Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Spectroscopy Research Articles

Rapid Prediction of ANFO Based Explosives through ATR-FTIR Analysis – Use of ATR-FTIR in Explosives

Ammonium Nitrate Fuel Oil (ANFO) is preferred mining explosives in worldwide. It is composed of ammonium nitrate (94-96%) and liquid hydrocarbon as fuel oil (4-6%), which is detonated through an explosive charge. In India, Forensic Science Laboratories received many criminal cases from investigation agencies for chemical analysis of sample as semi-solid materials supposed to be explosive material. In the present study, we developed an Attenuated Total Reflectance - Fourier Transform Infrared Spectroscopy (ATR-FTIR) procedure for easily analyzing the real crime exhibits related to ANFO based explosives. Firstly, semi-solid material is directly used on the ATR. Further, the sample is extracted using appropriate solvents (diethyl-ether/acetone). Extracts are subsequently analyzed on ATR-FTIR in comparison with standards for ammonium nitrate and diesel. The residue after acetone extract is dried and left solid material directly used on ATR for the detection of water-soluble compounds. The results significantly showed the presence of ammonium nitrate with the residue of diesel in a real crime exhibit. Hence, the proposed modify procedure can be advantageous for the rapid detection of diesel components mixed in ammonium nitrate through ATR-FTIR spectroscopy without the use of other chemical or instrumental analysis in a short period of time and also for easily identifying the presence of organic explosives (if any) among different samples received for the forensic opinion.

Enhancing forensic investigations: Identifying bloodstains on various substrates through ATR-FTIR spectroscopy combined with machine learning algorithms

The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel approach for analyzing bloodstains using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) in combination with machine learning. ATR-FTIR offers non-destructive and non-invasive advantages, requiring minimal sample preparation. By detecting specific chemical bonds in blood components, it enables the differentiation of various body fluids. However, the subjective interpretation of the spectra poses challenges in distinguishing different fluids. To address this, we employ machine learning techniques. Machine learning is extensively used in chemometrics to analyze chemical data, build models, and extract useful information. This includes both unsupervised learning and supervised learning methods, which provide objective characterization and differentiation. The focus of this study was to identify human and porcine blood on substrates using ATR-FTIR spectroscopy. The substrates included paper, plastic, cloth, and wood. Data preprocessing was performed using Principal Component Analysis (PCA) to reduce dimensionality and analyze latent variables. Subsequently, six machine learning algorithms were used to build classification models and compare their performance. These algorithms comprise Partial Least Squares Discriminant Analysis (PLS-DA), Decision Trees (DT), Logistic Regression (LR), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and Neural Network (NN). The results indicate that the PCA-NN model provides the optimal solution on most substrates. Although ATR-FTIR spectroscopy combined with machine learning effectively identifies bloodstains on substrates, the performance of different identification models still varies based on the type of substrate. The integration of these disciplines enables researchers to harness the power of data-driven approaches for solving complex forensic problems. The objective differentiation of bloodstains using machine learning holds significant implications for criminal investigations. This technique offers a non-destructive, simple, selective, and rapid approach for forensic analysis, thereby assisting forensic scientists and investigators in determining crucial evidence related to bloodstains.

Surface preparation of aluminum by atmospheric-pressure plasma jet for suspension plasma sprayed ceramic coatings

Surface preparation is a key step preceding the deposition of suspension plasma spray (SPS) coatings, a type of thermal spray coatings. This work evaluates the effect of surface preparation by atmospheric-pressure plasma jet (APPJ) treatment on the adhesion of SPS-deposited Al2O3 ceramic coatings on aluminum (Al-2024) substrates. For comparison, the substrate surfaces were also prepared by grit-blasting. Surface roughness (R) parameters and surface morphology of both grit-blasted and APPJ-treated substrates were characterized by confocal microscopy and scanning electron microscopy (SEM). In addition, surface chemistry was evaluated by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR). Based on morphological analyses, the grit-blasted surfaces exhibit typical random peaks and valleys, whereas those subjected to APPJ treatment show self-organized patterns with multilayered and multiscale features. ATR-FTIR spectroscopy analyses also demonstrated a significant difference in surface chemistry for both surface preparation types. After the characterization of both types of prepared substrates, SPS coating was deposited on the substrates under two spray conditions. One spray condition demonstrated poor adherence of the coatings for both types of the prepared substrates; the other spray condition produced coatings with good adherence. The microstructures of the coating were evaluated for both spray conditions. The Adhesion strength of the coating was evaluated for the condition, which produced a coating with good adhesion. ASTM-C-633 pull test protocol was followed for the adhesion evaluation, and the pull test revealed significant adhesion improvements when the surface was prepared by APPJ. Improved adhesion strength was linked to the surface morphology and also to the surface chemistry of the surface prepared by APPJ. In addition, splat morphology was investigated on both grit-blasted and APPJ-treated substrates to correlate the improved adhesion.

Impact of processing methods (dry-heating, autoclaving, and (microwave irradiation) on protein-related molecular structure spectral feature and protein nutritive value of cool-seasoned oat varieties in ruminant system in western Canada

The objectives of this study were to reveal the effects of heating method and variety on (1) protein molecular structure spectral features revealed by Attenuated Total Reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), and (2) ruminant relevant protein chemical profiles, protein subfractions, rumen degradable protein (RDP), rumen undegradable protein (RUP), and intestinal protein digestibility using the Cornell Net Carbohydrate and Protein System (CNCPS) in oat varieties [CDC Nasser (feed-type oat) and CDC Seabiscuit (milling-type oat)]. Heating method included control (raw), dry heating (DH), moist heating (autoclaving, MH), and microwave irradiation (MIR). The dry matter (DM), crude protein (CP), and neutral detergent fiber (NDF) contents in CDC Nasser were 934, 107, and 252 g/kg DM, respectively; the contents of these nutrients in CDC Seabiscuit were 933, 110, and 268 g/kg DM, respectively. Heat processing significantly affected the total RDP (TRDP, P < 0.001), which was higher for DH than MIR and MH in CDC Nasser (5.46%, 5.01%, and 4.45%DM). Conversely, MIR had a higher value for TRDP than DH and MH in CDC Seabiscuit (5.32%, 5.24%, and 4.51%DM). The total RUP (TRUP) was higher for MH than MIR and DH in both varieties (P < 0.001). The intestinal digestible feed protein was similar between the varieties (P = 0.308); however, DH and MH reduced the value, while MIR increased it compared to the control (P = 0.010) for both varieties. The results from the ATR-FTIR analysis showed an interaction between varieties and heat processing in the protein secondary structures α-helix (P = 0.003), β-sheet (P = 0.031), and their ratio (P = 0.030). Moreover, in the multivariate analyses, no independent groups were observed between the raw and the three heat processing methods in the protein-related molecular structure of oats. In conclusion, heat processing altered the protein subfractions, the TRDP, and TRUP, affecting intestinal protein digestibility. The ATR-FTIR spectroscopy with univariate molecular analysis showed heat processing-induced molecular structure changes in the protein-related spectral profiles in CDC Nasser and CDC Seabiscuit.

Multi-Analytical Approach to Evaluate Elements and Chemical Alterations in Pteris vittata Plants Exposed to Arsenic

The aim of this study was the development of a new multi-analytical approach to evaluate chemical alterations and differences in the element content in relation to arsenic (As) in the As hyperaccumulator fern P. vittata. P. vittata plants were grown on two natural As-rich soils with either high or moderate As (750 and 58 mg/kg). Dried samples from plant tissues were then analysed by means of micro-energy dispersive X-ray fluorescence spectrometry (μ-XRF), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and hyperspectral imaging (HSI) with a multivariate approach. The As and micro- and macronutrients content was evaluated by μ-XRF and a significant correlation between As, potassium (K), iron (Fe), calcium (Ca) and manganese (Mn) contents were found at both moderate and high As levels. The same samples were then analysed by ATR-FTIR spectroscopy and HSI (SWIR range, 1000–2500 nm). Interestingly, by FTIR analysis it was found that the main differences between the control and the As-contaminated samples are due to the intensity of the absorption band related to polysaccharides (i.e., cellulose, hemicellulose and pectin), lignin, lipid and amide groups. The same chemical alterations were detected by an HSI analysis and all the FTIR and HSI data were validated by a PCA analysis. These results suggest a possible complexation of As ions with the amide group. Moreover, the proposed μ-XRF, HSI and ATR-FTIR combining approach could be a promising strategy to monitor in-field phytoremediation approaches by directly controlling the As content in plants.

Analysis of Red-Lipstick using Attenuated Total Reflectance Fourier -Transform Infrared Spectroscopy

In the context of criminal investigations, specifically those involving crimes against women, it is common to findcosmetics such as lipstick, eyeliner, eye shadow, nail polish, and vermilion as potential evidence. These cosmeticitems are frequently used in everyday life and can be easily transferred onto clothing, drinking cups, handkerchiefs,tissue papers, cigarette butts, and various surfaces through contact during the commission of a crime.This paper presents the application of Attenuated Total Reflectance Fourier-Transform Infrared Spectroscopy(ATR-FTIR) as a powerful technique for investigating the chemical composition of red lipstick. By employingATR-FTIR spectroscopy, the researchers have analyzed the compounds present in the red lipstick. This spectralanalysis provides valuable chemical information that can aid in criminal investigations and forensic analysis. Theresult demonstrates that ATR-FTIR spectroscopy is a highly effective tool for examining the chemical compositionof cosmetics. The technique enables the identification of key compounds and functional groups present in thesamples, allowing for differentiation and characterization of various cosmetic products. Overall, this paperhighlights the significance of ATR-FTIR spectroscopy as a valuable tool in forensic analysis and criminalinvestigations involving cosmetic product (red lipstick). The chemical information obtained through this techniqueprovides crucial evidence that can contribute to the understanding and resolution of cosmetic-related crimes.

Classification and Prediction by Pigment Content in Lettuce (Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy.

Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.

Exploring the Potential of Grape Pomace Extract to Inhibit Thermo-Oxidative Degradation of Sunflower Oil: From Routine Tests to ATR-FTIR Spectroscopy.

Exploring new sources of natural antioxidants is of great interest to edible oil producers, in line with the toxicological problems generated by the use of synthetic antioxidants. This study assesses the potential of lyophilized Pinot Noir grape pomace extract (GPE) to enhance the sunflower oil stability against thermo-oxidative damage compared to BHT during a prolonged exposure to convective heat at 185 °C. Oil thermo-oxidation was monitored based on specific indices such as peroxide value (PV), para-anisidine value (p-AV), inhibition of oil oxidation (IO), total oxidation (TOTOX) value, conjugated dienes and trienes (CDs, CTs), but also by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), where absorbance ratios A 3009 cm-1/A 2922 cm-1 (RI), A 3009 cm-1/A 2853 cm-1 (RII), A 3009 cm-1/A 1744 cm-1 (RIII) and RIV = A 1744 cm-1/A 2922 cm-1 (RIV) were investigated. GPE showed a significant inhibitory effect on oil thermo-oxidation and this response was concentration-dependent. Substantial decreases in the investigated indices, compared to the control without added antioxidants, were obtained after 4 h and 8 h of heat exposure of the 800 ppm GPE sample: PV (47%; 42%), p-AV (38%; 33%), IO (54%; 46%), TOTOX (41%; 37%), CDs (46%; 39%), CTs (44%; 29%). Oil exposure to heat resulted in changes in RI-RIV attributed to the reduction in the degree of unsaturation, in response to primary and secondary lipid oxidation. FTIR spectroscopy can be used to differentiate untreated and heat-treated oils based on the absorbance ratios. An inhibitory effect close to that of BHT was achieved by 500 ppm GPE, while a dose of 800 ppm provided greater protection against thermo-oxidation. Our results promote GPE as a natural additive to limit the thermo-oxidative damage of plant oils.

Investigation of Glycine Polymorphic Transformation by In Situ ATR-FTIR and FT-Raman Spectroscopy

The solution-mediated phase transformation of α-form to γ-form glycine, including dissolution of metastable α-form, nucleation, and growth of stable γ-form during polymorphic transformation, was investigated using in situ attenuated total-reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and Fourier transform Raman spectroscopy (FT-Raman). The mechanistic influence of operating parameters such as agitation speed, crystallization temperature, α-form seed concentration, and NaCl concentration on polymorphic phase transformation was examined. When the agitation speed, crystallization temperature, and NaCl concentration were increased, the polymorphic transformation process was improved due to the promotion of nucleation and growth of stable γ-form, in addition to the promotion of dissolution of metastable α-form. Moreover, the time to induce γ-form nucleation and complete conversion of α-form to γ-form was also reduced with increasing α-form seed concentration.

Polymer composition analysis of plastic debris ingested by loggerhead turtles (Caretta caretta) in Southern Tyrrhenian Sea through ATR-FTIR spectroscopy

The ingestion of anthropogenic plastic debris by marine wildlife is widespread in the Mediterranean Sea. The endangered status (in the IUCN Red List) of Loggerhead turtle (Caretta caretta, Linnaeus, 1758) is a consequence of its vulnerability. In this study, macro-/meso-plastics (5–170 mm) collected from faeces of twelve loggerhead turtles rescued (live) in the Aeolian Archipelago (Southern Tyrrhenian Sea, Italy) were analyzed by size, weight, shape, color and polymer type through Attenuated Total Reflectance Fourier-Transform Infrared Spectroscopy (ATR-FTIR). The defecation rate during hospitalization (7–14 days) varied among turtles (from 0.08 to 0.58). The mean number of plastic expulsions (2.7 ± 1.8 items for turtle) was higher during the 5th day of hospitalization (Kruskal-Wallis test, P = 0.01). However, the mean number of plastic-like items defecated during the common days of hospitalization did not vary among turtles (Kruskal-Wallis test, P > 0.05). All turtles were found to have ingested plastic. A total of 114 debris items were recovered from their faeces, 113 of which were identified as plastic. Their color was mostly white-transparent (64.9%) and light (19.3%). Shape was mainly fragments (52.6%), sheets (38.6%), followed by nylon, net-fragments, elastic plastic, foamed plastic and industrial granules (8.8%). Meso-plastics (5–25 mm) represented 72% of the total number of debris and were found more frequently in turtle with Curved Carapace Length (CCL) ≤ 60 cm (CCL = 30–60 cm, n = 5) than those with CCL >60 cm (CCL = 60–71 cm, n = 7). Plastic items were composed mainly of polyethylene (48.2%) and polypropylene (34.2%). Polypropylene (R2 = 0.95, P < 0.001) and polyisoprene (R2 = 0.45, P = 0.017) were more common in meso-plastics while polyethylene (R2 = 0.44, P < 0.01) in macro-plastics. Finally, high-density polyethylene, polyvinyl chloride, polyamide and polyurethane were also found in some turtles. This study reveals high spreads of plastic contamination in faeces of both turtles with CCL ≤60 cm and CCL >60 cm, particularly vulnerable to the increasing quantity of floating plastic into their foraging sites highlighting the need of further research to associate debris ingestion with turtle diet and their size.

Characterization of grafting properties of ABS latexes: ATR-FTIR vs NMR spectroscopy

Acrylonitrile–Butadiene–Styrene (ABS) polymers have a complex microstructure which is formed during the grafting reaction of a polybutadiene seed latex. During the reaction, some styrene-acrylonitrile (SAN) chains are grafted onto the polybutadiene (PB) backbone chains, and this grafting is critical to achieve effective dispersion and compatibility of both phases. Therefore, accurate characterization of grafting properties helps understanding the polymerization mechanism as well as the final properties of the ABS polymer and its applications properties. In this work, the grafting properties of ABS latexes were determined by separation of the soluble and insoluble phase of the polymer, followed by the characterization of these phases using analytical techniques. Phase separation was carried out dispersing the latex in acetone followed by ultracentrifugation. The soluble and insoluble polymer phases were for the first time analysed by NMR spectroscopy to obtain the corresponding fractions of SAN and PB in each phase. The soluble fraction was analysed by liquid-state 1H Nuclear Magnetic Resonance (NMR) spectroscopy, whereas the insoluble fraction was analysed by solid-state 13C NMR spectroscopy. Moreover, both phases were analysed by Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR), and the results obtained are compared and discussed in this article. We found that the most accurate grafting properties are achieved by analysing the composition of the soluble and insoluble fraction by NMR spectroscopy.

Determination of LC-PUFAs and Protein Content in Premature infant formula powders by ATR-FTIR Spectroscopy and Chemometrics

Premature infant formula powder (PIFP) contains essential nutrients, such as protein and LC-PUFAs in which inadequate quantities of them may restrict the development of premature infants. The aim of this study was to propose a quantitative analysis for evaluating these nutrients in PIFP. To achieve this purpose, an integrated methodology was developed by combining attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) with the chemometrics. ATR-FTIR was utilized to obtain the PIFP spectra in the wavenumber range of 4000-400 cm-1. Principal component analysis (PCA) was employed to perform exploratory analysis of the spectra. Partial least square regression (PLSR) and principal component regression (PCR) models were established for normal spectra, first, and second derivatives to quantitatively determine the protein and LC-PUFAs. The results showed that the PLSR model provided a better prediction than the PCR model with the coefficient of determinations (R2) of 0.992 and 0.983 for training and validation, respectively. It also offers the best prediction for the PLSR model of protein considering the parameters such as R2 (normal spectra) (Cal: 0.995, Val: 0.981). The results revealed that a combination of the ATR-FTIR spectroscopy and the chemometrics models creates a rapid, inexpensive, and non-destructive method for the quantitative analysis of PIFP.

Spatially-resolved chemical and mechanical examination of adhesive mixing efficiency with ATR-IR and Martens hardness

In this work, a new combined approach to investigate the quality of mixed and cured 2-component epoxy adhesives will be proposed. The spatially-resolved attenuated total reflectance fourier transform infrared spectroscopy (ATR-FTIR) on a microscale and Martens hardness were used to investigate the chemical and mechanical properties respectively the performance of mixed 2-component epoxy adhesives. Therefore, the measurement principle was proved on samples with different proportion of the adhesive hardener. The results show that the difference in hardener proportion has a significant effect on the measurable epoxy concentration and the Martens hardness. In a second step the testing methods were applied to differently mixed adhesive samples to investigate the mixing homogeneity. The mixing methods used were hand mixing, a static mixer, an asymmetrical dual centrifuge and a novel mixing method based on power ultrasound. With a spatially-resolved representation of the ATR-FTIR and Martens hardness test results it is possible to distinguish between different mixed samples and to analyze the mixing quality. It was shown that the resolution and the sensitivity of the ATR-FTIR spectroscopy and the Martens hardness method allow to evaluate the mixing homogeneity of the used 2-component epoxy adhesives. Furthermore, the correlation of the non-destructive ATR-FTIR and hardness test results with destructive lap shear test of adhesive joints was investigated. By this, it was shown, that also the mechanical properties of adhesive joints are affected by the proportion of hardener and the used mixing method and correspond with the ATR-FTIR spectroscopy and Martens hardness.

Detection of Human Cholangiocarcinoma Markers in Serum Using Infrared Spectroscopy.

Simple SummaryCholangiocarcinoma is a form of liver cancer that is found, predominantly, in Thailand. Due to the non-specific symptoms and laboratory investigation, it is difficult to rule out cholangiocarcinoma from other liver conditions. Here, we demonstrate the development of a diagnostic tool for cholangiocarcinoma, based on the ATR-FTIR analyses of sera, coupled with multivariate analyses and machine learning tools to obtain a better specificity. The innovative approach that shows highly promising results for this otherwise difficult to diagnose cancer.Cholangiocarcinoma (CCA) is a malignancy of the bile duct epithelium. Opisthorchis viverrini infection is a known high-risk factor for CCA and in found, predominantly, in Northeast Thailand. The silent disease development and ineffective diagnosis have led to late-stage detection and reduction in the survival rate. Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) is currently being explored as a diagnostic tool in medicine. In this study, we apply ATR-FTIR to discriminate CCA sera from hepatocellular carcinoma (HCC), biliary disease (BD) and healthy donors using a multivariate analysis. Spectral markers differing from healthy ones are observed in the collagen band at 1284, 1339 and 1035 cm−1, the phosphate band () at 1073 cm−1, the polysaccharides band at 1152 cm−1 and 1747 cm−1 of lipid ester carbonyl. A Principal Component Analysis (PCA) shows discrimination between CCA and healthy sera using the 1400–1000 cm−1 region and the combined 1800—1700 + 1400–1000 cm−1 region. Partial Least Square-Discriminant Analysis (PLS-DA) scores plots in four of five regions investigated, namely, the 1400–1000 cm−1, 1800–1000 cm−1, 3000–2800 + 1800–1000 cm−1 and 1800–1700 + 1400–1000 cm−1 regions, show discrimination between sera from CCA and healthy volunteers. It was not possible to separate CCA from HCC and BD by PCA and PLS-DA. CCA spectral modelling is established using the PLS-DA, Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN). The best model is the NN, which achieved a sensitivity of 80–100% and a specificity between 83 and 100% for CCA, depending on the spectral window used to model the spectra. This study demonstrates the potential of ATR-FTIR spectroscopy and spectral modelling as an additional tool to discriminate CCA from other conditions.

Comparison of the Growth and Thermal Properties of Nonwoven Polymers after Atomic Layer Deposition and Vapor Phase Infiltration

The growth mechanism of Atomic Layer Deposition (ALD) on polymeric surfaces differs from growth on inorganic solid substrates, such as silicon wafer or glass. In this paper, we report the growth experiments of Al2O3 and ZnO on nonwoven poly-L-lactic acid (PLLA), polyethersulphone (PES) and cellulose acetate (CA) fibres. Material growth in both ALD and infiltration mode was studied. The structures were examined with a scanning electron microscope (SEM), scanning transmission electron microscope (STEM), attenuated total reflectance-fourier-transform infrared spectroscopy (ATR-FTIR) and 27Al nuclear magnetic resonance (NMR). Furthermore, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) analysis were used to explore the effect of ALD deposition on the thermal properties of the CA polymer. According to the SEM, STEM and ATR-FTIR analysis, the growth of Al2O3 was more uniform than ZnO on each of the polymers studied. In addition, according to ATR-FTIR spectroscopy, the infiltration resulted in interactions between the polymers and the ALD precursors. Thermal analysis (TGA/DSC) revealed a slower depolymerization process and better thermal resistance upon heating both in ALD-coated and infiltrated fibres, more pronounced on the latter type of structures, as seen from smaller endothermic peaks on TA.

Application of ATR-FTIR spectroscopy and chemometrics for the discrimination of human bone remains from different archaeological sites in Turkey.

Examining diagenetic parameters such as the organic carbonate contents and the crystallinity of bone apatite quantify the post-mortem alteration of bone. Burial conditions are one of the factors that can influence the diagenesis process. We studied the changes to the organic and mineral components and crystallinity of human bone remains from five Medieval sites in Turkey: Hakemi Use, Komana, İznik, Oluz Höyük and Tasmasor using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) and principal component analysis (PCA). Analysis of spectral band ratios related to organic and mineral components of bone demonstrated differences in the molecular content in the skeletal remains from the five sites. In order to examine the degree of carbonation of a phosphate matrix, curve-fitting procedures were applied to the carbonate band. We found that the infrared crystallinity index appears to not be sensitive to carbonate content at room temperature for the bone remains studied here. The recrystallization process in bone remains behaved differently among the archaeological sites. The results demonstrate that the burial environments differently affect the organic and mineral components of archaeological bone remains.

Preparation of a Novel Flame Retardant Formulation for Cotton Fabric.

A novel halogen-free flame-retardant formulation was prepared and coated onto cotton fabrics. The structure of phosphorus compounds in the system was characterized by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and nuclear magnetic resonance spectroscopy (1H-NMR). Results from the ATR-FTIR spectroscopy, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX) analyses presented that the flame retardant was coated successfully onto a cotton surface. We investigated the thermal stability and fire-retardant behaviors of cotton fabrics using thermal gravimetric analysis (TGA) and the vertical flame test. We also discuss the mechanism of flame retardance of coated cotton fabrics.

Rapid and simultaneous determination of physical and chemical properties of asphalt by ATR-FTIR spectroscopy combined with a novel calibration-free method

Determining wax content, softening point, and penetration of asphalt products by traditional methods is too time-consuming to meet the instant need in practice. Although infrared spectroscopy combined with multivariate calibration may realize rapid determination of asphalt, its modeling and maintenance are very labor-intensive and challenging. In this paper, a novel method combining attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) with a fast algorithm of spectrum representation (SR) was proposed to rapidly and expediently determine the properties. The proposed method achieves calibration-free, effectively overcoming the drawback of the modeling methods. The feasibility of the new method was demonstrated using 431 asphalt samples produced by different kinds of crude oil. The root mean standard error of prediction (RMSEP) of wax content, softening point, and penetration by SR method are 0.14%, 0.55 °C and 4.71 (0.1 mm), respectively. Each of them is below the reproducibility of corresponding standard test method. Moreover, the new method is compared with other two commonly used methods, partial least squares regression (PLS) and local modeling by densification (LMD), in detail. The results show that the new method not only can solve the disadvantages of PLS modeling effectively, but also is remarkably superior to LMD method, in repeatability and speed, and robustness for predicting the sample in the region with lower density and unreasonable distribution of the samples in data bank.

Identification of asphalt fingerprints based on ATR-FTIR spectroscopy and principal component-linear discriminant analysis

The objective of this study is to develop a method for rapidly identifying oil sources and controlling the quality of asphalt based on infrared spectroscopy. An identification method of asphalt fingerprint was established by combining the attenuated total reflectance fourier transform infrared spectroscopy (ATR-FTIR) and the chemometric methods. This method can be utilised to distinguish, both rapidly and correctly, between different sources of the asphalt samples, identify categories therein, and control the quality of asphalt. Further more, the discriminant function was established, which can be used for distinguishing between asphalt samples from these three oil sources with an accuracy of up to 96.2%. This research results provide a scientific basis for reasonable selection, quality monitoring, and guarantee of origin of asphalts.

Discrimination of intact almonds according to their bitterness and prediction of amygdalin concentration by Fourier transform infrared spectroscopy

Intact almond kernels (N = 360, half sweet and half bitter) were analyzed using attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) for the prediction of amygdalin concentration and to classify them according to their bitterness. Amygdalin concentrations for sweet and bitter almonds, determined by high performance liquid chromatography, were between 0.7–350 and 15,000-50,000 mg kg−1, respectively. Concentrations were successfully predicted by applying partial least squares (PLS) to the pre-treated spectral data with R2p of 0.951 and RMSEP of 0.398. Additionally, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and PLS-DA models were constructed to classify samples according to their bitterness. All three models provided a satisfactory discrimination of almonds into sweet and bitter categories, providing overall accuracy values of 83.3%, 86.1% and 98.6%, respectively. The results indicate the potential of ATR-FTIR spectroscopy for the reliable, easy and fast prediction of amygdalin concentration, and for almond classification according to their bitterness.