Vibrational Spectroscopy最新文献

During the last decades, Fourier-transform infrared spectroscopy (FTIR) with attenuated total reflectance (ATR) has gained a substantial role in monitoring structural changes of proteins. The conformation of the polypeptide backbone, reflecting the pattern of intramolecular hydrogen bonding, affects the vibrational energy of carbonyl groups and, consequently, the absorption of infrared light. Specifically sensitive to the conformational state of a polypeptide is the Amide I region (1700–1600 cm⁻¹), whose individual bands correlate with distinct secondary structures. ATR-FTIR thus provides the possibility to determine secondary structure content by deconvolution of the Amide I region, which makes it a valuable tool for investigating protein structure. Furthermore, the sensitivity of the Amide I band to subtle differences in hydrogen bonding enables discrimination between different β-sheet conformations, including intramolecular and intermolecular β-sheet. Compared to other methods for secondary structure determination, such as circular dichroism, this advantage makes infrared spectroscopy an excellent tool for monitoring aggregation processes. This review is intended to explain the principle of FTIR, the specificities of protein FTIR spectroscopy, to correlate the spectra with the protein secondary structure, and to provide different approaches for spectral analysis. To highlight FTIR contribution as a reliable parameter in protein structural analysis, here we review the data regarding the determination of the secondary structure of native globular proteins, the monitoring of discrete conformational changes upon destabilizing treatments, and the monitoring of structural changes in the aggregation process.

The vibrational spectrum of the vapour phase above YI₃ was measured by infrared spectroscopy. The spectrum revealed four strong bands, three of which could be assigned to the fundamantal infrared active vibrations of the YI₃ monomer molecule. The observed frequencies agree very well with quantum-chemical calculations reported in literature. The presence of dimer molecules, the fraction of which is significant in the vapour according to literature, is also discussed in the context of assigning the fourth strong band in the experimental spectrum. Although its position matches well with a strong fundamental of the dimer, its intensity suggest a dimer fraction that is far outside the range for the other rare-earth triiodides.

Numerous illnesses progress silently, often reaching a critical stage by the time symptoms appear, making medical intervention difficult. While there exist sophisticated clinical techniques for precise disease detection, they are often invasive procedure and as a rule of thumb is used only with definite symptomatic cases. In addition, the extreme clinical costs prevent a large population from taking advantage of contemporary medical facilities. A non-invasive, economically feasible screening method that detects diseases even in their pre-symptomatic stages may overcome many critical challenges in health and medicine. Utilizing vibrational spectroscopy to identify volatile metabolites offers promise as such a non-invasive, cost-effective diagnostic tool. The article outlines the potential of vibrational spectroscopy for non-invasive diagnostics and proposes a method for its clinical implementation.

In near-infrared spectroscopy analysis, the predictive performance of models may degrade due to variations in the measurement environment and sample properties. Maintenance or updates to the model are essential to uphold prediction accuracy. The state-of-the-art approach for model updates involves labeling new samples and incorporating them into the calibration set. However, this strategy leads to an escalating size of the calibration dataset, and the low ratio of new samples renders the model update process inefficient. To enhance update efficiency, a size-stable updating strategy is presented which involves the simultaneous addition of new samples and removal of old samples. This is achieved by utilizing the new set as a basis for identifying and deleting significantly different old labeled samples. To improve labeling efficiency, a batch mode update is employed. Initially, calibration samples are clustered to yield multiple clusters with comparable sizes to the new update set. Subsequently, differences between multiple old sample clusters and the new set are calculated. The old sample cluster that most different are removed, accompanied by the addition of the new sample set to maintain calibration set size stability. In calculating set similarity, a multi-indices fusion method is employed to ensure accurate judgments. The efficacy of this method is validated through simulated and real data.

The synthesis of TiO₂ nanotubes film decorated with Ag nanoparticles (TiO₂NTs/AgNPs) and its potential applicability as a SERRS (Surface-Enhanced Raman Resonance Scattering) substrate for detection and speciation of anthocyanins species in grape skin extracts is reported. Preliminary spectrophotometric analysis in the UV–Vis region allowed to identify the maximum absorption bands at 527 and 620 nm, related to the flavylium cation (AH⁺) and quinoidal base (A^–), respectively. The increase in the ratio AgNPs/extract concentration in colloidal solutions of AgNPs promoted the intensification of the vibrational modes of anthocyanins species, due to the SERRS effect. From deconvolutions of these SERRS spectra, a linear relationship (R² = 0.993) for the intensity ratio of the vibrational modes A^–/AH⁺ (i.e., for the ratio 1631–1640/1648–1659 cm^–1) as a function of the grape skin extract dilutions at colloidal solutions of AgNPs was found. The deposition of the extracts onto TiO₂NTs/AgNPs surface promoted an additional SERRS signal intensification for A^– in detriment to AH⁺. The deconvolution of these SERRS spectra showed again a linear relationship (R² = 0.999) between the intensity bands ratio A^–/AH⁺ and grape skin extract dilutions in the range of 0.1–1.0 %. The SERRS technique using the TiO₂NTs/AgNPs substrate was more sensitive for anthocyanins quantification compared to the spectrophotometric one. The results revealed that the TiO₂NTs/AgNPs films are versatile, efficient, and promising platforms for SERRS quantification and speciation of anthocyanin species in situ.

The bis(chloro)-bis(1,10-phenanthroline)-manganese(II) crystalline complex, with the formula [Mn(phen)₂Cl₂], was synthesized by slow evaporation method. Structural, thermal, and vibrational properties of the crystal were obtained by X-ray powder diffraction (XRPD), thermal analysis, Raman, and Fourier-transform infrared (FT-IR) spectroscopy. XRPD results showed that the crystal belongs to a monoclinic system with P2₁/c ($C_{2h}^5$) space group. Thermal analyses revealed that the material presents good stability within 300–558 K temperature interval. In addition, Hirshfeld surface analysis revealed the sites involved in intermolecular interactions predominant in the crystal. Also, all Raman and IR-active bands were assigned from computational calculations based on density functional theory (DFT) to analyze intramolecular vibration modes. Additionally, the electronic and vibrational properties were investigated considering three different media (vacuum, methanol, and water) using the integral equations formalism of the polarizable continuum model (IEFPCM). Lastly, the bactericidal assays on Staphylococcus aureus (ATCC 6538), Escherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853) e Enterococcus faecalis (ATCC 25923) were carried out to evaluate the antimicrobial activity of this complex.

It is well known that polyphenols possess potent antioxidant qualities. However, the precise antioxidant action mechanism of phenolic compounds is still not well defined. It is evident that understanding the molecular mechanisms behind polyphenol-lipid interactions is necessary to comprehend this impact. The objective of this work is to examine the effect of phenolic compound coumarin on the physicochemical properties of cell membrane models. To accomplish this goal, model membranes made up of zwitterionic dimyristoylphosphatidylcholine (DMPC) and anionic dimyristoylphosphatidylglycerol (DMPG) lipids were used. These lipid species are the main component of the mammalian and gram-positive bacteria membranes, respectively. Differential scanning calorimetry (DSC) and Raman spectroscopy were applied for studying the interaction of coumarin with DMPC and DMPG. From the DSC results, it was found that pretransition was abolished for both lipid systems at 20 mol% coumarin. Main phase transition shifted to lower temperatures and broadened, but there was not a complete disappearance of main transition and coumarin was not fully miscible. A sharp peak at a higher temperature and a broad shoulder at a lower temperature of the main transition of DMPC were detected when compared to coumarin/DMPG binary system. A reduction in calorimetric enthalpy and entropy values of coumarin/DMPC system at 20 mol% was observed whereas an increase in these calorimetric parameters for coumarin/DMPG system occurred. Thus, the disorder was caused when 20 mol% coumarin interacts with DMPC lipid bilayers. The increase in enthalpy could be a result of the 20 mol% coumarin interactions with DMPG lipid bilayers or a possible generated partial interdigitation. From the Raman results, the peak height Raman intensity ratio I₁₀₉₀/I₁₁₃₀ showed that coumarin induces disorder in the DMPG bilayers in the gel phase due to the increasing gauche:trans ratio. According to the results obtained by DSC and Raman spectroscopy, it was found that coumarin has a varied effect on membranes made from lipids with choline and glycerol head groups. Thus, it is obvious that the polyphenol/membrane interactions can be significantly impacted by the lipids’ structural variations.

The rising possibilities of vibrational spectroscopy coupled with multivariate modeling, enables many applications of these well-established analytical techniques, easy-to-use for rapid and non-destructive analysis. Owing to the lack of need for hazardous solvents or reagents in sample preparation, FTIR, NIR and Raman spectroscopy belong to the group of environmentally friendly techniques, known as “green analytical techniques”. Such characteristics re-introduce them into the pharmaceutical industry, especially as part of process-analytical-technology (PAT) tools for both qualitative and quantitative analysis.

This research aimed to assess the potential of ATR-FTIR, NIR and Raman as a PAT for the quantification of Vitamins B1 (thiamine), B6 (pyridoxine) and B12 (cyanocobalamin) in a powder blend, using a partial least square (PLS) regression model, as well as to monitor the powder blend homogeneity.

Results from each of the vibrational spectroscopy techniques were compared with those obtained by HPLC, which served as the reference analytical technique for quantifying the active substances in complex matrices using multivariate analysis. The developed PLS models were evaluated for their ability to quantify each of the active substances in the powder blend. The ATR-FTIR, NIR and Raman spectrum segments, revealed by the Variable Importance for Projection (VIP) plot for each model, respectively, were linked to the band assignment of each active ingredient.

The statistical indicators of the models (interpretation rate (R2X and R2Y), predictive ability (Q2 and RMSEP) and accuracy (RMSEE)) demonstrated their suitability for in-process estimation of vitamin B1, B6 and B12 contents in powder blends. Estimating B12 content proved more challenging, likely due to sampling issues related to its low content in the powder blend. Nevertheless, the observed variability in the models aligns with the variability in results obtained by HPLC, indicating the lack of blend homogeneity. Therefore, the quantification models could be considered for a further upgrade as PAT for monitoring the powder blend homogeneity during the manufacturing of vitamin blends.

Biomacromolecules, which mainly include nucleic acids, proteins, sugars, and lipids, are extensively present in the somatic cells of organisms. These four types of biomacromolecules not only play a crucial role in various normal physiological processes of organisms but also draw increasing attention in the field of in vitro research, such as nucleic acid aptamers and nucleotide drugs. Consequently, the accurate detection of biomacromolecules holds immense importance and significance. Surface-enhanced Raman Scattering (SERS) possesses a natural advantage in the detection of biomacromolecules due to its “fingerprint” characteristics, non-destructive nature, high sensitivity, simplicity, and speed, and it is not influenced by water signals. This paper primarily discusses the research progress of SERS in the detection of biomacromolecules, elaborates on the application of SERS in detecting nucleic acids, proteins, sugars, and lipids, and anticipates the potential application of SERS in biomacromolecule detection. It is anticipated that this paper will provide researchers with innovative ideas and methods for developing and applying new biomacromolecular detection techniques.

Tuna fish oil (TO) is a valuable source of omega fatty acids and polyunsaturated fatty acids required for human growth and development. Triggered by economic reasons, TO can potentially be adulterated with pork oil (PO), which has a lower price. The adulteration is a serious problem because PO is a non-halal oil, which is truly prohibited to be consumed, especially for Muslim. This research aimed to develop an effective and efficient analytical technique for detecting PO adulteration in TO using Fourier transform infrared (FT-IR) spectroscopy aided by machine learning techniques. Various machine learning techniques were developed, including linear regression, support vector machine (SVM), k-nearest neighbor (kNN), artificial neural network (ANN), and gradient boosting. The result showed that SVM at the fingerprint region (1400–900 cm⁻¹) demonstrated the best model to detect and predict PO in TO with the highest R² (0.993) and the lowest root mean square error (RMSE) of 2.719 %. All levels of PO contained in TO could be accurately predicted, as indicated by the closeness between the actual value and predicted value of PO levels predicted by the model. In conclusion, machine learning could be a promising tool for detecting adulterants in fish oil samples. Further research on method standardization is important to propose the method as the method of choice for fish oil authentication, including halal authentication.