Vibrational Spectroscopy最新文献

In near-infrared spectroscopy analysis, ensuring the accurate transfer of models between different instruments relies on maintaining the accuracy of instrument wavelengths and absorbance. To mitigate absorbance drift at different wavelength points, this paper proposes a near-infrared spectroscopy point-by-point quadratic polynomial correction method based on carbon-titanium dioxide powder samples. The method establishes a quadratic polynomial relationship model for the absorbance of each wavelength point between the main instrument and slave instruments. The study utilized two S450 grating-based diffuse reflection near-infrared spectroscopy instruments, with one serving as the main instrument and the other as the slave instrument. The point-by-point quadratic polynomial was employed to correct wheat spectra collected by the slave instruments, and a crude protein content prediction model for wheat was established, comparing it with linear regression correction. After correction, the average Euclidean distance of wheat spectra decreased by 66.71%, from 0.0937 to 0.0321, and the average peak-valley Euclidean distance decreased by 72.28%, from 0.0203 to 0.0056. The standard deviation of the predicted results decreased by 90.69%, from 1.4372 to 0.1338. The correction effect of the method combined with traditional preprocessing methods was superior to using preprocessing methods alone. Overall, the near-infrared spectroscopy point-by-point quadratic polynomial correction method based on carbon-titanium dioxide powder samples significantly reduces spectral differences between different instruments, enhances spectral consistency, and diminishes prediction errors, achieving improved model sharing between instruments.

Photonic data analysis is a field at the intersection of imaging, spectroscopy, machine learning, and computer science. The diversity of both data types and application scenarios requires flexibility in the methods applied, combining a full range of computational methods, from classical chemometric techniques to state-of-the-art deep learning solutions. Interdisciplinary and international collaborations are needed to accelerate the progress of photonic data science. An underlying data infrastructure and standardization will be needed to provide collaborative platforms for research on data comparability, enabling the integration of novel photonic techniques into routine applications. The increasing complexity of the questions being investigated requires the application of more sophisticated data-driven models, which may only be optimized for large data sets. Unfortunately, novel techniques in the early stages of development can rarely provide a variability of measured samples sufficient to build a generalizable complex model. To overcome this problem, state-of-the-art methods will emerge for working with extremely limited or unbalanced data, as well as for dealing with device-to-device variations. Further developments are also foreseen in computable artificial intelligence methods, which will allow the validation of models of any architecture by comparing them with the knowledge of the researchers.

Although the potential of Coherent Raman Spectroscopy (CRS) in the area of biomedicine, life sciences and material sciences has been well demonstrated, its wide-spread practical application is still rather limited. The two main CRS techniques are Coherent Anti-Stokes Raman Scattering (CARS) and Stimulated Raman Scattering (SRS) spectroscopy or microscopy. Here we present the current state of the art and challenges facing CRS. Although many technological challenges have been addressed to date, showing how to improve resolution, sensitivity and selectivity of CRS, significant efforts are still needed to increase the awareness of these techniques in the academic community, develop reliable protocols, and extend them to practical applications. For this purpose it is also necessary to initiate national and international research networks that can significantly contribute to the development of CRS approaches in areas that have so far made little use of CRS alongside other Raman spectroscopic methods. The purpose of this perspective paper is to present the current state-of-the-art of CRS with a historical background, assess the challenges and present some future development visions.

Raman optical activity (ROA) has truly reached middle age at 50 years. The technique has matured significantly in this period, both with respect to instrument development and number of applications and users. Yet, ROA is still viewed as an auxiliary technique, compared to conventional Raman and infrared absorption spectroscopies. In this perspective, we outline the newest trends in the field of ROA, including exciting opportunities for future developments and of course ask the important question: what is the future of ROA?

It is often said that “you are what you eat”, and whether this is said in this decade or in 2050 the choices we make about the food we consume can alter our biology. Therefore, knowing exactly what you eat is important for one to maintain a healthy balanced diet. However, as the food industry grows in complexity and struggles to meet the demand of a rapidly increasing population, it is likely that food fraud will pose a much larger threat to the future safety of food industries and consumers. Thus, it is necessary to employ and develop analytical techniques such as infrared and Raman spectroscopy as mobile ‘Capable Guardians’ to reduce this potential risk. Recent advancements in portable spectroscopic instrumentation which can provide rapid on-site measurements show promise, and may have a pivotal role to play in the ongoing saga of food fraud and contamination. Therefore, the objective of this review is to present a comprehensive overview of food fraud and contamination, highlighting the common analytical methods employed for their assessment, with a specific emphasis on the utility of portable handheld spectroscopic instrumentation which in the future can offer guardianship and thus ensure personalised food security.

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.