Fourier Transform Infrared Spectroscopy Data Research Articles

Identification on Chemical Constituent of Alpinia purpurata Hydrosol Extracted by Hydro Distillation Method

Alpinia purpurata (Halia Bara) is a perennial herbaceous plant with virtually identical qualities to common ginger, but with smaller and more pungent rhizomes. It is widely known for ornamental purposes and is also applied in the medicinal field. Extraction of essential oil and hydrosol of Alpinia purpurata can be done through the hydro distillation method. The essential oil contains α-pinene, β-caryophyllene, geranial, neral and β-pinene that contribute to medicinal values while hydrosol's chemical components are yet to be identified. Hence, the primary objective of this study is to use the hydro distillation method to identify the chemical constituents and functional groups of active chemical compounds present in Alpinia purpurata's rhizomes hydrosol. Hydro distillation is also done at different heating temperatures and distillation times to see what influence temperature and distillation time have on the components and active compounds in the hydrosol. The powdered sample is used for extraction at 60°C, 80°C, and 100°C, with distillation periods of 1, 1.5, 2, and 2.5 hours. Burette is used to extract essential oil from the hydrosol. To further investigate the hydrosol, gas chromatography-mass spectrometry (GC-MS) and Fourier-transform Infrared Spectroscopy (FTIR) are utilised extensively. The major chemical constituents detected in Alpinia purpurata's hydrosol at 100°C for 2.5 hours of distillation time include 1-Dodecanamine (40.08 %) and functional groups present are O-H, N-H, C-H, and C=H stretching, according to GC-MS and FTIR data. The increased heating temperature and distillation time caused the denaturation of substances in the extracted hydrosol. The chemical constituents present in hydrosol are greatly different from the chemical constituents that are present in essential oil qualitatively and quantitatively.

Weighted LASSO variable selection for the analysis of FTIR spectra applied to the prediction of engine oil degradation

The aim of this work is to quantify the relationship between different methods of artificial oil alteration as well as engine oils collected from a passenger car using FTIR (Fourier-transform infrared) spectroscopic data and chemometric methods. We propose a comprehensive procedure for the analysis of FTIR spectra: First, a reconstruction error based pre-processing to filter non-informative variables is introduced, then simultaneous variable selection and parameter estimation using the (weighted) LASSO is performed. Eventually, post-selection inference is applied to derive confidence intervals for the selected model coefficients. The proposed pre-processing methods do not rely on manual selection of FTIR absorption bands suitable for analysis but perform filtering of non-informative variables objectively. With weighted LASSO, experts’ knowledge can be integrated with the model. This pipeline for the analysis of FTIR spectroscopic data is demonstrated and validated on a real-world dataset including series of FTIR spectra of used and artificially altered engine oils.

Continuous Monitoring and Characterization of Copolymerization Reactions of Acrylate Monomers with Indistinguishable Ultraviolet Spectra Using Infrared Spectroscopy

AbstractApplications of Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP) have steadily expanded over the past 20 years. An ultraviolet (UV) detector, often combined with a differential refractive index detector (RID), is the usual method to track monomer conversion, but the method can fail when comonomers have similar UV spectra. Here, Fourier‐transform infrared spectroscopy (FTIR) is coupled to ACOMP to separate comonomer conversion during solution copolymerization of tert‐butyl acrylate (tBA) and n‐butyl acrylate (nBA). The UV spectra of these comonomers are too similar for separation. The FTIR region between 1100 and 1410 cm−1, however, shows a strong difference between tBA and nBA peaks—1403 and 1192 cm−1, respectively. Reaction rates determined from FTIR data show that tBA's is higher than nBA's. The terpolymerization of tBA, nBA, and methyl methacrylate (MMA) is also monitored, using MMA's 1325 cm−1 peak. Reaction rates are significantly lower than in the nBA/tBA copolymerization. The incorporation of FTIR into ACOMP opens the path for further expansion to a wide variety of copolymerization reactions, including systems of three or more comonomers. It can be a key new detector in the next steps for development of fully automatic feedback control of composition and molecular weight.

Injectable Platelet-Rich Fibrin as a Drug Carrier Increases the Antibacterial Susceptibility of Antibiotic-Clindamycin Phosphate.

The aim of this study was to investigate the change in clindamycin phosphate antibacterial properties against Gram-positive bacteria using the platelet-rich fibrin as a carrier matrix, and evaluate the changes in the antibiotic within the matrix. The antibacterial properties of CLP and its combination with PRF were tested in a microdilution test against reference cultures and clinical isolates of Staphylococcus aureus (S. aureus) or Staphylococcus epidermidis (S. epidermidis). Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM) analysis was done to evaluate the changes in the PRF_CLP matrix. Release kinetics of CLP was defined with ultra-performance liquid chromatography (UPLC). According to FTIR data, the use of PRF as a carrier for CLP ensured the structural changes in the CLP toward a more active form of clindamycin. A significant decrease in minimal bactericidal concentration values (from 1000 µg/mL to 62 µg/mL) against reference cultures and clinical isolates of S. aureus and S. epidermidis was observed for the CLP and PRF samples if compared to pure CLP solution. In vitro cell viability tests showed that PRF and PRF with CLP have higher cell viability than 70% after 24 h and 48 h time points. This article indicates that CLP in combination with PRF showed higher antibacterial activity against S. aureus and S. epidermidis compared to pure CLP solution. This modified PRF could be used as a novel method to increase drug delivery and efficacy, and to reduce the risk of postoperative infection.

Analysis of flower extract and natural dye solution from Sesbania javanica using Fourier-transform infrared spectroscopy (FTIR) chemometrics, and determination of its antioxidant and anti-glucosidase activities

Fourier-transform infrared spectroscopy (FTIR) chemometrics and determination of antioxidant and anti-glucosidase activities of flower extract and natural dye solution from Sesbania javanica were performed. Total phenolic content and total flavonoid content were the highest in ethanolic flower extract, while antioxidant activity (1/EC50 = 0.4935) and α-glucosidase inhibition (1/EC50 = 2.9671) were the highest in dye solution, obtained from mixing white clay filler with aqueous dye extract. The correlations of FTIR data, bioactive compound contents, and biological activities were assessed by partial least squares structural equation modelling. Results showed that a strongly positive relationship was significantly demonstrated between FTIR fingerprints and bioactive compound contents (β = 0.825, t = 17.037, p value < 0.05), and between bioactive compound contents and biological activities (β = 0.563, t = 3.000, p value < 0.05). However, a strongly negative relationship was significantly found between FTIR fingerprints and biological activities (β = -1.204, t = 6.065, p value < 0.05). Moreover, the PCA and cluster analysis from FTIR data led to well grouping in PC plots and phylogenetic trees. Interestingly, the PCA and cluster analysis from FTIR data of plant extracts in powdered form provided better separation in PC plots and phylogenetic trees than in liquid form.

P-433 Comparative analysis of serum obtained in early pregnancy using Fourier transform-infrared spectroscopy to predict tubal ectopic pregnancy and miscarriage

Abstract Study question Can Fourier transform-infrared (FT-IR) spectroscopy be used to discriminate adverse early pregnancy outcomes such as tubal ectopic pregnancy and miscarriage from live normally-sited pregnancies? Summary answer Biochemical information acquired from serum samples using FT-IR spectroscopy does not discriminate adverse early pregnancy outcomes from live normally-sited pregnancies. What is known already Ectopic pregnancy and miscarriage are associated with maternal morbidity and mortality. Current management of pregnancy of unknown location and viability may involve serial blood human chorionic gonadotrophin-beta measurements and several ultrasound scans. Multiple interventions and length of time to diagnosis are costly and have a profound psychological impact on women. Vibrational spectroscopic methods such as Fourier transform-infrared (FT-IR) spectroscopy have been increasingly shown to discriminate disease states such as cancer from controls utilising the ‘metabolomic signature’ of molecules within a biological sample. It is hypothesised that this signature will differ between live normally-sited pregnancies, and tubal ectopic pregnancy and miscarriage. Study design, size, duration A total of 380 women were recruited to the prospective cohort study, Ectopic Pregnancy Diagnosis sTudy (ExPeDITe) between November 2018 and November 2021. Inclusion criteria comprised pregnant women ≥18 years presenting with pain and/or bleeding at < 10 weeks of gestation. A sample of blood and demographic data were collected at the time of consent, and a full history was taken. Early pregnancy outcomes were collected and classified according to ESHRE guidance. Participants/materials, setting, methods Serum samples prepared from collected blood (n = 340) were diluted 1:4 to prevent oversaturation of spectra and quality controls were created from the pooled diluted serum. Samples were transferred to silicon plates and dried at 37.5 °C. Samples were subjected to FT-IR spectroscopy and spectral data were submitted to baseline correction, smoothing, and vector normalisation prior to multivariate statistical analysis including Principal Component Analysis (PCA) and Partial Least Squares – Discriminant Analysis (PLS-DA). Main results and the role of chance Infrared spectral data collected from live normally-sited pregnancies, tubal ectopic pregnancies, pregnancies of unknown location, and miscarriages displayed similar infrared signatures, exhibiting bands associated to vibrational modes related to the main molecular constituents commonly found in most biological samples such as proteins, lipids, nucleic acids, and carbohydrates. Spectral data were used as inputs to PCA and PLS-DA models as clustering algorithms in order to classify the different pregnancy outcomes. The output obtained by the classification model generated with PLS-DA shows two distributions; a ‘real’ and ‘null/random’ model. These models displayed poor correct classification rates, indicating no differences in the infrared signatures between the different pregnancy outcomes. Similar findings were also observed in the PCA results as no clustering pattern indicating correct classification between the experimental groups was observed in the scores plot. Although infrared spectroscopy has been well demonstrated as an analytical tool to discriminate several disorders from healthy/control samples including cancer, diabetes and many others, these findings indicate that the information about the overall biochemistry probed by infrared spectroscopy acquired from serum samples currently lacks accuracy/sensitivity to discriminate different pregnancy outcomes. Limitations, reasons for caution Proteins were not removed from samples prior to analysis, which may mask discreet changes in small molecule metabolites. As FT-IR spectra multivariate in nature power calculations are not possible. If this study had shown case-control separation then these findings would have been validated with a second set of samples. Wider implications of the findings The findings of this study suggest that FT-IR spectroscopy of serum may not be discriminatory to identify important early pregnancy outcomes, but further analytical techniques should be utilised to confirm if metabolomic analysis of blood or other biofluids would provide a novel diagnostic test. Trial registration number REC 17/NW/0646

Poly-caprolactone nanofibrous coated with sol-gel alginate/ mesenchymal stem cells for cartilage tissue engineering

Human adipose-derived mesenchymal stem cells (AMSCs) were encapsulated in alginate sol and coated on PCL nanofibers to produce a scaffold. The alginate solution was generated in three different concentrations (0.5%, 2%, and 3.5%) in the specified scaffold, and the resulting solutions were ionic cross-linked to prepare coated networks on the PCL nanofibers. We analyzed the samples using scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR) tests. The electrospinning method and coating alginate on PCL with a calcium chloride cross-linker were both successful, according to the SEM and FTIR data. The behaviors of AMSCs in in-vitro conditions were evaluated by viability and chondrogenic potential of alginate gel through MTT, DAPI, alcian staining, and immunofluorescence (ICC) assay, respectively. The results showed that PCL/Alg scaffolds not only support the adhesion and proliferation of AMSCs but also can enhance their chondrogenic differentiation without applying any external differential agent. The cells adequately exhibited the COLLAGEN type Ⅱ expression as a chondrogenic differentiation marker. As a result, alginate gels with cells coated on PCL nanofibers have a lot of potential for building bioactive networks as a substrate in cartilage tissue engineering.

Assessment of Easily Accessible Spectroscopic Techniques Coupled with Multivariate Analysis for the Qualitative Characterization and Differentiation of Earth Pigments of Various Provenance

Natural minerals and earths with coloring properties have been widely used as artistic pigments since prehistoric times. Despite being extensively studied, the complex chemistry of earth pigments is still unsatisfactory described with respect to their mineralogical and structural variability and origin. In this study, a large group of earth pigments from various geographical locations was investigated using easily accessible spectroscopic techniques and multivariate analysis with the aim to identify distinctive mineralogical and chemical characteristics of natural pigment sources. Portable X-ray fluorescence (p-XRF), Fourier transform infrared spectroscopy (FTIR) and fiberoptic Raman spectroscopy were used for the elemental, molecular and structural characterization of the investigated pigments. Diagnostic spectral features and chemical patterns (fingerprints) were identified and discussed with respect to their geological sources. Due to the occurrence of similar accompanying minerals, it was observed that the differentiation of red and yellow ochers is more challenging compared to green, brown and black pigments. However, for some of the investigated pigments, the presence of certain accessory minerals and/or of certain chemical patterns can have diagnostic value. Principal component analysis (PCA) of the FTIR and XRF data matrices showed promising results in terms of geological attribution, highlighting a promising tool for provenance research. The results of the study demonstrate the potential benefits of this rapid and nondestructive approach for the characterization and differentiation of earth pigments with similar hues coming from different geological sources.

The Agronomic Traits, Alkaloids Analysis, FT-IR and 2DCOS-IR Spectroscopy Identification of the Low-Nicotine-Content Nontransgenic Tobacco Edited by CRISPR-Cas9.

In this study, the agricultural traits, alkaloids content and Fourier transform infrared spectroscopy (FT-IR) and two-dimensional correlation infrared spectroscopy (2DCOS-IR) analysis of the tobacco after Berberine Bridge Enzyme-Like Proteins (BBLs) knockout were investigated. The knockout of BBLs has limited effect on tobacco agricultural traits. After the BBLs knockout, nicotine and most alkaloids are significantly reduced, but the content of myosmine and its derivatives increases dramatically. In order to identify the gene editing of tobacco, principal component analysis (PCA) was performed on the FT-IR and 2DCOS-IR spectroscopy data. The results showed that FT-IR can distinguish between tobacco roots and leaves but cannot classify the gene mutation tobacco from the wild one. 2DCOS-IR can enhance the characteristics of the samples due to the increased apparent resolution of the spectra. Using the autopeaks in the synchronous map for PCA analysis, we successfully identified the mutants with an accuracy of over 90%.

Fourier transform infrared spectroscopy as a new tool for surveillance in local stewardship antimicrobial program: a retrospective study in a nosocomial Acinetobacter baumannii outbreak.

The aim of our study is to determine the discriminatory power of the Fourier transform infrared spectroscopy (FTIR), using multi-locus sequence typing (MLST) and pulsed-field gel electrophoresis (PFGE) as molecular typing references. The study included seventeen isolates (OXA-23- and OXA-58-producing Acinetobacter baumannii) previously recovered from clinical specimens during the period May 2010-April 2011. Molecular typing was performed by PFGE and MLST. The specimens were analyzed in quadruplicate using the IR Biotyper (Bruker GmbH, Bremen, Germany). For each isolate, the average of the spectra was used for the analysis of the data. Comparing FTIR data with MLST, the results obtained by IR Biotyper are very consistent with those from MLST, since the software was able to differentiate the three ST assigned to the strains. Comparing FTIR data with PFGE, most results could be confirmed, as IR Biotyper clearly differentiated ST-80 SLV OXA-58-producing A. baumannii (pulsotype 3) from the rest of strains of OXA-58-producing A. baumannii (pulsotypes 1 and 2). All the OXA-23-producing A. baumannii isolates (pulsotype 4) grouped together by FTIR. FTIR proved to be an effective tool to investigate local epidemiology, and can achieve the same typeability and discriminatory power as genome-based methods.

The use of infrared spectroscopy and chemometrics to investigate deterioration in vegetable tanned leather: potential applications in heritage science

Vegetable tanned leather presents a unique challenge to conservators and curators of heritage collections, as little is known about how its physical and chemical properties change upon deterioration. Developing a better understanding of deterioration processes would be incredibly valuable in informing the conservation, storage, and restoration of leather objects. Fourier Transform infrared spectroscopy (FTIR) used with attenuated total reflectance (ATR) is increasingly applied in the heritage sector due to its relative ease of application and potential to be non-destructive. However, whilst FTIR has been applied successfully to the understanding of deterioration in other protein-based materials such as parchment, its application to the analysis of leather has been limited, largely due to the highly complex spectra obtained. Here, we have developed multivariate statistical methods for the analysis of FTIR data obtained from a time-series of leather samples artificially degraded at different pH values. Principal component analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA) and k-means clustering, when used together, are demonstrated as powerful tools in identifying early subtle differences in the FTIR spectra as leather degrades, identifying differences occurring over time and between different environmental conditions. We show that k-means clustering of time series data was able to highlight some areas of the spectrum that might be indicative of degradation, which more common chemometric techniques could not. The methods we describe here have the potential to widen the application of FTIR as a fast, non-destructive and reliable tool for assessing the condition of archaeological and historical leather objects, ultimately leading to better informed conservation, storage and restoration of these objects.

Photizo: an open-source library for cross-sample analysis of FTIR spectroscopy data.

MotivationWith continually improved instrumentation, Fourier transform infrared (FTIR) microspectroscopy can now be used to capture thousands of high-resolution spectra for chemical characterization of a sample. The spatially resolved nature of this method lends itself well to histological profiling of complex biological specimens. However, current software can make joint analysis of multiple samples challenging and, for large datasets, computationally infeasible.ResultsTo overcome these limitations, we have developed Photizo—an open-source Python library enabling high-throughput spectral data pre-processing, visualization and downstream analysis, including principal component analysis, clustering, macromolecular quantification and mapping. Photizo can be used for analysis of data without a spatial component, as well as spatially resolved data, obtained e.g. by scanning mode IR microspectroscopy and IR imaging by focal plane array detector.Availability and implementationThe code underlying this article is available at https://github.com/DendrouLab/Photizo with access to example data available at https://zenodo.org/record/6417982#.Yk2O9TfMI6A.

Contrary to an old belief, four corner curing does not provide more bond strength or polymerization in bonding of orthodontic metal brackets

Objectives: The study aimed to investigate the effect of the direction of light curing on bond strength and adhesive polymerization of orthodontic metal brackets. Material and Methods: In this in vitro investigation, 75 extracted human upper premolars were randomly divided into three groups according to the curing direction: Group A (20 s curing from the occlusal side of the bracket), Group B (10 s from occlusal and 10 s from gingival), and Group C (5 s from four corners of the bracket). After bonding, the brackets were subjected to a shear bond strength (SBS) test performed with a universal testing machine. Moreover, Fourier transform infrared spectroscopy (FTIR) analysis was used to find the polymerization ratio of the adhesives in each group. Kruskal–Wallis test was to statistical analysis of SBS and FTIR data, respectively, at the 0.05 level of significance. Results: The difference in SBS of metal brackets was not statistically significant between the groups (P > 0.05). However, the difference in polymerization ratio was significant between all groups where the highest and lowest ratio belonged to Groups A and B, respectively (P < 0.05). Conclusion: Curing metal brackets from four corners do not increase their bond strength and/or polymerization.

Predicting crude oil properties using fourier-transform infrared spectroscopy (FTIR) and data-driven methods

Two data-driven approaches based on the Fourier-transform infrared spectroscopy (FTIR) data are presented in this work to predict crude oil properties. The first approach is the combination of the principal component analysis (PCA) and the support vector regression (SVR), namely PCA-SVR. In the PCA-SVR, the PCA is employed to extract the high-dimension FTIR data to obtain lower-dimensional data. The lower-dimensional data is utilized as the inputs of the SVR to predict crude oil properties. The second approach is a hybrid model composed of the autoencoder and the SVR, namely Auto-SVR. In the Auto-SVR, the autoencoder is exploited to learn new representations for the dimensionality reduction of the FTIR data. The learned lower-dimensional representations are input into the SVR to predict crude oil properties. The presented data-driven approaches are used to predict fractions of light virgin naphtha (LVN), heavy virgin naphtha (HVN), kerosene (Kero), distillate, vacuum gas oil (VGO), and residual in crude oil. According to the obtained results, the presented methods can achieve accurate predictions with satisfactory prediction accuracy.

Fourier Transform Infrared (FTIR) Spectroscopic Analysis of Amino Acid Serine for its Chiral Identification

Synthesis of chiral drugs required chiral starting materials organic compounds. DL serine is an amino acid that is used as a key starting material in the synthesis of many drug substances. D or L isomer of Serine will lead to the chiral drug substances whereas DL mixture will lead to racemic drug substances. FTIR can be used as the primary analytical tool for the identification of Serine isomers and its racemic mixture. Many absorption bands in FTIR spectra identify whether a molecule is a specific isomer or racemic mixture. A systematic comparison of FTIR spectroscopic data is carried out revealing that the compound is the specific isomer and the racemic mixture shows different spectra with different bands.

Thermal Migration Behavior of Na+, Cu2+ and Li+ in Montmorillonite

The main aim of this paper is to study the cation fixation sites in montmorillonite after heating at different temperatures. Montmorillonite was used to adsorb cations (Na+, Cu2+ and Li+) in the solution, and the montmorillonite-adsorbed cations were heated at different temperatures (unheated, 100 °C, 200 °C and 300 °C) for 25 h. Subsequently, the basal spacing of montmorillonite treated at different temperatures was monitored by X-ray diffraction (XRD). The exchangeable cationic content (Na+, Cu2+ and Li+) in montmorillonite was determined based on an inductively coupled plasma emission spectrometer (ICP-OES). In addition, the stretching and bending vibration changes in the OH group and the Si-O bond in montmorillonite were detected by Fourier transform infrared spectroscopy (FTIR). The vibration changes were related to the cation fixation sites. The XRD data showed that when the heating temperature reached 200 °C, the structure of montmorillonite adsorbing Li+ and Cu2+ ions completely collapsed, but the layer spacing of montmorillonite adsorbing Na+ decreased slightly, which indicated that Li+ and Cu2+ were more easily able to enter the crystal structure. The ICP-OES results showed that the contents of exchangeable Na+, Cu2+ and Li+ in montmorillonite decreased with the increase in heating temperature, and Li+ was more easily fixed by montmorillonite than Na+ and Cu2+. The FTIR data showed that when montmorillonite adsorbed with Li+ was heated at more than 200 °C, a new OH stretching vibration band appeared at 3971 cm−1, which may be caused by the migration of Li+ into the octahedral vacancy to form a local trioctahedral structure. Na+ has a large radius; it can only be fixed near the OH group and may not enter the tetrahedron/octahedron of montmorillonite. The number of charges carried by Cu2+ is high and the dehydration enthalpy of hydrated Cu2+ is high. When the heating temperature was greater than 200 °C, Cu2+ mainly entered the hexagonal cavity of the tetrahedron and caused slight changes in the OH bending vibration. The vibration of the Si-O bond hardly changed after montmorillonite adsorbed Na+, but the stretching vibration peak of the Si-O bond moved to the high value region after adsorbing Cu2+ and Li+, which was speculated to be related to the migration of Cu2+ and Li+ into the crystal structure.

Deep reinforced neural network model for cyto-spectroscopic analysis of epigenetic markers for automated oral cancer risk prediction

Understanding epigenetic changes can provide vital information for early stage oral cancer diagnosis. Vibrational spectroscopy methods like Raman spectroscopy (RS) and Fourier Transform Infrared Spectroscopy (FTIR) can provide several advantages over conventional molecular biology methods, by incorporating information from fingerprint regions. Moreover, application of advanced spectral analysis tools like deep learning (DL) techniques can be efficiently applied for analyzing the large spectral dataset and extracting the vital features. Epigenetic changes are identifies in oral epithelial cells of healthy individuals, oral leucoplakia and squamous cell carcinoma patients through analysis of Raman (400-1800 ​cm−1) and FTIR data (700-2000 ​cm−1). Deep reinforced neural network (DRNN) model is employed to classify the epigenetic changes identified from the Raman and FTIR spectra. Feature extraction layer of DL model uses peak detection layer and reinforced learning layer to identify significant epigenetic features. Classification layer is made up of N numbers of back propagated Artificial Neural Network (ANN) layers. DL model developed is fully automated and overcome the wave shift problem of spectroscopic data. Testing accuracy of the proposed DRNN model is 83.33%. Class wise accuracies for NRML, OLPK and OSCC are 83.3%, 87% and 95.24%, respectively. Proposed DRNN model attains an overall ROC of 0.88 Present study employs combination of two complementary vibrational spectroscopy methods for oral cancer detection and chemometric analysis of the spectral features with DRNN mode. Identification of the epigenetic changes and utilization of the knowledge in cancer prediction will enable the proposed study to develop smart point-of-care diagnostic system.

Fourier transform infrared spectroscopy and chemometrics for the discrimination of animal fur types

Rapid and reliable animal fur identification has remained a challenge for customs inspection. The accurate distinction between fur types has a significant meaning in implementing the correct tariff policy. A variety of analytical methods have been applied to work on distinguishing animal fur types, with tools of microscopy, molecular testing, mass spectrometry, Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. In this research, the capability of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) combined with pattern recognition methods was investigated for the discrimination of animal fur in six types. This work was to explore the non-destructive application of ATR-FTIR technique in discriminant analysis of animal fur. All spectra were collected by ATR-FTIR of the wavenumber ranging from 4000 to 650 cm−1. Data pretreatments included moving average smoothing and multiplicative scatter correction (MSC). Four supervised classification algorithms were chosen to categorize the types of fur: soft independent modeling of class analogy (SIMCA), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM). PLS-DA and LS-SVM were both effective approaches, with a 100% classification accuracy rate. The accuracy of PCA-LDA and SIMCA was 98.33% and 99.44%, respectively. Furthermore, LS-SVM model obtained using Monte-Carlo sampling method also obtained 100% prediction accuracy, while all other methods produced misclassification. LS-SVM corrected the non-linearities for the animal fur FTIR data but also remarkably improved the prediction performance level. The results of this study revealed that the combination of ATR-FTIR and chemometrics has a huge potential for animal fur discrimination.

TOOTHFIR: Presenting a dataset and a preliminary meta-analysis of Fourier Transform Infra-red Spectroscopy indices from archaeological and palaeontological tooth enamel

The assessment of the preservation status of archaeological and palaeontological tooth enamel is of vital importance in the interpretation of isotopic and elemental analyses that underpin chronological, dietary, and mobility studies. Transmission and ATR Fourier Transform Infra-red Spectroscopy (FTIR) have provided an important means of studying the presence and crystallographic context of key structural groups in tooth enamel. However, differences in sample preparation and measurement methods prevent the setting of absolute references to assess tooth enamel preservation. Here, we present TOOTHFIR, a dataset of FTIR indices developed to study the crystal-chemical properties of enamel bioapatite, from Miocene, Pliocene, Pleistocene, Holocene, and modern samples. Using collected data, we conducted a preliminary statistical meta-analysis to assess inter-lab measurement variability (relying on modern samples) and the extent to which the mixed data remains useful in revealing diagenetic patterns. Our results confirmed significant variations with measurement methodology although it remained possible to observe broad relationships between the values for FTIR indices and environmental parameters. In this respect, we introduced the use of principal component analysis to summarize FTIR data. To improve data comparability and interpretation of preservation status, we suggest several measures.

Neodymium oxide nanoparticles synthesis using phytochemicals of leaf extracts of different plants as reducing and capping agents: Growth mechanism, optical, structural and catalytic properties

AbstractThe main idea of this research is to synthesize the neodymium oxide (Nd2O3) nanoparticles with a particle size of less than 10 nm and to investigate the influence of leaf extracts of different plants. Leaf extracts of three plants namely Phyllanthus emblica, Citrus limetta, and Citrus paradisi, were used for the fabrication of Nd2O3 via the sol–gel and green synthesis methods, and the mechanism for the synthesis of neodymium oxide has been investigated. All prepared samples were subjected to UV–Visible Spectroscopy, Fourier Transform Infrared spectroscopy (FTIR), Thermogravimetric analysis (TGA), X‐ray diffraction (XRD) analysis, and Scanning Electron Microscopy (SEM) and Energy Dispersive X‐ray (EDX) analysis to study the optical and structural properties. The band gap of the neodymium oxide nanoparticles was observed between 4.45 and 5.26 eV. The bond length of Nd‐O for the neodymium oxide samples as‐synthesized was calculated as 2.18 and 2.24 Å by using FTIR data. Furthermore, kinetic and thermodynamic parameters were calculated using TGA. XRD patterns showed the formation of cubic, monoclinic, and hexagonal structures of Nd2O3. The Nd2O3 nanoparticles were used as photocatalysts for the degradation of methylene blue under irradiation of sunlight, and the maximum percentage degradation of 74.8% was observed.