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Honeysuckle flower is a common edible-medicinal food with significant anti-inflammatory efficacy. Process quality control of its ethanol precipitation is a topical issue in the pharmaceutical field. Near infrared (NIR) spectroscopy is commonly used for process quality analysis. However, establishing a robust and reliable quantitative model of complex process remains a challenge in industrial applications of NIR. In this paper, modeling design based on quality by design concept (QbD) was implemented for the ethanol precipitation process quality control of Honeysuckle flower. According to the 56 models' performances and 25 contour plots, quadratic model was the best with Radj2 increasing from 0.1395 to 0.9085, indicating the strong interaction among spectral pre-processing methods, variable selection methods, and latent factors. SG9 and CARS was an appropriate combination for modeling. Furthermore, spectral assignment method was creatively introduced for variable selection. Another 56 models' performances and 25 contour plots were established. Compared with the chemometric variable selection method, spectral assignment combined with QbD concept made a higher Rpre2 and a lower RMSEP. When the latent factors of PLS was small, Rpre2 of the model by spectral assignment increased from 0.9605 to 0.9916 and RMSEP decreased from 0.1555 mg/mL to 0.07134 mg/mL. This result suggests that the variable selected by spectral assignment is more representative and precise. This provided a novel modeling guideline for process quality control in PAT., Graphical abstract Unlabelled Image, Highlights • Quality by Design concept was conducted for NIR model design. • Strong synergic interactions among model parameters were discovered by QbD. • Spectral assignment was used to select variable instead of chemometric method. • A more robust model was established by spectral assignment combined with D-optimal.

A combination of multireference CASSCF/CASPT2 and time-dependent DFT (TD-B3P86) theoretical treatments was employed to test their predictions against recently proposed assignments of the vacuum-UV spectra of fluorene and its three heteroanalogues — dibenzofuran, carbazole, and dibenzothiophene — up to the ionization threshold. For the low-lying transitions, the theoretically based assignments are generally not problematic because of the well resolved bands, although, even in this region, the two methods yield some opposing predictions. Further on toward the vacuum region, the assignments prove increasingly challenging because of predicted crowding of transitions, many of which exhibit significant intensity. Some of the transitions in this region and beyond—toward the ionization thresholds—are thus necessarily assigned only tentatively. Overall, the two methods are frequently found to complement each other well, and equivalent transitions usually appear as bracketed from the high- (CASPT2) and low- (TD-B3P86) energy sides.

Near infrared (NIR) assignment of Isopsoralen was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. Yunkang Oral Liquid was applied to study Isopsoralen, the characteristic bands by spectral assignment as well as the bands by interval partial least squares (iPLS) and synergy interval partial least squares (siPLS) were used to establish partial least squares (PLS) model. The coefficient of determination in calibration [Formula: see text] were 0.9987, 0.9970 and 0.9982. The coefficient of determination in cross validation [Formula: see text] were 0.9985, 0.9921 and 0.9982. The coefficient of determination in prediction [Formula: see text] were 0.9987, 0.9955 and 0.9988. The root mean square error of calibration (RMSEC) were 0.27, 0.40 and 0.31 ppm. The root mean square error of cross validation (RMSECV) were 0.30, 0.67 and 0.32 ppm. The root mean square error of prediction (RMSEP) were 0.23, 0.43 and 0.22 ppm. The residual predictive deviation (RPD) were 31.00, 16.58 and 32.41. It turned out that the characteristic bands by spectral assignment had the same results with the chemometrics methods in PLS model. It provided guidance for NIR spectral assignment of chemical compositions in Chinese Materia Medica (CMM).

The general aim of this work is to obtain information about the adsorption geometry of several benzohydroxamic acid (BHA) derivatives deposited on copper oxide. The investigation is done by means of surface enhanced Raman spectroscopy (SERS). In order to interpret the experimental information, an assignment of the SERS spectra is carried out. The process of assignment is supported by theoretical spectra derived from quantum-chemical calculations. As the size of the molecules and the overall lack of symmetry of the adsorbate-substrate system hinder the transparent description of the molecular vibrations, the local symmetry of the benzene is used as a basic classification feature. Such an approach is justified because, according to the theoretical spectra, the vibrations typical for the free benzene ring are conserved in the studied molecules. The deformations of the substituent and hydroxamic groups are added as a secondary descriptive element. On the basis of the spectrum assignment, the common vibrations typical for all investigated compounds and those used for identification that are pertinent to each of them, are classified. The surface selection rule is then qualitatively applied in order to derive information about the adsorption geometry. Copyright © 2006 John Wiley & Sons, Ltd.

International audience; We consider the solvability of a vector moment problem associating it to the analysis of controllability for a certain delayed system of neutral type. In this way we succeeded to determine exactly the minimal interval on which the moment problem is solvable.

NMR crystallography has been around for half a century, but with the advent of NMR crystal structure determination protocols in the last decade it has shown perspectives that were not seen before. Amalgamation of NMR and crystal structure determination has been successful in predicting crystal structures de novo. Still, there are many challenges to make these methods universal and applicable to any molecular crystal at any stage of a structural investigation. Larger molecules still take too much time to be solved by the current methods up to the point of being impossible. Amorphous structures of molecular solids had not been solved in general for any method. Reaction mechanisms and structures involved in the formation of solids are still challenging to investigate due to their fast nature and the low concentration of the reaction intermediates in solution. The current NMR crystal structure determination protocols involve NMR at the final step of the candidate crystal structure selection, but one of the biggest bottlenecks is actually at the first steps of structure determination that involve selecting gas phase conformers that will later be used in the trial crystal structures. Here, we develop a series of new NMR methods to address these bottlenecks. We show how unambiguous constraints extracted from solid state NMR experiments can help to significantly reduce the initial conformer space and help to select conformers that correspond more closely to what is found in the crystal structure. We then show how machine learned chemical shifts can be included in the crystal structure determination process from the start, and we determine the crystal structure of two compounds, one of whom is polymorphic. Then, we show how machine learned shifts in combination with molecular dynamics can be used to solve the atomic level structure of an amorphous compound yielding insights into the hydrogen bonding and stabilization of the amorphous form. To further aid crystal structure determination, we present a new method to assign spectra based on machine learned chemical shifts and propose a new database for small organic molecular crystals that would help in the future crystallographic investigations. Finally, we investigate carbonate speciation by using dissolution DNP.