Vibrational Spectroscopy最新文献

Accurate quantification of bacteria is critical for ensuring food safety, advancing biomedical research, and a range of other pressing concerns. Raman spectroscopy is a popular technique for quantitative analysis due to its benefits of being fast, non-destructive, and highly sensitive. However, the accuracy of the transfer model is often limited by factors such as differences in equipment and environmental noise, which limits the popularization of Raman spectroscopy. In this paper, we propose an approach that overcomes this challenge by introducing a dual branch network based on Continuous Wavelet Transform (CWT) for model transfer. Our model comprises dual branches that perform distinct tasks. The spectral learning branch is responsible for extracting features from the spectral domain. The time-frequency map learning branch employs CNNs for extracting the multi-scale information-rich features. The proposed method is used for the quantitative analysis of Escherichia coli. The proposed approach significantly outperforms traditional methods in improving prediction accuracy. It offers a much-needed solution to the long-standing challenge of Raman spectroscopy in the field of bacterial quantitative analysis. With our approach, we can expect to see Raman spectroscopy more widely adopted in the future.

Dyes are important concerns regarding aquatic environmental contamination given their extensive industrial use and the occurrence of highly toxic and carcinogenic effects on the biota. In this work, photodegradation processes of the organic dye crystal violet (CV) by a hybrid plasmonic photocatalyst involving titanium dioxide (TiO₂) and silver nanoparticles (AgNP), through irradiation with low-power visible light were studied, and the experiments were tracked by ultraviolet-visible absorption (UV–VIS) and surface-enhanced resonance Raman scattering (SERRS) spectroscopies. Enhanced photocatalytic activity was observed, reaching about 71, 80 and 87 % of CV removal with only 100 minutes of irradiation, depending on the Ag loading used. The high photocatalytic efficiency is further highlighted by the low energy consumption in the process, requiring only ca. 1.46 kW h L^-1 in the best reaction condition. Quantum-mechanical calculations were used to the assignment of electronic spectra, as well as to the prediction of frontier molecular orbitals and atomic charges, aiming to propose mechanisms for radical attacks. Such results allow suggesting degradation processes involved mainly N-demethylation and bond breaking of central carbon. The presence of CV protonated species was also supported through Density Functional Theory (DFT) investigation. The integration of theoretical and experimental results allows proposing the formation of pararosaniline, phenol and benzophenone derivatives, which may have highest ecotoxicity than the original contaminant, outstanding the remarkable relevance of SERRS spectroscopy in monitoring such recalcitrant substances.

Radiation therapy, particularly X-ray-based treatment, is widely used against cancer due to its ability to induce cell death, hence local tumor control Recently, increasing attention has been devoted to the role of lipid metabolism in the radiation-induced response of tumor cells. This study utilized Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy to examine the role of lipids in the response of hepatocarcinoma (HepG2) cells to X-ray radiation. Infrared spectra were acquired from lipids extracted from HepG2 cells exposed to different X-ray doses (0, 2, and 6 Gy). Results showed that X-ray exposure causes shifts in the peak positions in infrared spectra indicating biochemical changes in lipid components. The phosphate group asymmetric stretching band shifted to higher wave numbers in the 2 and 6 Gy exposed samples, likely due to alterations in membrane fluidity. The 2-Gy exposure led a reduction of sphingolipid, phospholipid, and fatty acid contributions that can be probably ascribed to apoptosis processes. The 6-Gy exposure triggered also changes in sphingolipid content potentially linked to increased lipid peroxidation supported by higher carbonyl contribution. This peroxidation results in smaller lipid fragments and various degradation products. The changes in sphingolipids are also confirmed by the analysis of different ratios between the areas of selected bands and the results of a mass spectroscopy investigation carried out on the same samples.

The determination of the o-nitrotoluene (o-MNT) content in separation process of mononitrotoluene (MNT) is of interest, since it affects the purity of m-nitrotoluene (m-MNT) and p-nitrotoluene (p-MNT). In real-world applications, the calibration model inevitably requires dealing with complex extrapolation problems. Therefore, this study extracted the spectral features of the o-nitrotoluene based on the interval selection algorithm. The linear calibration method (partial least squares (PLS)) and nonlinear calibration methods (support vector machine (SVM), back propagation (BP), random forest (RF), extreme learning machine (ELM)) were used to build the calibration models based on o-nitrotoluene samples in different concentration ranges, and the prediction accuracy and robustness of the calibration model were compared. The results indicate that the effectiveness of different calibration methods is different when going from prediction accuracy to robustness. The prediction accuracy and robustness of RF models are not satisfactory. BP models, which are capable of producing very accurate results in terms of prediction accuracy, are not able to solve extrapolation problems. PLS model has more advantages in model prediction accuracy. ELM has shown the best behavior in terms of robustness of model, but is inferior to PLS in terms of prediction accuracy.

In recent years, many antibacterial agents have been produced with the aim of eradicating infectious diseases, but many of these agents are ineffective against the resistance presented by bacteria. It is currently estimated that more than 60 % of current antibiotics are ineffective, so the discovery of new drugs is vital. Among the compounds studied in recent years are polyoxotungstates, inorganic compounds targeted for their pharmacological properties. The aim of this study was therefore to chemically characterize two tungstates: calcium and sodium, and to evaluate their microbiological properties, both in combination with antibiotics and due to their ability to reverse the resistance process represented by the expression of the enzyme betalactamase. The microbiological tests were carried out using the microdilution technique, with colorimetric disclosure, using resazurin, and the chemical characterization and vibrational modes of the compounds were evaluated using Fourier transform infrared spectroscopy with attenuated total reflectance. Calcium tungstate showed four spectroscopic bands, located between 84 and 1915 cm⁻¹, while sodium tungstate showed two bands at 335 and 935 cm⁻¹. Calcium tungstate intensified the effect of gentamicin against the bacterium Escherichia coli 06, as well as reversing the mechanism of enzymatic resistance presented by the bacteria Staphylococcus aureus K-4100 and K-4414. Given the current scenario of resistance, these results represent new alternatives for the treatment of bacterial infections, allowing a better understanding of the properties of polyoxometalates. These results are unprecedented as far as the literature is concerned.

Accurate identification and discrimination of bacteria is crucial for ensuring food safety and reducing pathogenic infections. This study presents a novel approach that combines surface-enhanced Raman scattering spectroscopy (SERS) with chemometric methods for discriminant analysis of a mixture of pathogenic bacteria into different types and proportions. Au@Ag@SiO₂ composite nanomaterials were employed as the SERS substrate to collect Raman spectra of multiple pathogenic bacteria. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) methods were combined with standard normal variate (SNV) to discriminate the different species of mixed bacteria and the multiple proportion mixed bacterial samples, respectively. The results showed that SNV-PLS-DA had good classification performance in the discriminant analysis of different species of mixed bacteria, with an accuracy of 92% for the external test set. Furthermore, both SNV-PLS-DA and SNV-OPLS-DA models exhibited excellent classification performance in the discrimination of multiple pathogenic bacteria at different mixing proportions, achieving 100% accuracy in the external test set, but except for mixed samples of Escherichia coli and Salmonella typhimurium. Our method demonstrates the accurate capability of the SERS platform combined with chemometric methods in the discriminant analysis of multiple pathogenic bacteria at different species and mixing proportions, which provides novel insights for the synchronous analysis of multiple pathogenic bacteria.

Molecular characteristics of skeletal remains were studied utilizing ATR-FTIR spectroscopy, focusing on comparisons between mature adult and immature non-adult skeletal elements. To cover the intra-skeletal variability, different types of bones were analysed. The objective was to identify significant differences between various skeletal elements of adults and non-adults. Additionally, the correlation between observed differences and DNA preservation was investigated. Despite exposure to taphonomic factors, findings indicate minimal diagenetic changes or a well-balanced alteration in mineral and collagen within bones. The identified differences primarily reflect functional and structural differences among various skeletal elements. Significant differences between adults and non-adults, or lack of it, is attributed to different paths of bone maturation from childhood to adulthood. High DNA preservation in non-adult petrous bones was attributed to the interplay between DNA and carbonates, both occupying hydroxyl sites in the lattice. Conversely, lower DNA content in other bones, especially non-adult bones, was correlated with high relative concentrations of collagen, in which DNA is less stable and more prone to degradation. This study highlights the importance of skeletal variation (inter, intra, developmental stage) when assessing the preservation state of the remains and choosing samples for further analyses such as DNA. For the first time, differences between mature adult and immature non-adult bones are included.

The recent experimental identification of aminomethanol (NH₂CH₂OH) and its synthesis under simulated interstellar conditions suggests that it may be formed, and thus observable, in the interstellar medium. To aid in its possible detection, this work presents high-level theoretical, anharmonic vibrational and rotational spectroscopic data for the three lowest-energy conformers of NH₂CH₂OH. All three conformers fall within 0.80 kcal mol⁻¹ of each other at the CCSD(T)-F12b/cc-pCVTZ-F12 level, with an anharmonic zero-point vibrational energy correction. Additionally, all three conformers are shown to have multiple vibrational frequencies with intensities greater than 100 km mol⁻¹. The most notable of these are the symmetric O-C-N stretch of the lower-energy C₁ conformer at 976.4 cm⁻¹, the C-O stretch of the C_s conformer at 964.9 cm⁻¹, and the antisymmetric O-C-N stretch of the higher-energy C₁ conformer at 1099.0 cm⁻¹. While much of the vibrational spectra of the three overlap, these features should help to separate them. Finally, all three conformers have non-zero dipole moments on the order of 1 D, suggesting that they may be observable by rotational spectroscopy, as well. In all cases, the computational data provided herein will help to facilitate future experimental and observational studies on this key molecule in the formation of extraterrestrial amino acids.

Energetic cocrystal materials are considered to be one of the important directions for the development of energetic materials, due to their high energy density and low sensitivity. However, there is still a lack of effective methods to carry out rapid structural and purity identification. Herein, we explored a method for rapid identification and identification of unknown components extracted from CL-20/MTNP and CL-20/HMX cocrystal processes based on Raman spectroscopy combined with principal component analysis (PCA). Thirty sets of cocrystal and 30 sets of mixed explosives were randomly selected as the training set and 10 sets each as the validation set. The principal components were extracted by dimensionality reduction of the collected Raman spectra using the principal component sub-featured clustering algorithm of chemometrics. The region identification structure formed by different principal components allows intelligent output of whether the sample was cocrystal or not. The results show that the cumulative contribution rate of the three principal components in the sample set was 98.7 %. The confidence ellipses of the validation set were all well distributed within the confidence ellipses of the training set. And the structure identification results of explosive cocrystals were output quickly, accurately and intelligently. Therefore, this method shows good potential application value in the rapid structural identification of other complex mixtures such as energetic even pharmaceutical cocrystals.

In contemporary biomedicine, rapid diagnosis and accurate treatment of trauma is a top priority. The skin, as the organ with the most extensive contact with the outside world, is always inevitably and easily scarred when subjected to varying degrees of violence. However, when evanescent trauma occurs, the judgment of trauma becomes quite difficult, especially for closed trauma. Evanescent trauma mainly refers to the postmortem autolysis and corruption of body tissues, which lead to the failure to identify the traumatic state occurring before death using traditional detection techniques. Rapid and accurate identification of trauma and even trauma in the evanescent state plays an important role in the actual forensic examination. There have been few records on the development of quick and accurate models for the recognition and prediction of evanescent trauma on the skin. In this study, a predictive model for rapid identification of evanescent trauma in skin tissue was constructed by combining forensic spectroscopy and chemometrics analysis. Based on the mean spectra, principal component analysis (PCA) and corresponding loading plots, suggested that certain biomolecules, such as proteins and lipid molecules, might be the source of the difference between the control group and trauma group, and furthermore, there was a certain pattern of change of each molecule in the continuation of postmortem time. Partial least squares discriminant analysis (PLS-DA) was then applied to estimate the identification power of the training dataset and the testing dataset. The AUC values were 89.55 % and 94.67 %. In addition, the AUC values of the fresh-phase trauma recognition model were 100 % and 100 %, respectively, and the AUC values of the evanescent-phase trauma recognition model were respectively 92.52 % and 98.77 %. In summary, the combination of forensic spectroscopy and chemometrics completely applies their advantages of rapidity, accuracy, objectivity, high resolution and discriminative power to the study of evanescent trauma identification. Moreover, in judicial reality, the combination of spectroscopy and stoichiometry is also expected to make a huge difference in medical and criminal law applications.