Vibrational Spectroscopy最新文献

The structure of the hard tissues of the lower third molars (enamel, dentin, enamel-dentin junction) at different stages of eruption in the presence/absence of connective tissue dysplasia as a factor that can affect not only odontogenesis, but also teething was analyzed using infrared (IR) spectroscopy. A technique for deconvolution of IR spectra of hard dental tissues has been developed. It has been established that the differences between the stages of eruption are due to changes in the mineral component (phosphate ions) for all dental tissues, while for the enamel-dentin junction an important contribution is made by fluctuations in the methyl and methylene groups of organic compounds, for dentin the contribution of collagen absorption bands is shown. The differences between the stages of tooth eruption increase in the following order: dentin, enamel-dentin junction, enamel. It can be assumed that in the early stages of tooth formation, it is with the participation of collagen proteins that changes in the structure of dentin occur, which subsequently causes changes in the enamel-dentin junction and enamel. Changes in the enamel are subtler and appear only with additional processing of the IR spectra using mathematical methods.

Understanding and control of molecular vibrations are essential aspects for both fundamental and application considerations of organic semiconductor electronics. The reason is that the organic electronic devices are driven by diverse electronic processes in molecular solids, such as charge carrier transport and excitonic progression, that are strongly influenced by coupling with vibrations. In the present study, molecular vibrations of single-crystals of pentacene, a representative organic semiconductor material, were examined in the far- to mid-infrared range by means of Fourier transform infrared (FT-IR) spectroscopy using a linearly polarized synchrotron radiation light source. The IR absorption spectra exhibited significant modulation depending on the crystalline in-plane azimuthasl angle of c*-oriented single-crystal pentacene.

Essential Oil (EO) extracted from Rosemary is known for its therapeutic, antifungal, stimulant and antibacterial effects. This study aimed to detect and quantify the adulteration of Rosemary essential oil with different percentages of eucalyptus essential oil, using two analytical techniques: gas chromatography with Flame Ionization Detection (GC-FID) and Fourier Transform Mid-infrared spectroscopy (FT-MIR), combined with chemometric tools such as Principal Component Analysis (PCA), Partial Least Squares regression (PLS-R) and support vector regression (SVR). The use of PCA on the results obtained from GC-FID and FT-MIR indicates the possibility of categorizing the data into two distinct groups: adulterated essential oil and non-adulterated essential oil. However, it is noted that GC-FID can only detect adulteration starting from 40%, while spectroscopy is capable of detecting lower percentages of adulteration. The use of PLS-R and SVR calibration models for adulteration quantification demonstrates high performance capabilities for both techniques (GC-FID and FT-MIR), as indicated by high R2 correlation coefficients indicating good fit, with lower root mean square error (RMSE) values demonstrating predictive accuracy. The results suggest that FT-MIR spectroscopy is preferable to GC-FID for the quantification and discrimination of adulterated essential oils. FT-MIR spectroscopy is considered superior to GC-FID due to its non-destructiveness, speed and lack of sample preparation.

Escherichia coli (E. coli) is one of the most important pathogenic bacteria causing poultry diseases, characterized by a wide distribution range, rapid spread, and high mortality rate. Early diagnosis of E. coli in poultry feces provides the possibility for targeted treatment and rapid recovery of diseased poultry, and more importantly, prevents the rapid spread of pathogens among densely bred poultry. In order to implement rapid, low-cost, and high-frequency detection of E. coli, this study explored the feasibility of Raman spectroscopy. Firstly, theoretical configurations and density functional calculations of N-acetylmuramic acid and N-acetylglucosamine in the cell wall of E. coli were performed. Then, Raman measurement models for E. coli were established based on two feature extraction methods (Successive Projections Algorithm, Competitive Adaptive Reweighted Sampling) and four modeling methods (Random Forest Algorithm, Convolutional Neural Networks, Back Propagation Neural Networks, Radial Basis Function). Finally, a method based on the extraction of Raman spectral features using density functional theory was determined to optimize the existing models, and it was demonstrated that this feature variable extraction method improved the accuracy of all four measurement models to some extent. Ultimately, the optimal model, the improved SPA-RF, was obtained through comparative analysis, with an accuracy, precision, recall, specificity, FNR, FDR, and AUC of 98.38%, 98.61%, 99.83%, 88.08%, 0.81%, 11.82%, and 1, respectively. This study reports an early method for the early treatment of E. coli diseases and provides a molecular structure database for studying N-acetylmuramic acid and N-acetylglucosamine, as well as a basis for vibrational spectroscopy detection of E. coli diseases, promoting the application of Raman spectroscopy technology in the diagnosis of livestock diseases.

Given the intricacies and nonlinearity inherent to industrial fermentation systems, the application of process analytical technology presents considerable benefits for the direct, real-time monitoring, control, and assessment of synthetic processes. In this study, we introduce an in-line monitoring approach utilizing Raman spectroscopy for ethanol production by Saccharomyces cerevisiae. Initially, we employed feature selection techniques from the realm of machine learning to reduce the dimensionality of the Raman spectral data. Our findings reveal that feature selection results in a noteworthy reduction of over 90% in model training time, concurrently enhancing the predictive performance of glycerol and cell concentration by 14.20% and 17.10% at the root mean square error (RMSE) level. Subsequently, we conducted model retraining using 15 machine learning algorithms, with hyperparameters optimized through grid search. Our results illustrate that the post-hyperparameter adjustment model exhibits improvements in RMSE for ethanol, glycerol, glucose, and biomass by 9.73%, 4.33%, 22.22%, and 13.79%, respectively. Finally, specific machine learning algorithms, namely BaggingRegressor, Support Vector Regression, BayesianRidge, and VotingRegressor, were identified as suitable models for predicting glucose, ethanol, glycerol, and cell concentrations, respectively. Notably, the coefficient of determination (R²) ranged from 0.89 to 0.97, and RMSE values ranged from 0.06 to 2.59 g/L on the testing datasets. The study highlights machine learning's effectiveness in Raman spectroscopy data analysis for improved industrial fermentation monitoring, enhancing efficiency, and offering novel modeling insights.

Vibrational spectroscopy, largely based on infrared absorption and Raman scattering techniques, is much vaunted as a label free approach, delivering a high content, holistic characterisation of a sample, with demonstrable applications in a broad range of fields, from process analytical technologies and preclinical drug screening, to disease diagnostics, therapeutics, prognostics and personalised medicine. However, in the analysis of such complex systems, a trend has emerged in which spectral analysis is reduced to the identification of individual peaks, based on reference tables of assignments derived from literature, which are then interpreted as biomarkers. More sophisticated analysis attempts to unmix the spectrum of the complex mixture into constituent components, which are then used to characterise the biochemistry of a sample and changes to it, in terms of its constituent components. Data mining the spectra, and in particular change due to kinetic processes, remains a challenge, and it is proposed that the rate of temporal evolution of the combination spectrum can be used in itself as a label by which to guide the spectral analysis. Ultimately, it is argued that the true potential of label free spectroscopy is best harnessed in a truly “spectralomic” approach, by which the spectral signature of an “event”, such as drug intercalation in the DNA of the nucleus of a cell, or a key stage of a cellular pathway such as oxidative stress, is presented. It is envisioned that, in the future, such Spectralomics pathway analysis will be fully integrated with similar omics approaches, potentially ultimately through deep learning algorithms, and underpinned by systems biology kinetic models, to provide a living human cell atlas, describing the function and dysfunction of organism at a cellular level, as the basis for improved healthcare.

Kidney disease is a worldwide public health problem, affecting between 8% and 16% of the global population. Chronic kidney disease is a silent disease and its detection is often late, making the treatment difficult. Actual diagnosis is based on dosage of canonical renal biomarkers like serum creatinine and observation of proteinuria/albuminuria which would be relatively insensitive to disease progression which usually is detected too late for any efficient therapeutic intervention. Besides, cardiovascular alterations and uremic toxins accumulation play a negative role in biological functions and lead to the main causes of death in a kidney damage situation. In this way, the development of options for real-time detection of kidney injuries is an urgent need. Considering the emerging Fourier-Transform Infrared spectroscopy (FTIR) as biophotonic resource for the biomedical sciences, we aimed to identify spectral signatures related to biomarkers of chronic kidney disease in human blood serum using micro-FTIR option. We investigated samples from 17 healthy individuals and 33 chronic kidney disease patients. Serum samples were analyzed by micro-reflectance FTIR and compared to routine blood and urinary exams (urinary creatinine, glucose, glycated hemoglobin, creatinine, urea, total proteins, albumin) outcomes. We notice that separator gel in VACUETTE® container tubes is a relevant source of spectral interference which decreased the accuracy of discrimination from 85% to 65%. Acquiring diluted gel signal as background would improve the discrimination performance in spite of some gel bands still present in samples data. In this case our results indicated that vibrations associated with galactose-4-sulfate, CH₂ bending of the methylene chains, C = C in lipids and fatty acids, C = O stretching of lipids, C = N stretching, CH bending vibration from the phenyl rings, N-H bending vibration coupled to C-N stretching, β-sheet of Amide II, and phenyl ring were modulated in blood serum samples of chronic kidney disease patients compared to healthy individuals. Urinary trypsin inhibitors, fatty acids, phenolic derivatives, tryptophan, and plasminogen were the biomolecules related to these assignments. In conclusion, micro-FTIR is a viable option for fast diagnosis of chronic kidney disease being also a powerful tool for monitoring the disease. We propose a method enabling large batches analysis, being eligible technology for clinical laboratories in healthcare facilities. However, for direct clinical applications ATR-FTIR would be the option of choice.

Raman imaging has been employed for a wide range of biological sample analyses, is often chosen for its non-invasiveness, and ability to provide rich information from samples with minimal preparation requirements. In this paper we give a brief overview of the applications of spontaneous Raman imaging in bioanalysis and then consider what Raman imaging in 2050 might look like. We discuss the state of Raman imaging around its inception, then provide a snapshot of current technology, then look towards 2050. We then discuss some of the potential bottlenecks for the continuing development of Raman imaging for biological sample analysis and, where appropriate, outline approaches to overcome these challenges.

In this paper we present a method for transferring calibrations between different spectrometers based on assigning wavelength correspondence. It has been tested for near-infrared (NIR) and Raman spectroscopic instruments, and three examples are included in the paper. The calibration transfer is done in three steps: first wavelength correspondence is established. Second, PLS models are built and tuned for the new spectrometer. Third, the PLS models are slope and bias corrected. The advantages with this approach are that it does not require transfer samples and that there is only one parameter to tune: the number of PLS components. While a few samples with reference values are required for the tuning, it is fewer than methods with multiple parameters that need to be tuned.

Raman spectroscopy is one of many tools available to verify the molecular composition of an analyte. Due to its non-destructive nature and its ability to accurately discern differences in closely related molecular structures, it has become invaluable in many fields, including its potential for forensic gunshot residue detection. Firearm-related fatalities in the United States continue to rise and many of them remain unsolved. This necessitates tools that are better equipped to aid in the investigation of firearm-related crimes, capable of high-throughput analysis yet remaining sensitive to give accurate and valuable information. The comparative downside of Raman spectroscopy to neighboring techniques like mass spectrometry and nuclear magnetic resonance is its lower sensitivity. Surface-enhanced Raman spectroscopy allows for the benefits of Raman spectroscopy alongside the added lower limit of detection with the simple application of nanoparticles, typically gold. Unfortunately, these benefits have seen little on-site application due to the difficulty of translating methods from conventional tabletop Raman spectrometers to point-and-shoot portable Raman spectrometers which have even lower sensitivities (higher limits of detection). Herein, we outline a versatile methodology for the detection of 6 organic gunshot residue components (diphenylamine (DPA), ethyl centralite (EC), 2,4-dinitrotoluene (2,4-DNT), 2-nitrodiphenylamine (2-nDPA), 4-nitrodiphenylamine (4-nDPA), and N-nitrosodiphenylamine (N-nDPA)) in liquid-phase that allows us to detect millimolar concentrations of these analytes. Furthermore, we report calculated vibrational assignments for these 6 analytes in solution, alongside detailed peak-by-peak analyses on a portable instrument. We showed signal enhancement and lower LODs through our data processing as well as a proof-of-concept SERS enhancement in a complex liquid-phase matrix, with an increased sensitivity of 700% when using SERS.