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Solvent effects on the absorption and fluorescence spectra of Zaleplon: Determination of ground and excited state dipole moments
摘要： Solvent effects on the absorption and fluorescence spectra of Zaleplon, a nonbenzodiazepine sedative/hypnotic drug that is mainly used for the short term treatment of insomnia, were investigated in 18 different solvents with diverse polarities. Dipole moments of the ground and excited state (μg and μe) were determined by Lippert–Mataga, Bakhshiev, Reichardt, McRae and Suppan solvatochromic methods. The dipole moment of Zaleplon ground state in the gas phase has been calculated as μg = 10.95 D (TD-DFT) with B3LYP/cc-pVTZ functional. There is a good agreement of theoretical data with Reichardt, McRae, and Suppan correlations, while some dissidence with Lippert–Mataga and Bakhshiev equations is suggesting the occurrence of specific solute–solvent interactions. Additionally, multiple linear regression analyses with Kamlet–Taft and Catalan solvatochromic models was applied to solute–solvent interactions. Dominant property of the solvent that affects the absorption band and Stokes shifts of Zaleplon is polarity of the solvent while the emission band is influenced mainly by solvent basicity.
关键词： Solvatochromic methods,Dipole moment,Stokes shift,MLR analysis,Zaleplon
更新于2025-09-23 15:22:29
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Determination of the superficial citral content on microparticles: An application of NIR spectroscopy coupled with chemometric tools
摘要： This work evaluates near-infrared (NIR) spectroscopy coupled with chemometric tools for determining the superficial content of citral (????????) on microparticles. To perform this evaluation, using spray drying, citral was encapsulated in a matrix of dextrin using twelve combinations of citral:dextrin ratios (CDR) and inlet air temperatures (IAT). From each treatment, six samples were extracted, and their ???????? and NIR absorption spectral profiles were measured. Then, the spectral profiles, pretreated and randomly divided into modeling and validation datasets, were used to build the following prediction models: principal component analysis-multilinear regression (PCA-MLR), principal component analysis-artificial neural network (PCA-ANN), partial least squares regression (PLSR) and an artificial neural network (ANN). During the validation stage, the models showed ??2 values from 0.73 to 0.96 and a root mean squared error (RMSE) range of [0.061–0.140]. Moreover, when the models were compared, the full and optimized ANN models showed the best fits. According to this study, NIR coupled with chemometric tools has the potential for application in determining ???????? on microparticles, particularly when using ANN models.
关键词： Food composition,Food science,Spectroscopy,Food chemistry,MLR,PCA,PLSR,Prediction,Chemometrics,Food analysis,ANN
更新于2025-09-12 10:27:22
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A comparative path establishment study on routing performance of MLR WDM optical networks
摘要： In this paper, we investigate the effect of path establishment method priorities over routing performance in mixed line rate (MLR) wavelength division multiplexed (WDM) optical networks. The survivable routing with rate and wavelength assignment (SRRWA) problem is presented and an efficient shared backup path protection solution is proposed. We prepared detailed simulation scenarios with all possible prioritizations and observed their performances. The simulation results show that assigning higher priority to single hop methods as compared with multi‐hop methods yields better performance. In both methods, it has been observed that assigning higher priority to grooming reduces the communication cost and the traffic blocking ratio while enhancing the resource utilization.
关键词： MLR,survivable communication,rate and wavelength assignment (RRWA),optical WDM networks,traffic grooming,shared backup protection,routing
更新于2025-09-10 09:29:36
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EXPRESS: Bulk Protein and Oil Prediction in Soybean Using Transmission Raman Spectroscopy: A Comparison of Approaches to Optimize Accuracy
摘要： Rapid measurements of protein and oil content are important for a variety of uses, from sorting of soybeans at the point of harvest to feedback during soybean meal production. In this study, our goal is to develop a simple protocol to permit rapid and robust quantitative prediction of soybean constituents using transmission Raman spectroscopy (TRS). To develop this approach, we systematically varied the various elements of the measurement process to provide a diverse test bed. First, we utilized an in-house-built benchtop TRS instrument such that suitable optical configurations could be rapidly deployed and analyzed for experimental data collection for individual soybean grains. Second, we also utilized three different soybean varieties with relatively low (33.97%), medium (36.98%), and high protein (41.23%) contents to test the development process. Third, samples from each variety were prepared using whole bean and three different sample treatments (i.e., ground bean, whole meal, and ground meal). In each case, we modeled the data obtained using partial least squares (PLS) regression and assessed spectral metric-based multiple linear regression (metric-MLR) approaches to build robust prediction models. The metric-MLR models showed lower root mean square errors (RMSEPs), and hence better prediction, compared to corresponding classical PLS regression models for both bulk protein and oil for all treatment types. Comparing different sample preparation approaches, a lower RMSEPs was observed for whole meal treatment and thus the metric-MLR modeling with ground meal treatment was considered to be optimal protocol for bulk protein and oil prediction in soybean, with RMSEP values of 1.15±0.04 (R2= 0.87) and 0.80±0.02 (R2= 0.87) for bulk protein and oil, respectively. These predictions were nearly two- to three-fold better (i.e., lower RMSEPs) than the corresponding NIR spectroscopy measurements (i.e., secondary gold standards in grain industry). For content prediction in whole soybean, incorporating physical attributes of individual grains in metric-MLR approach show up to 22% improvement in bulk protein and a relatively mild (up to ~ 5%) improvement in bulk oil prediction. The unique combination of metric-MLR modeling approach (which is rare in the field of grain analysis) and sample treatments resulted in improved prediction models, and using the physical attributes of individual grains is suggested as a novel measure for improving accuracy in prediction.
关键词： near-infrared spectroscopy,Soybean,NIR spectroscopy,MLR,transmission Raman spectroscopy,multiple liner regression,PLS regression
更新于2025-09-09 09:28:46
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Replacement Orthogonal Wavelengths Selection as a new method for multivariate calibration in spectroscopy
摘要： Wavelength selection is a critical step in multivariate calibration. Variable selection methods are used to find the most relevant variables, leading to improved prediction accuracy, while simplifying both the built models and their interpretation. In addition, different spectrophotometer designs and measurement principles result in non-destructive technologies applied in many fields, such as agriculture, food chemistry and pharmaceutics. However, an on-chip or portable device does not allow acquiring data from a large number of wavelengths. Therefore, the most informative combination of a limited number of variables should be selected. The Replacement Orthogonal Wavelengths Selection (ROWS) method is described here as a new method. This algorithm aims at selecting as few wavelengths as possible, while keeping or improving the prediction performance of the model, compared to when no variable selection is applied. The ROWS is applied to several near infrared spectroscopic data sets leading to improved analytical figures of merits upon wavelength selection in comparison to a built PLS model using entire spectral range. The performance of the ROWS-MLR method was compared to the FCAM-PLS method. The resulting models are not significantly different from those of FCAM-PLS; however, it involves a significantly smaller amount of variables.
关键词： Replacement Method,ROWS-MLR,Orthogonalization,FCAM-PLS,Near-Infrared spectroscopy
更新于2025-09-09 09:28:46