Vibrational Spectroscopy最新文献

The ground state pyramidal geometries of NX₃ (X = H, F, Cl, Br) molecules might undergo a process called “pyramidal inversion”, with a planar transition state structure connecting two identical but oppositely oriented pyramids. In view of recent findings regarding infrared intensities of planar molecules as well as IR intensities of transition states structures, which have demonstrated how the atomic dipoles cannot be ignored when describing the molecular dipole moment, we now combine these two approaches in evaluating the IR intensities of the planar transition state structures of the pyramidal inversions of NX₃. We also applied a numerical method to decompose the force constant of the out-of-plane imaginary normal mode. Our findings show that Coulomb forces are the main factor that shapes the inversion barrier of those molecules. Also, the Charge–Charge Transfer–Dipolar Polarization (CCTDP) decomposition of the imaginary reveals that, while the CT term is null due to symmetry constraints, the DP contribution follows the same direction of the inversion and the atomic polarization X in response to the nitrogen movement in the imaginary normal mode depends on the relative volume of N and X. The out-of-plane normal modes of molecules such as PF₅ are slight different from those of NX₃, since their normal modes may not be subject to the same symmetry constrains, indicating a mathematical distinction between planar and non-planar molecules.

This study investigated the use of handheld spatially offset Raman spectroscopy for the identification of drugs concealed within fruit and vegetable food products, which is a common method of drug trafficking in busy environments such as airports. Handheld Raman spectroscopy is advantageous due to its mobility, speed, and chemical specificity for drug analysis. In this study, spatially offset Raman spectra of six substances were collected and included cocaine and its impurities. Raman spectra were collected for drugs on their own and for drugs concealed in transparent bags and in various food products such as green pepper, pomegranate, potato, and zucchini. The collected spectra were analyzed using different algorithms. The results showed successful identification of drugs in three out of the four tested food products, except for pomegranate, which had a thick rind and spongy tissue that hindered detection. An instrumental hit quality index algorithm provided instant identification with matches above 80% in the three identified products. Correlation in wavelength space yielded high correlation coefficient values between substances in food substrates and reference substances, although there were a few false negatives due to noisy spectra. Principal component analysis successfully differentiated between drugs in different food products. In summary, the study demonstrated the potential of handheld spatially offset Raman spectroscopy for identifying drugs concealed within food products. Future work aims to expand the technique to a wider range of substances and food products and develop a quantitative approach to predict substances’ concentrations. Overall, this research contributes to the field of forensic applications and offers insights into the detection of illicit drugs in challenging scenarios.

The natural gas production from Brazilian pre-salt fields imposed new challenges for petrochemical industry. Actual treatment facilities are not adequate for this new scenario and studies have been conducted to apply new adsorbents materials and membranes for water and CO₂ removal from natural gas at high pressures. To better develop such investigations, sensors for on-line monitoring of natural gas properties like CO₂ and water content are important, since their presence affects the quality of the final product. Near infrared (NIR) spectroscopy associated to chemometric models (partial least squares) were employed for on-line monitoring of representative natural gas systems (methane/CO₂, methane/water and methane/CO₂/water) and a real natural gas at temperature and pressure ranges from 20 to 60 °C and 10 to 200 bar, respectively, water content up to gas saturation and CO₂ content up to 50 wt%. Water solubility values used as reference for NIR Spectrometer calibration in model systems were taken from the literature and for real natural gas calculated with Cubic Plus Association (CPA) Equation of State (EoS), while CO₂ content was experimentally controlled aiming to calibrate the chemometric models in the full range of pressure and temperature. Several strategies were adopted for the chemometric model’s development to obtain the best correlation between NIR spectra and experimental data. Results indicate good correlation in both calibration and validation steps attaining linear correlation coefficients (R²) higher than 0.96 for all systems investigated. The proposed methodology is a potential tool for on-line monitoring of natural gas composition, including CO₂ and water content, at high-pressures and can be applied at petrochemical industries or in laboratories, dispensing sampling or any sample preparation.

“Drug-facilitated sexual assault" (DFSA) is a sexual assault perpetrated against a person rendered unconscious by a substance that changes her/his physical and/or mental condition, such as ethanol or drugs. Several active pharmaceutical ingredients, whether used alone or with alcoholic beverages, can produce anterograde amnesia and loss of inhibition. The most common pharmaceuticals found in DFSAs are GHB (-hydroxybutyric acid), benzodiazepines (Valium, Xanax, or Roipnol), antidepressants (Venlafaxine), muscle relaxants (cyclobenzaprine), antihistamines, sleeping pills (diphenhydramines), hallucinogens, and opioids. Biological samples are typically examined in cases of suspected DFSA; however, occasionally, samples are sent to labs a long period after being collected, jeopardizing the accuracy of the analysis. As a result, in recent years, the focus has shifted to directly detecting the presence of drugs in alcoholic beverages. In light of this, the purpose of the current study is to build a FT-IR-based approach for the determination of alprazolam in a common long drink (gin and tonic). To achieve this goal, pure (Class Pure) and spiked gin tonics (Class Spiked) were analyzed by Fourier-transform infrared spectroscopy (FT-IR). Afterward, two classifiers were used: Sequential preprocessing through ORThogonalization Linear Discriminant Analysis (SPORT-LDA) and Soft Independent Modeling of Class Analogies (SIMCA). Both approaches provided good results: SPORT-LDA achieved a 95% and a 98% accuracy rate (on the external test set of samples) for spiked and pure cocktails, respectively. This corresponds to the misclassification of 5 spiked and 1 pure drinks. The SIMCA model of class pure achieved 98.2% and 91.7% of specificity and sensitivity, respectively, coinciding with 55 pure samples (over 60) correctly accepted and 2 (over 110) erroneously rejected by the model. In conclusion, the SIMCA model of class pure seems preferable, because it minimizes the type II error. Eventually, the study was circumscribed to the spiked cocktails and a novel SPORT model was used to quantify alprazolam in spiked cocktails. This provided noteworthy results, in fact, it led to a Root Mean Square Error in Prediction (RMSEP) of 0.95, and a R²_pred of 0.98.

The low solubility of solute molecules in a nonpolar solvent and the weak interactions between solute and solvent molecules can be utilized to study isolated solute molecules. The in-situ inclusion process of H₂O and D₂O in CCl₄ was observed by infrared absorption spectroscopy. These in-situ spectra showed deviations from the monomer spectrum over time attributed to the formation of water dimers. The water dimer spectrum was extracted by subtraction between the time-series spectra. DFT calculations allowed the assignment of the four vibrational modes in the dimer spectrum. The most notable changes from the monomer to the dimer spectra were in the donor molecule, which showed a peak redshift and a large increase in the absorption strengths, especially for the donor OH (OD) bond which participates in the hydrogen bonding to the acceptor water molecule. Using free energy calculations, the equilibrium constants of H₂O and D₂O dimers dissolved in CCl₄ were determined to be 0.013 and 0.015, respectively, and the concentrations were determined to be ∼1 $\mu$M. The intensity ratio between the $v_3$ mode of the water monomer and the $v_{\mathrm{BD}}$ mode of the dimer calculated through DFT was found to be in close agreement with the ratios from the monomer and dimer spectra. In contrast, when the solvent was chloroform, the increase of the $v_{\mathrm{BD}}$ mode of the dimer from the experiment did not agree with that from the calculations, indicating that the specific interaction of the water molecules with chloroform should be considered.

Oysters are extensively cultivated worldwide. However, significant variations in chemical composition, quality, and price exist between oysters from different geographical origins. This study employed portable near-infrared spectroscopy in conjunction with chemometric analysis to determine the geographical origin and glycogen content of oysters. Pretreatment methods (multiplicative scattering correction, first derivative, and second derivative) were used to preprocess the raw spectra. Partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), and support vector machine (SVM) were then adopted to establish the qualitative models. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were compared for predicting the glycogen content. The results revealed that the PLS-DA, OPLS-DA, and SVM models classified the geographical origin of oysters with 100% accuracy. For quantitative analysis, the regression equations displayed high predictive ability. The SVMR model was superior to the PLSR model for glycogen content prediction, with a coefficient of determination of prediction (R²_P) of 0.9253 and a residual prediction deviation (RPD) of 3.62. Therefore, the proposed approach is suitable for the accurate and environmentally friendly determination of the geographical origin and glycogen content of oysters, thus representing an attractive alternative method for the traceability supervision and quantitative analysis of seafood products.

Rapid identification of the active state of foodborne bacteria is crucial for ensuring the safety and quality control of food or pharmaceutical products. In this study, a combination of hyperspectral microscope imaging (HMI) and machine learning algorithm is employed for the identification of active state of Escherichia coli (E. coli). Hyperspectral microscope images of live, 100 ℃ heat inactivation and 121 ℃ high-pressure inactivation of E. coli are collected in wavelength range of 370–1060 nm. Savitzky-Golay (SG) smoothing combing with normalization is used for spectra preprocessing. And principal component analysis (PCA) is employed for spectral dimension reduction. Four different regions of interest (ROIs), including the entire bacterial cell ROI (cell), the outer cell wall ROI (cell_r), the membrane structure ROI (cell_w) formed by the cell wall and cell membrane, and the central of the cell ROI (cell_cy), are extracted and used as model input variables to investigate the influence on the modeling results. Five model algorithms, support vector machines (SVM), random forests (RF), k-nearest neighbors (KNN) algorithms, discriminant analysis (DA) classifiers, and long short-term memory (LSTM) neural networks are used and compared. Modeling results with spectral data of cell_r perform better than those with other ROIs. Accuracy of the models with data of the cell_r ROI are as follows: 79.78% for SVM, 95.11% for RF, 91.33% for KNN, 98.22% for DA, and 93.78% for LSTM. DA achieves the highest classification accuracy. The results show that high-temperature inactivation induces changes in bacterial tissue and morphology, resulting in certain spectral differences among bacteria in three different states. The combination of hyperspectral microscope imaging and machine learning algorithm can provide an effective method for identification of active and inactive states of E. coli. Furthermore, the model, constructed with the data of cell_r ROI, exhibits the best performance in identification.

Once turmeric has been ground into powder, it is difficult to tell visually if it has been tampered with. In this study, ATR-FTIR spectroscopy was used in tandem with one-class support vector machine (OCSVM) to detect adulteration in turmeric powder. The OCSVM models were trained using 42 pure turmeric powder samples, optimized using 30 pure turmeric powder samples, and subsequently evaluated by classifying 30 pure and 120 adulterated (cornstarch, Metanil Yellow, Orange II, and Sudan I) samples. Preprocessing methods, such as Savitzky-Golay (SG)-derivatives, standard normal variate (SNV), and multiplicative scatter correction (MSC), were used individually and in combination to obtain the best-performing model. Models were assessed by comparing the sensitivity, specificity, and efficiency values and compared with one-class soft independent modeling of class analogy (OCSIMCA). The best performing OCSVM model (sensitivity = 1.00, specificity = 0.89) was obtained by first conducting an MSC on the raw data followed by SG-2nd derivative transformation. It also has an efficiency value of 0.94, which was 0.14 higher than when data preprocessing was not done. Compared to the results of OCSIMCA, the OCSVM model gave a higher efficiency value and can detect lower levels of cornstarch adulteration. Also, the results showed that inclusion of data preprocessing can lead to a better classification model. With the obtained evaluation parameter values, ATR-spectroscopy coupled with OCSVM demonstrated its potential for screening turmeric powder products.

Spectra obtained from fresh grapevine organs provide information on chemical composition but could also contain valuable information on the morphological and physical attributes. The prediction of grapevine organs physical attributes using infrared spectroscopy is explored for the first time in this study. Near infrared spectroscopy (NIR) using a solid probe (NIR-SP) and a rotating integrating sphere (NIR-RS) and mid infrared (MIR) were used to obtain spectra from fresh and intact grapevine shoots, leaves, and berries. Linear partial least squares (PLS) and non-linear least absolute shrinkage and selection operator (LASSO), and extreme gradient boost (XGBoost) were implemented to predict relevant physical attributes in grapevine organs. NIR-RS using XGBoost showed coefficients of determination in validation (R²val) of 91.01% and root mean square error of prediction (RMSEP) of 0.71 mm (6.80%) for berry diameter. Shoot diameter was predicted at R²val of 62.08% and RMSEP at 0.82 mm (12.75%) using NIR-RS with LASSO regression. Monitoring these attributes throughout the growing season can lead to important viticultural information on grapevine yield, growth, and health.

The Raman spectrum of furfural is computed and analyzed using the Vibrational Coupled Cluster Method (VCCM). Furfural has immense applications in organic synthesis, electrocatalysis and energy conversion process. The experimental Raman spectrum of furfural is congested and broad, even in the medium energy regions like C-C stretching and CO stretching regions. We extensively analyze the Fermi and higher quanta resonance effects on the Raman spectrum by examining the VCCM wavefunctions. In addition, a systematic study on the effect of incident frequency on the anharmonic Raman activity is carried out by comparing the results with incident wavelengths 325.0 nm and 632.8 nm against static polarizability.