotubes

Abstract: The internal channels of semiconducting single-walled carbon nanotubes (SWCNTs) were filled with silver chloride. The filling was confirmed by high-resolution scanning transmission electron microscopy. The filling-induced modifications of Raman modes of SWCNTs were analyzed. The fitting of the radial breathing mode (RBM) and G-bands of Raman spectra of the pristine and filled nanotubes with individual components allowed analyzing in detail the influence of encapsulated silver chloride on the electronic properties of different diameter nanotubes. The analysis of the RBM-band allowed revealing the changes in resonance excitation conditions of SWCNTs upon filling. The analysis of the G-band allowed concluding about p-doping of nanotubes by incorporated silver chloride accompanied by charge transfer from nanotubes to the inserted salt.

Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon

Abstract: The cold storage time of salmon has a significant impact on its freshness, which is an important factor for consumers to evaluate the quality of salmon. The efficient, accurate, and convenient protocol is urgent to appraise the freshness for quality checking. In this paper, the ability of visible/near-infrared (VIS/NIR) spectroscopy was evaluated to predict the cold storage time of salmon meat and skin, which were stored at low-temperature box for 0~12 days. Meanwhile, a double-layer stacked denoising autoencoder neural network (SDAE-NN) algorithm was introduced to establish the prediction model without spectral pre-preprocessing. The results showed that, compared with the common methods such as partial least squares regression (PLSR) and back propagation neural network (BP-NN), the SDAE-NN method had a better performance due to its high efficiency in decreasing noise and optimizing the initial weights. The determination coefficient of test sets (R2test) and root mean square error of test sets (RMSEP) have been calculated based on SDAE-NN, for the salmon meat (skin), the R2test can reach 0.98 (0.92), and the RMSEP can reach 0.93 (1.75), respectively. It is highlighted that the algorithm is efficient and accurate and that the salmon meat would be more suitable for predicting freshness than the salmon skin. VIS/NIR spectroscopy combined with the SDAE-NN algorithm can be widely used to predict the freshness of various agricultural products.

Apple Variety Identification Using Near-Infrared Spectroscopy

Abstract: Near-infrared (NIR) spectra of apple samples were submitted in this paper to principal component analysis (PCA) and successive projections algorithm (SPA) to conduct variable selection. Three pattern recognition methods, backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM), were applied to establish models for distinguishing apples of different varieties and geographical origins. Experimental results show that ELM models performed better on identifying apple variety and geographical origin than others. Especially, the SPA-ELM model could reach 98.33% identification accuracy on the calibration set and 96.67% on the prediction set. This study suggests that it is feasible to identify apple variety and cultivation region by using NIR spectroscopy.

Characterization and Analysis of Ultrathin CIGS Films and Solar Cells Deposited by 3-Stage Process

Abstract: In view of the large-scale utilization of Cu(In,Ga)Se2 (CIGS) solar cells for photovoltaic application, it is of interest not only to enhance the conversion efficiency but also to reduce the thickness of the CIGS absorber layer in order to reduce the cost and improve the solar cell manufacturing throughput. In situ and real-time spectroscopic ellipsometry (RTSE) has been used conjointly with ex situ characterizations to understand the properties of ultrathin CIGS films. This enables monitoring the growth process, analyzing the optical properties of the CIGS films during deposition, and extracting composition, film thickness, grain size, and surface roughness which can be corroborated with ex situ measurements. The fabricated devices were characterized using current voltage and quantum efficiency measurements and modeled using a 1-dimensional solar cell device simulator. An analysis of the diode parameters indicates that the efficiency of the thinnest cells was restricted not only by limited light absorption, as expected, but also by a low fill factor and open-circuit voltage, explained by an increased series resistance, reverse saturation current, and diode quality factor, associated with an increased trap density.

Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division

Abstract: The traditional remote sensing image segmentation method uses the same set of parameters for the entire image. However, due to objects’ scale-dependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same scene and form a scene. Based on this scenario, this paper proposes a stratified object-oriented image analysis method based on remote sensing image scene division. This method firstly uses middle semantic which can reflect an image’s visual complexity to classify the remote sensing image into different scenes, and then within each scene, an improved grid search algorithm is employed to optimize the segmentation result of each scene, so that the optimal scale can be utmostly adopted for each scene. Because the complexity of data is effectively reduced by stratified processing, local scale optimization ensures the overall classification accuracy of the whole image, which is practically meaningful for remote sensing geo-application.

Near-Infrared Spectroscopic Study of Chlorite Minerals

Abstract: The mineral chemistry of twenty chlorite samples from the United States Geological Survey (USGS) spectral library and two other regions, having a wide range of Fe and Mg contents and relatively constant Al and Si contents, was studied via infrared (IR) spectroscopy, near-infrared (NIR) spectroscopy, and X-ray fluorescence (XRF) analysis. Five absorption features of the twenty samples near 4525, 4440, 4361, 4270, and 4182 cm−1 were observed, and two diagnostic features at 4440 and 4280 cm−1 were recognized. Assignments of the two diagnostic features were made for two combination bands ( and ) by regression with IR fundamental absorptions. Furthermore, the determinant factors of the NIR band position were found by comparing the band positions with relative components. The results showed that Fe/(Fe + Mg) values are negatively correlated with the two NIR combination bands. The findings provide an interpretation of the NIR band formation and demonstrate a simple way to use NIR spectroscopy to discriminate between chlorites with different components. More importantly, spectroscopic detection of mineral chemical variations in chlorites provides geologists with a tool with which to collect information on hydrothermal alteration zones from hyperspectral-resolution remote sensing data.

The Impact of Simulated Spectral Noise on Random Forest and Oblique Random Forest Classification Performance

Abstract: Hyperspectral datasets contain spectral noise, the presence of which adversely affects the classifier performance to generalize accurately. Despite machine learning algorithms being regarded as robust classifiers that generalize well under unfavourable noisy conditions, the extent of this is poorly understood. This study aimed to evaluate the influence of simulated spectral noise (10%, 20%, and 30%) on random forest (RF) and oblique random forest (oRF) classification performance using two node-splitting models (ridge regression (RR) and support vector machines (SVM)) to discriminate healthy and low infested water hyacinth plants. Results from this study showed that RF was slightly influenced by simulated noise with classification accuracies decreasing for week one and week two with the addition of 30% noise. In comparison to RF, oRF-RR and oRF-SVM yielded higher test accuracies (oRF-RR: 5.36%–7.15%; oRF-SVM: 3.58%–5.36%) and test kappa coefficients (oRF-RR: 10.72%–14.29%; oRF-SVM: 7.15%–10.72%). Notably, oRF-RR test accuracies and kappa coefficients remained consistent irrespective of simulated noise level for week one and week two while similar results were achieved for week three using oRF-SVM. Overall, this study has demonstrated that oRF-RR can be regarded a robust classification algorithm that is not influenced by noisy spectral conditions.

Using Compact 1H NMR, NIR, and Raman Spectroscopy Combined with Multivariate Data Analysis to Monitor a Biocatalyzed Reaction in a Microreaction System

Abstract: Process analytical technology aims at process knowledge and process improvement, efficiency, and sustainability. A prerequisite is process monitoring. The combination of microreaction systems and spectroscopy proved suitable due to dimension and compound reduction and real-time monitoring capabilities. Compact 1H NMR, NIR, and Raman spectroscopy were used to monitor the biocatalyzed hydrolysis and esterification of acetic anhydride to isoamyl acetate using immobilized Candida antarctica lipase B (CALB) in a microreaction system in real-time. To facilitate the identification of signals suitable for the extraction of concentration-time (c-t) graphs, 2D heterocorrelation spectra were generated through covariance transformations applied to 1D Raman, NIR, and NMR data. By means of this purely mathematical statistical procedure, the relevant signals of the process media were assigned to educts and products and thus made applicable for univariate data evaluation. The data obtained were interpreted in terms of a first-order kinetic model, and corresponding reaction rate constants were extracted. An alternative, elegant, and fit-for-automation approach for the kinetic analysis of the spectra was demonstrated in using multivariate curve resolution (MCR). The results of the univariate and multivariate approaches were comparable with regard to reaction rates and concentrations. While the manual integration of the 1H NMR spectra followed by univariate analysis allowed to establish a concentration profile of the final product isoamyl acetate hence revealing more details, multivariate analysis was found more suitable for process automation.

Mobile Spectroscopy in Archaeometry: Some Case Study

Abstract: We provide an overview of recent results obtained by the innovative application of mobile spectroscopy for in situ investigation in archaeometry. Its growing relevance is linked to the great advantages of avoiding the transport and eventual damage of precious artifacts and of allowing the analysis of those specimens that are, for example, built into infrastructures or in some way permanently affixed. In this context, some case studies of combined instrumental approaches, involving X-ray fluorescence (XRF) and Raman spectroscopy, integrated by infrared thermography (IRT), are, in particular, discussed: the archaeological site of Scifi (Forza d’Agro, province of Messina, Italy) and the Abbey of SS. Pietro e Paolo d’Agro (Casalvecchio Siculo, province of Messina, Italy). In the first case, the elemental composition, as obtained by XRF, of two types of mortars belonging to two different chronological phases, dated back between the 3rd and the 5th century AD, allowed us to hypothesize a same origin area of their raw materials and a different production technique. Again, the combined use of XRF and Raman spectroscopies, supported by IRT technique, on pottery fragments of Greek-Hellenistic age and late imperial period, furnished important information concerning the receipts for the pigmenting agents of the finishing layer, allowing in some cases their unambiguous identification. In the second case, XRF data collected on bricks and stones from the external facade of the abbey allowed us to make some hypothesis concerning the provenance of their constituents materials, supposed to be in the area of valley of the river Agro.

Characterization and Digital Restauration of XIV-XV Centuries Written Parchments by Means of Nondestructive Techniques: Three Case Studies

Abstract: Parchment is the primary writing medium of the majority of documents with cultural importance. Unfortunately, this material suffers of several mechanisms of degradation that affect its chemical-physical structure and the readability of text. Due to the unique and delicate character of these objects, the use of nondestructive techniques is mandatory. In this work, three partially degraded handwritten parchments dating back to the XIV-XV centuries were analyzed by means of X-ray fluorescence spectroscopy, µ-ATR Fourier transform infrared spectroscopy, and reflectance and UV-induced fluorescence spectroscopy. The elemental and molecular results provided the identification of the inks, pigments, and superficial treatments. In particular, all manuscripts have been written with iron gall inks, while the capital letters have been realized with cinnabar and azurite. Furthermore, multispectral UV fluorescence imaging and multispectral VIS-NIR imaging proved to be a good approach for the digital restoration of manuscripts that suffer from the loss of inked areas or from the presence of brown spotting. Indeed, using ultraviolet radiation and collecting the images at different spectral ranges is possible to enhance the readability of the text, while by illuminating with visible light and by collecting the images at longer wavelengths, the hiding effect of brown spots can be attenuated.

Excitation-Dependent Fluorescence Quantum Yield for Freshwater Chromophoric Dissolved Organic Matter from Northern Russian Lakes

Abstract: Advanced fluorescence analysis within the wide range of excitation wavelengths from 230 to 510 nm accompanied with chromatography was used to study natural chromophoric dissolved organic matter (CDOM) from three freshwater Karelian lakes. The influence of excitation wavelength (λex) on fluorescence quantum yield and emission maximum position was determined. The CDOM fluorescence quantum yield has reached a minimum at λex∼270–280 nm and a maximum at λex∼340–360 nm. It was monotonously decreasing after 370 nm towards longer excitation wavelengths. Analytical reversed-phase high-performance liquid chromatography with multiwavelength fluorescence detector characterized distribution of fluorophores between hydrophilic/hydrophobic CDOM parts. This technique revealed “hidden” protein-like fluorophores for some CDOM fractions, in spite of the absence of protein-like fluorescence in the initial CDOM samples. The humic-like fluorescence was documented for all hydrophilic and hydrophobic CDOM chromatographic peaks, and its intensity was decreasing along with peaks’ hydrophobicity. On contrary, the protein-like fluorescence was found only in the hydrophobic peaks, and its intensity was increasing along with peaks’ hydrophobicity. This work provides new data on the CDOM optical properties consistent with the formation of supramolecular assemblies controlled by association of low-molecular size components. In addition, these data are very useful for understanding the CDOM function in the environment.

Gd3+-Doped Magnetic Nanoparticles for Biomedical Applications

Abstract: Magnetic nanoparticles (MNPs) made of iron oxides with cubic symmetry (Fe3O4, γ-Fe2O3) are demanded objects for multipurpose in biomedical applications as contrast agents for magnetic resonance imaging, magnetically driven carriers for drug delivery, and heaters in hyperthermia cancer treatment. An optimum balance between the right particle size and good magnetic response can be reached by a selection of a synthesis method and by doping with rare earth elements. Here, we present a microwave-assisted polyol synthesis of iron oxide MNPs with actual gadolinium (III) doping from 0.5 to 5.1 mol.%. The resulting MNPs have an average size of 14 nm with narrow size distribution. Their surface was covered by a glycol layer, which prevents aggregation and improves biocompatibility. The magnetic hyperthermia test was performed on 1 and 2 mg/ml aqueous colloidal solutions of MNPs and demonstrated their ability to rise the temperature by 3°C during a 20–30 min run. Therefore, the obtained Gd3+ MNPs are the promising material for biomedicine.

Utilization of Machine Vision to Monitor the Dynamic Responses of Rice Leaf Morphology and Colour to Nitrogen, Phosphorus, and Potassium Deficiencies

Abstract: Machine vision technology enables the continuous and nondestructive monitoring of leaf responses to different nutrient supplies and thereby contributes to the improvement of diagnostic effects. In this study, we analysed the temporal dynamics of rice leaf morphology and colour under different nitrogen (N), phosphorus (P), and potassium (K) treatments by continuous imaging and further evaluated the effectiveness of dynamic characteristics for identification. The top four leaves (the 1st incomplete leaf and the top three fully expanded leaves) were scanned every three days, and all images were processed in MATLAB to extract the morphological and colour characteristics for dynamic analysis. Subsequently, the mean impact value was applied to evaluate the effectiveness of dynamic indices for identification. According to the results, higher nutrient supply resulted in a faster leaf extension rate and a lower developing rate of chlorosis, and the influence of N deficiency on leaf growth was the greatest, followed by P deficiency and then K deficiency. Furthermore, the optimal indices for identification were mainly calculated from morphological characteristics of the 1st incomplete leaf and colour characteristics of the 3rd fully expanded leaf. Overall, dynamic analysis contributes not only to the exploration of the plant growth mechanism but also to the improvement of diagnostics.

β-Tricalcium Phosphate Interferes with the Assessment of Crystallinity in Burned Skeletal Remains

Abstract: The analysis of burned remains is a highly complex process, and a better insight can be gained with advanced technologies. The main goal of this paper is to apply X-ray diffraction, partially supported by infrared attenuated total reflectance spectroscopy to determine changes in burned human bones and teeth in terms of mineral phase transformations. Samples of 36 bones and 12 teeth were heated at 1050°C and afterwards subjected to XRD and ATR-IR. The crystallinity index was calculated for every sample. A quantitative evaluation of phases was documented by using the Rietveld approach. In addition to bioapatite, the following mineralogical phases were found in the bone: β-tricalcium phosphate (β-TCP) (Ca3(PO4)2), lime (CaO), portlandite (Ca(OH)2), calcite (CaCO3), and buchwaldite (NaCaPO4). In the case of bone, besides bioapatite, only the first two mineralogical phases and magnesium oxide were present. We also observed that the formation of β-TCP affects the phosphate peaks used for CI calculation. Therefore, caution is needed when its occurrence and evaluation are carried out. This is an important warning for tracking heat-induced changes in human bone, in terms of physicochemical properties related to structure, which is expected to impact in forensic, bioanthropological, and archaeological contexts.