Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy Research Articles

Enhancing Antibacterial and Water-Repellent Properties for the Production of High-Performance Fabrics in Home Textiles

In this research, we aimed to enhance the antibacterial activity and water repellency functionalities of single-layer 100% cotton fabrics on both the face and back sides, utilizing conventional methods. Unlike conventional approaches that often rely on lamination or coating techniques to imbue fabric surfaces with distinct properties, our study pursued a cost-effective strategy using traditional textile finishing methods. By integrating double functionality into cotton fabric, we aimed to circumvent the need for additional equipment and high initial costs associated with other techniques. To achieve this, we improved the antibacterial activity of the fabrics through the incorporation of silver ions in the pad-dry process. Simultaneously, water repellency functionality was introduced through a printing application, where the printing paste, devoid of dyestuff, was combined with functional agents and applied using rotary printing to the fabric’s backside. Following these finishing applications, we conducted comprehensive tests encompassing washing durability, antibacterial efficacy, water repellency, mechanical strength, and fastness properties of the multifunctional fabrics. Characterization tests, including Fourier transform infrared attenuated total reflectance (FTIR-ATR) and scanning electron microscope analyses (SEM-EDX), were performed. The outcome of our study showcased the successful enhancement of double-functional cotton fabric, featuring water repellency and antibacterial efficiency on distinct sides. Furthermore, the results indicated that these functionalities endured up to 20 washing cycles, with fastness and performance tests consistently achieving peak levels.

Cyclodextrin-Assisted Surface-Enhanced Photochromic Phenomena of Tungsten(VI) Oxide Nanoparticles for Label-Free Colorimetric Detection of Phenylalanine.

Herein are presented the results of experiments designed to evaluate the effectiveness of host-guest interactions in improving the sensitivity of colorimetric detection based on surface-enhanced photochromic phenomena of tungsten(VI) oxide (WO3) nanocolloid particles. The UV-induced photochromic coloration of WO3 nanocolloid particles in the presence of aromatic α-amino acid (AA), l-phenylalanine (Phe) or l-2-phenylglycine (Phg), and heptakis(2,3,6-tri-O-methyl)-β-cyclodextrin (TMβCDx) in an aqueous system was investigated using UV-vis absorption spectrometry. The characteristics of the adsorption modes and configurations of AAs on the WO3 surface have also been identified by using a combination of adsorption isotherm analysis and attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR). A distinct linear relationship was observed between the concentration of AAs adsorbed on the WO3 nanocolloid particles and the initial photochromic coloration rate in the corresponding UV-irradiated colloidal WO3 in aqueous media, indicating that a simple and sensitive quantification of AAs can be achieved from UV-induced WO3 photochromic coloration without any complicated preprocessing. The proposed colorimetric assay in the Phe/TMβCDx/WO3 ternary aqueous system had a linear range of 1 × 10-8 to 1 × 10-4 mol dm-3 for Phe detection, with a limit of detection of 8.3 × 10-9 mol dm-3. The combined results from UV-vis absorption, ATR-FTIR, and adsorption isotherm experiments conclusively indicated that the TMβCDx-complexed Phe molecules in the Phe/TMβCDx/WO3 ternary aqueous system are preferentially and strongly inner-sphere adsorbed on the WO3 surface, resulting in a more significant surface-enhanced photochromic phenomenon. The findings in this study provided intriguing insights into the design and development of the "label-free" colorimetric assay system based on the surface-enhanced photochromic phenomenon of the WO3 nanocolloid probe.

Design of Silk Fabric Surface with Enhanced Antiseptic Properties through Graft Modification and Iodophor Integration

AbstractTo improve the hydrophobic character of silk and impart iodine complex ion adsorption, the surface of woven silk fabric is enriched by different functional groups through the grafting of N‐vinylpyrrolidone (NVP), ethylene glycol dimethacrylate (EGDMA), and their mixtures (NVP–EGDMA). The dominant fibrous texture of silk enables an efficient graft copolymerization in terms of high grafting yield (>30%) after applying a mild degumming process. The structural and morphological alterations in the silk surface are evidenced with attenuated total reflectance‐fourier transform infrared spectroscopy (ATR‐FTIR), X‐ray diffraction (XRD), and scanning electron microscopy (SEM) techniques. The imparted hydrophilicity property of the samples is revealed by contact angle–wetting time measurements. The usability of the graft‐modified samples as an iodophor polymeric substrate is then examined by treatment with I2/KI solutions, and the results are monitored using UV–vis spectroscopy. It is obtained that the NVP–EGDMA mixture‐grafted sample exhibits satisfactorily high iodine adsorption compared to untreated and NVP‐grafted silk samples, such as 0.05 g per 1 g fabric with high antibacterial activity against various pathogens, including methicillin‐resistant Staphylococcus aureus (MRSA) responsible for hospital‐acquired infections. This finding evidences its potential as an alternative antiseptic tissue for biomedical applications.

Chitosan-supported calcium hydroxide hybrid material as new, efficient, and recyclable catalyst for biodiesel production

In this work, a novel supported catalyst was prepared starting from calcium chloride and chitosan flakes using a very mild approach in an aqueous medium without final calcination at high temperatures. The as-prepared catalyst was fully characterized by thermogravimetric analysis (TGA), attenuated total reflectance Fourier transform infrared (ATR-FTIR), and X-ray photoelectron (XPS) spectroscopies, transmission and scanning electron microscopies (TEM, SEM), Energy Dispersive X-ray Spectroscopy (EDS), demonstrating that it consists of calcium hydroxide particles of about 200 nm supported on chitosan micrometric structures. The most crucial parameters in the transesterification process were investigated. A methanol/oil ratio of 6:1, a reaction time of 6 h, and a temperature of 60 °C were found to lead to complete conversion. A reaction on a gram scale using waste oil as a starting material was also tested, and excellent results were achieved. Moreover, the catalyst proved to be very robust, since even after the 10th recycle, the conversion rate remained at around 90 %. Spectroscopic analyses showed minimal leaching of material without modification of chemical composition. The kinetic behavior (activation energy, Ea) of the catalyst was also studied, which resulted in similar outcomes to Ca-based systems present in the literature, but without the need for costly preparations and with superior recycling resistance. An Ea value of 63.25 kJ·mol−1 was found, which agrees with data reported in the literature.

Detection of meloxicam residues in milk using ATR- FTIR spectroscopy coupled with chemometrics

Meloxicam (MELX), a non-steroidal anti-inflammatory drug (NSAID), is frequently administered to cattle, poses a risk to human health when it is found in milk. Regulatory bodies of EU, Canada, China and India have prescribed maximum residue limits for MELX in milk as 15, 35 and 15 and 10 μg/kg, respectively. This study explores the use of attenuated total reflectance- Fourier transform infrared (ATR-FTIR) spectroscopy in conjunction with chemometric techniques to detect MELX residues in milk. Samples of pure milk and milk samples spiked with MELX at different concentrations ranging from 1 to 50 μg/kg were analysed. ATR-FTIR measurements were taken in the wavenumber range of 4000–400 cm−1 for all the samples. Principal component analysis (PCA) was applied in the regions of 2940–2840 and 1765–1730 cm−1 to differentiate clusters of milk sample with those of MELX and milk spiked with MELX samples. The coefficient of determination (R2) values for calibration and validation sets in wavenumber regions of 2940–2840 and 1765–1730 cm−1 were >0.92 and > 0.94, respectively for the developed partial least squares regression (PLSR) models. All the samples showed 100% classification efficiency using the soft independent modeling of class analogy (SIMCA) technique. The study concluded that the presence of MELX residues can be detected in milk even at 1 μg/kg level using ATR-FTIR and chemometrics. Results obtained from developed FTIR based chemometric method were in agreement with those obtained from LC-MS/MS method and have the potential for application in screening milk samples for the presence of MELX.

Advancements towards the development artificial intelligence for sensory analysis: Integrating pattern recognition and signal processing in ATR-FTIR analysis of spirits

Spirits have been gaining acceptance by international consumers due to its distinct organoleptic profile, which provide each beverage with a characteristic sensation of the product (taste, smell, and color). However, it is important to establish differences by an analytical technique supported by artificial intelligence to demonstrate with mathematical solutions rather of being subjective such as human senses. To reduce the uncertainty associated to the sensory analysis, the present study shows as strategy the recognition of patterns of ATR-FTIR spectra of a variety of distilled beverages: Tequila 100% agave (n = 20), Mezcal (n = 20), Raicilla (n = 20), Aguardiente (n = 20), and Vodka (n = 20). All ATR-FTIR spectra present seven characteristic bands associated with methanol, ethanol, isoamyl alcohol, butanol, ethyl acetate, acetaldehyde, and furfural in beverages. Based on the analysis of the peak signals obtained from the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectra, the implementation of an Artificial Neural Network was proposed with the objective to classify each alcoholic beverage in its corresponding groups. Results showed that the artificial intelligence algorithm proposed permits to correct classify 100% of the samples being a great alternative strategy being innovative, fast, and of low-cost strengthening food quality analyses by machine learning decision-making.

Surface properties of schwertmannite with different sulfate contents and its effect on Cr(VI) adsorption

Sulfate is a critical component of schwertmannite. However, the sulfate content of schwertmannite decreases with the increase in AMD-polluted river pH value, and its impact on the surface properties and Cr(VI) adsorption of schwertmannite remains unclear. To address this issue, different methods, including batch adsorption, N2 physisorption, scanning electron microscopy (SEM), potentiometric titration, X-ray photoelectron spectroscopy (XPS), attenuated total reflectance–Fourier transform infrared spectroscopy (ATR-FTIR) in combination with two-dimensional correlation spectroscopy (2D-COS) analysis, and surface complexation modeling (SCM) were employed. Our results showed that sulfate existed as both non-protonated bidentate-binuclear complexes and non-protonated outer-sphere complexes in schwertmannite. The surface morphology and particle size of schwertmannite were unaffected by the sulfate content. However, a decrease in sulfate content resulted in a drop in the negative charge on the surface of schwertmannite, which is conducive to particle aggregation. An increase in pH, on the other hand, raised the negative charge and encouraged the dispersion of the particles. And then, reducing the sulfate content of schwertmannite led to a decrease in acid-base buffer and Cr(VI) adsorption capacity of schwertmannite. The adsorption process of Cr(VI) by schwertmannite can be divided into two stages. In the first stage, Cr(VI) was adsorbed primarily through anion exchange reactions with the sulfate; however, the contribution of surface complexation gradually increased as the initial sulfate content decreased because of a reduced likelihood of anion exchange and an increase in the specific surface area of schwertmannite. Additionally, the behavior of sulfate and Cr(VI) in this stage could be predicted based on the initial sulfate content of schwertmannite. In the second stage, surface complexation with hydroxyl sites became the predominant mechanism of adsorption, which still exhibited great potential at higher Cr(VI) concentrations. Finally, the aforementioned hypothesis was used to establish a CD-MUSIC model, and the fitting results demonstrated that the density and Cr(VI) adsorption capacity of the sulfate site dropped as the sulfate content decreased, while the hydroxyl site was less impacted. The findings have provided insights into further understanding the properties of schwertmannite and its adsorption of Cr(VI).

Controlled dual activity of an organometallic antibiotic through micelle formulation

Sodium Lauryl Sulphate (SLS) and Cetyltrimethylammonium bromide (CTAB) were employed to create micelles (SLS@CIPTIN and CTAB@CIPTIN) of the organometallic antibiotic Ph2Sn(CIP)2 (CIPTIN) (where HCIP= ciprofloxacin). The characterization of the micelles was conducted in the solid-state using melting point, Attenuated Total Reflectance-Fourier Transform Infra-Red (ATR-FTIR), 119Sn Mössbauer and X-ray Fluorescence (XRF) spectroscopies, and Thermogravimetry/Differential Thermal Analysis (TG-DTA), while in solution, by Ultra violet-Visible (UV–Vis) and Nucleus Magnetic Resonance (1HNMR ) spectroscopic techniques were employed. The compounds were tested for their antiproliferative activity against human breast adenocarcinoma cells: MCF-7 (hormone depended (HD)) and MDA-MB-231 (hormone independent (HI)). The in vitro and in vivo non-genotoxicity was confirmed with micronucleus (MN) and Artemia salina assay. The antiproliferative mechanism of action of SLS@CIPTIN and CTAB@CIPTIN was studied by cell morphology and cell cycle arrest. The antibacterial activity was evaluated using Minimum Inhibitory Concentration (MIC), Minimum Bactericidal Concentration (MBC) and Inhibition Zones (IZ) against Minimum Bactericidal Concentration (MBC) against Gram-negative strains P. aeruginosa, E. coli, as well as Gram-positive strains S. epidermidis and S. aureus. The results indicate that both the investigated CIPTIN and its supramolecular assembly (SLS@CIPTIN) exhibit activity. Therefore, the encapsulation of antimicrobial compounds can indeed be regarded as an attractive strategy for the development of new dual-active agents. These agents offer concurrent anticancer and antibacterial attributes without inducing toxicity.

Exploring untapped bacterial communities and potential polypropylene-degrading enzymes from mangrove sediment through metagenomics analysis.

The versatility of plastic has resulted in huge amounts being consumed annually. Mismanagement of post-consumption plastic material has led to plastic waste pollution. Biodegradation of plastic by microorganisms has emerged as a potential solution to this problem. Therefore, this study aimed to investigate the microbial communities involved in the biodegradation of polypropylene (PP). Mangrove soil was enriched with virgin PP sheets or chemically pretreated PP comparing between 2 and 4 months enrichment to promote the growth of bacteria involved in PP biodegradation. The diversity of the resulting microbial communities was accessed through 16S metagenomic sequencing. The results indicated that Xanthomonadaceae, unclassified Gaiellales, and Nocardioidaceae were promoted during the enrichment. Additionally, shotgun metagenomics was used to investigate enzymes involved in plastic biodegradation. The results revealed the presence of various putative plastic-degrading enzymes in the mangrove soil, including alcohol dehydrogenase, aldehyde dehydrogenase, and alkane hydroxylase. The degradation of PP plastic was determined using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR), Scanning Electron Microscopy (SEM), and Water Contact Angle measurements. The FTIR spectra showed a reduced peak intensity of enriched and pretreated PP compared to the control. SEM images revealed the presence of bacterial biofilms as well as cracks on the PP surface. Corresponding to the FTIR and SEM analysis, the water contact angle measurement indicated a decrease in the hydrophobicity of PP and pretreated PP surface during the enrichment.

ATR-FTIR spectroscopy and Machine learning for sustainable wood sourcing and species Identification: Applications to wood forensics

Wood forensics, a vital tool in addressing global concerns surrounding illegal logging and the authenticity of wood products, plays a pivotal role in promoting sustainable forestry practices. The integration of Attenuated Total Reflectance Fourier-Transform Infrared (ATR-FTIR) spectroscopy and machine learning presents a potent approach to streamline identification processes and enhance responsible wood sourcing. ATR-FTIR facilitates the detailed characterization of wood components by analyzing spectral data, revealing unique chemical signatures associated with different wood species. This study focuses on Eucalyptus, Dalbergia, and Populus wood species in India, revealing common peaks related to cellulose, flavones, hemicellulose, and lignin constituents. Isolation Forest (iForest) identifies outliers, subsequently removed for supervised modelling, while Principal Component Analysis (PCA) reduces dimensionality effectively. Tree-based supervised machine learning algorithms, including Decision Tree, Extra Tree, Random Forest, and CatBoost classifiers, enhances the accuracy of wood species discrimination by deciphering complex patterns within ATR-FTIR spectra. Further, evaluation through various performance metrics supports the comparative analysis. The outcomes of this study hold significant value for law enforcement agencies in combating illegal logging and timber trade, and potential applications in quality control for timber industries. This research substantiates its real-world applicability, marking a noteworthy advancement in the evolving field of wood forensics.

Novel sulfonated calixarenes derived crosslinker for reinforcing and toughening collagen: Relevance between structure and properties

AbstractCollagen is widely used in the development of new functional materials due to its high structural stability, good biocompatibility, and excellent hydrophilicity, but the collagen protein denatures easily, which makes its thermal stability and mechanical strength difficult to reach some of the parameters required in the application. To solve this, protein crosslinking is an effective way to better use collagen. In this paper, we compared the thermal stability and morphological stability of three kinds of sulfonated calixarenes crosslinked collagen before and after the treatment. The results showed that sulfonated calixarenes could significantly improve the thermal stability and morphological stability of dermal collagen. The reinforcing and toughening mechanism of the sulfonated calixarenes was revealed by scanning electron microscopy (SEM), infrared spectroscopy (FT‐IR), attenuated total reflection‐Fourier transform infrared spectroscopy (ATR‐FTIR), ultraviolet–visible–near‐infrared (UV–vis–NIR) spectrometer, X‐ray diffraction detection (XRD), atomic force microscopy (AFM), and circular dichroism (CD) detection. The results showed that sulfonated calixarenes were evenly distributed in collagen fibers and formed hydrogen bonds or electrovalent bonds with amino groups, peptide groups, or amide groups in collagen to improve the thermal stability of the collagen. Three kinds of sulfonated calixarenes enter collagen fiber to support the collagen, increase the three‐dimensional weave angle of collagen, and improve the physical and mechanical properties of collagen, thereby toughening and enhancing the collagen. Based on the above studies, the reinforcing and toughening mechanism of the sulfonated calixarenes crosslinked collagen was established. Confirmed the existence of hydrogen bonds between the three sulfonated calixarenes and collagen, and the nonchemical bond energy of the system was proportional to the molecular size of sulfonated calixarenes.

Structural Analysis of Heat-Calcined Modified Sewage Sludge From WWTP in Gas Complex For Environmental Risk Assessment

Thermal processing of sewage sludge (SS) wastewater treatment plant WWTP has received increasing attention in recent years. Heat calcination valorise the carbon rich organic fraction of SS, while effectively reducing SS volume. The refractory metals (thermally stable, i.e. Zn, Cu and Cr) are less volatile at different temperatures at 200 and 400 °C of thermal processes and they are enriched in the residues. To gain insights into mineralogical analysis, physicochemical, microstructure properties and heavy metals ( HMs ) were characterized by proximate, ultimate field emission x-ray fluorescence ( XRF ), scanning electron microscopy ( SEM ), attenuated total reflectance-Fourier transform infrared ( ATR-FTIR ) spectroscopy, x-ray diffraction ( XRD ) and UV-vis spectrophotometer. Such analysis plays a crucial role in the effective management of WWTPs and in evaluating the environmental risks associated with the disposal of wastewater sludge’s. It aids in assessing the potential value of these sludge’s as fertilizer, considering factors such as the presence of potash and iron, for agricultural purposes, or as building materials, considering the presence of silica and calcium oxide. The assessment is carried out utilizing X-ray fluorescence (XRF) technology. Furthermore, a case study is included to illustrate typical situations in a WWTP, emphasizing the main challenges and limitations linked with ships, as well as wastewater management and disposal. Additionally, the specific scenario of gas complex GP1/Z is scrutinized to suggest a wastewater treatment solution, which involves evaluating potential locations for the treatment plant and determining the appropriate treatment methods to be implemented.

Impact of different smoke flavors on the quality of hermetically sealed green chili paste (Nam Prik Num)

This research examined how various smoke flavors such as powdered smoke, teak, and longan wood affected the quality of hermetically sealed green chili paste (Nam Prik Num). Teak and longan wood smoking produced a darker green color than the control and smoke powder samples. The smoky aroma had no impact on yield, pH, water release, or chemical composition. However, it did influence moisture content, lightness (L*), redness (a*), yellowness (b*), and water activity (aw) compared to control and smoke-flavored samples. The attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) showed structural changes, control with C-H groups at 2924 cm−1, and smoking introduced –COOR at 1744 cm−1, signifying flavor-induced alterations. The heightened peak hinted at a distinctive smoke aroma, deepening our understanding of molecular transformations. Sample smoke flavor compounds studied using a solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS), included hydrocarbons, alcohol, aldehydes, ketones, esters, acids, and sulfide compounds, with sulfide compounds, alcohol, hydrocarbon, and alcohol compounds as the predominant volatiles. Nam Prik Num with smoke powder scored higher in appearance, color, aroma, taste, and texture than the control. Flavoring with smoke powder is more effective than charcoal grilling for adding smoky taste to Nam Prik Num.

Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning

BackgroundThe degree to which Anopheles mosquitoes prefer biting humans over other vertebrate hosts, i.e. the human blood index (HBI), is a crucial parameter for assessing malaria transmission risk. However, existing techniques for identifying mosquito blood meals are demanding in terms of time and effort, involve costly reagents, and are prone to inaccuracies due to factors such as cross-reactivity with other antigens or partially digested blood meals in the mosquito gut. This study demonstrates the first field application of mid-infrared spectroscopy and machine learning (MIRS-ML), to rapidly assess the blood-feeding histories of malaria vectors, with direct comparison to PCR assays.Methods and resultsFemale Anopheles funestus mosquitoes (N = 1854) were collected from rural Tanzania and desiccated then scanned with an attenuated total reflectance Fourier-transform Infrared (ATR-FTIR) spectrometer. Blood meals were confirmed by PCR, establishing the ‘ground truth’ for machine learning algorithms. Logistic regression and multi-layer perceptron classifiers were employed to identify blood meal sources, achieving accuracies of 88%–90%, respectively, as well as HBI estimates aligning well with the PCR-based standard HBI.ConclusionsThis research provides evidence of MIRS-ML effectiveness in classifying blood meals in wild Anopheles funestus, as a potential complementary surveillance tool in settings where conventional molecular techniques are impractical. The cost-effectiveness, simplicity, and scalability of MIRS-ML, along with its generalizability, outweigh minor gaps in HBI estimation. Since this approach has already been demonstrated for measuring other entomological and parasitological indicators of malaria, the validation in this study broadens its range of use cases, positioning it as an integrated system for estimating pathogen transmission risk and evaluating the impact of interventions.

Microplastic concentrations and risk assessment in water, sediment and invertebrates from Simon's Town, South Africa

Plastic pollution is an ever-increasing threat globally and poor waste management in South Africa has caused an increase in plastic leakage into the environment. Plastic waste in the environment are categorized according to size and plastic particles smaller than 5 mm in size are regarded as microplastics (MPs), and little to no research has been done on MPs pollution within the marine coastal environment and rocky shores in South Africa. Sampling was done in February 2020 at a rocky shore within Simon's Town Marina, Cape Town. MPs were extracted from collected water (n = 5), sediment (n = 5) and biota (n ≤ 30) samples. The extracted MPs were further classified based on shape, colour, size and an attenuated total reflectance Fourier-transform infrared (ATR-FTIR) instrument was utilized for polymer type identification The risks posed by MPs because of concentration at which they occurred and chemical composition were assessed in all the sample types. As expected, MPs were higher in sediment (38 ± 2 MP/kg) than in water (0.37 ± 0.06 MP/L) as the area has low water energy, allowing MP particles to settle within the sediment. Filter-feeding organisms had the lowest average MP particle concentrations (0.28 ± 0.04 MP/g) but displayed the highest variation of MP particle colours due to the non-selective feeding strategy, where other feeding strategies ingested mostly black/grey particles. The dominant MP size was between 100 μm and 500 μm in size for all samples combined, with the most abundant MP polymer type being nylon (27.27 %), polyethylene terephthalate (PET) (18.18 %) and natural MP particles such as cotton (18.18 %). The risk assessment indicated that polymer type poses a greater risk of MP pollution than MP concentrations.

Evaluation of the Quality Changes in Three Commercial Pastourma Samples during Refrigerated Storage Using Physicochemical, Microbiological, and Image Analyses Combined with Chemometrics.

Despite the inherent stability of dried and cured products, such as pastourma, appropriate refrigeration remains essential for preserving their optimal characteristics. This study explored quality and safety characteristics in lamb, beef, and buffalo pastourma during 16-day refrigeration storage after package opening. The comprehensive approach employed Attenuated Total Reflection-Fourier-Transform Infrared (ATR-FTIR) spectroscopy, colorimetry, and image analysis, alongside physicochemical and microbiological analyses, to shed light on these alterations. The findings reveal a reduction in textural uniformity and color vibrancy (fading reds and yellows) across all samples during storage, with lamb pastourma exhibiting the most pronounced effects. Notably, image analysis emerged as a powerful tool, enabling the accurate classification of samples based on storage duration. Additionally, significant variations were observed in moisture content, hue angle, firmness, and TBARS levels, highlighting their influence on pastourma quality. The study documented a gradual decrease in lactic acid bacteria and aerobic plate count populations over time. ATR-FTIR spectra's interpretation revealed the presence of lipids, proteins, carbohydrates, and water. Protein secondary structures, demonstrably influenced by the meat type used, exhibited significant changes during storage, potentially impacting the functional and textural properties of pastourma. Overall, the findings contribute to a deeper understanding of pastourma spoilage during storage, paving the way for the development of improved preservation and storage strategies.

New methods for the identification of malachite pigments with varying particle sizes used in ancient Chinese murals by spectroscopic techniques

This study attempts to propose a simple, low-interventive and higher accuracy identification method for malachite pigments with varying particle sizes based on attenuated total reflection-Fourier transform infrared (ATR-FTIR), and evaluate the accuracy of ATR-FTIR and ultraviolet–visible near-infrared (UV-VIS-NIR) spectroscopy for identifying particle sizes. The micro-morphology, particle size, colour difference, and spectral characteristics of the malachite samples were determined using scanning electron microscopy (SEM), laser particle size analysis, colourimetry, UV-VIS-NIR and ATR-FTIR spectrometry. The principal component analysis (PCA) and mathematical models based on nonlinear curve fitting using UV-VIS-NIR and FTIR spectral data were developed and evaluated for the particle size identification of various malachite samples (pure malachite samples, fresh and aged malachite-rabbit glue samples). SEM morphological results showed that the malachite pigments with smaller particle sizes appear to have irregular flakes with a few loose agglomerated granules attached to the surface. The weight-specific surface area also increased gradually as the pigment particle size decreased. The colour difference results revealed that smaller malachite particle size gradually shifted toward the red–blue regions in CIE L*a*b* colour space. In addition, more intense reflectance and IR spectral absorbance peaks were observed in the case of the smaller particle size. The above proposed methods of particle size identification proved to be highly feasible and effective in predicting pigment particle size, which were confirmed by the PCA identification results and the mathematical model. Also, ATR-FTIR spectroscopy could provide more accurate predictions than UV-VIS-NIR spectroscopy, by keeping prediction errors below 5.5 μm.

Novel short Agave americana based biocomposite and nanobiocomposites for automotive applications

Short fibers of Agave Americana (AA) was extracted from its plant leaf, was chemically treated with Ac2O, HCOOH, H2O2, KMnO4 and NaOH, and then characterized by Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), thermo-gravimetric/differential thermo-gravimetric (TGA/DTG), and field emission-scanning electron microscopy (FE-SEM). PVA stabilized copper nanoparticles from chemical reduction method was characterized using field emission-scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDAX), powder X-ray diffraction (PXRD), Dynamic light scattering analysis (DLS), UV–visible absorption spectroscopy, Fourier Transform infrared (FT-IR) spectroscopy and Thermogravimetric analysis/differential thermogravimetry (TGA/DTG). Bio-composites (AA + Polyester Resin (PE) and hybrid nano bio-composites (AA + Polyester Resin (PE) + Cu) were prepared from the untreated and treated AA fibers and further characterized. The synergistic effect of chemical treatment on morphological (SEM), thermal (TGA/DTG), mechanical properties (flexural, tensile, impact and compressive strength) followed by % water absorption were examined. The average surface roughness values (Ra) of chemical treated fiber was identified to be in decreasing manner along with compression strength of biocomposite in the order of untreated (10.74 μm, 44.01 MPa) > NaOH (8.55 μm, 45.07 MPa) > HCOOH (3.49 μm, 24.10 MPa) Ac2O (3.24 μm, 22.10 MPa) > H2O2 (2.51um, 17.9 MPa) > KMnO4 (1.52 μm, 15.1 MPa) treated fibers. Subsequently, the addition of 2s@PVA led to reverse the order namely, the compressive strength of the bionanocomposites were Untreated (10.74 μm, 9.0 MPa) < NaOH (8.55 μm, 0.1 MPa) < HCOOH (3.49 μm, 3.6 MPa) < Ac2O (3.24 μm, 7.6 MPa) < H2O2 (2.51um, 13.3 MPa) < KMnO4 (1.52 μm, 44.1 MPa) treated fibers. Similarly, the biocomposite where the fibres were treated with NaOH, HCOOH were more rough and had, good interconnection between fiber/PE matrix along with enhanced mechanical properties. On addition of nanobiocomposite, only KMnO4 treated fiber composite possed significant mechanical properties. Therefore, mixing CuNPs@PVA with KMnO4 treated fibers led to significant boost to the mechanical properties and minimised the % water absorption properties when compared to the untreated AA/PE biocomposites. The KMnO4 treatment of the AA fiber and addition of copper nanoparticles caused the enhancement of thermal properties, tensile strength and flexural strength. But, these nanobiocomposites were observed to have low impact strength; while, H2O2 treated nanobiocomposites had highest impact strength. The chemical treatment of the AA fiber with NaOH, Ac2O and KMnO4 developed the water resistance of the respective biocomposites. All the nanobiocomposites with chemically treated AA fibers (except Ac2O) were found to have high water resistance. So, they can be used in automotive industry for non-structural applications.

Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) Spectroscopy Combined With Deep Learning for Classification of Idiopathic Recurrent Spontaneous Miscarriage (IRSM)

Idiopathic recurrent spontaneous miscarriage (IRSM) is characterized by the repeated losses of two or more clinically detected pregnancies before 24 week of gestation with the cause remaining unknown. We aim to ascertain whether attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy with deep learning (DL) can accurately distinguish IRSM from controls. Endometrial tissue (internal lining of the uterine cavity) was collected from 20 women with IRSM and 20 controls during window of implantation. ATR-FTIR spectroscopy was used to acquire spectra (400–4000 cm−1) from three random locations across each specimen and averaged. Multivariate analysis was used to assess group differentiation. Furthermore, DL classifiers were implemented to predict IRSM. Hierarchical cluster analysis (HCA) demonstrated distinct clustering of the two groups. The principal component analysis (PCA) loading plot indicated contributions from amide I, II and III bands, symmetric methyl (CH3) bending of proteins, lipids, nucleic acids, asymmetric stretching of CH2 due to lipids, collagen, and glucose in discriminating between IRSM and controls. Supervised orthogonal projections to latent structures discriminant analysis (OPLS-DA) exhibited optimized discrimination between the groups. Convolutional neural network (CNN) and variational autoencoder-artificial neural network (VAE-ANN) classifiers exhibited good performance and showed classification accuracy of 94% and 94% and F1 scores (mean of recall and precision) of 95% and 93%, respectively. The optimal area under the receiver operating characteristic curve (AUC) value of both CNN and VAE-ANN was 0.94. ATR-FTIR spectroscopy combined with DL approach is promising for understanding complex disease pathophysiology and enabling early prediction.

Honey differentiation with FTIR-ATR spectroscopy – Comparison with physicochemical parameters of a Polish honey sample set

The varietal discrimination of honey is important because its bioactive properties, as well as specific characteristics, depend on the botanical origin. The present study aimed to verify the discriminatory power of Fourier-transform infrared spectroscopy (FT-IR) for differentiating Polish honeys and compare the results with the discrimination power of a model built on the basis of selected physicochemical parameters. For this purpose, buckwheat, rape, acacia, heather, goldenrod, and coniferous honeydew honeys were collected from trustworthy apiaries in different regions of Poland. The samples were further authenticated using pollen analysis and tested by means of FT-IR and the attenuated total reflection (ATR) technique. Basic psychochemical parameters were also assessed. The obtained data were analyzed by means of principal component analysis (PCA) and linear discriminant analysis (LDA). The obtained models were validated with a leave-one-out cross-validation (LOOCV) procedure. PCA analysis confirmed the usefulness of the ATR FT-IR method as a honey differentiation tool. The LDA for the FT-IR model allowed to correctly classify 26 out of 32 samples (81.3% accuracy), which was only slightly lower than for data the model based on the physiochemical characteristics (27 out of 32 samples and 84.4% accuracy), revealing a certain potential of FT-IR as fast evaluation method.