At-Line Monitoring of the Extraction Process of Rosmarini Folium via Wet Chemical Assays, UHPLC Analysis, and Newly Developed Near-Infrared Spectroscopic Analysis Methods.

The present study demonstrates the applicability of at-line monitoring of the extraction process of Rosmarinus officinalis L. leaves (Rosmarini folium) and the development of near-infrared (NIR) spectroscopic analysis methods. Therefore, whole dried Rosmarini folium samples were extracted by maceration with 70% (v/v) ethanol. For the experimental design three different specimen-taking plans were chosen. At first, monitoring was carried out using three common analytical methods: (a) total hydroxycinnamic derivatives according to the European Pharmacopoeia, (b) total phenolic content according to Folin-Ciocalteu, and (c) rosmarinic acid content measured by UHPLC-UV analysis. Precision validation of the wet chemical assays revealed a repeatability of (a) 0.12% relative standard deviation (RSD), (b) 1.1% RSD, and (c) 0.28% RSD, as well as an intermediate precision of (a) 4.1% RSD, (b) 1.3% RSD, and (c) 0.55% RSD. The collected extracts were analyzed with a NIR spectrometer using a temperature-controlled liquid attachment. Samples were measured in transmission mode with an optical path length of 1 mm. The combination of the recorded spectra and the previously obtained analytical reference values in conjunction with multivariate data analysis enabled the successful establishment of partial least squares regression (PLSR) models. Coefficients of determination (R2) were: (a) 0.94, (b) 0.96, and (c) 0.93 (obtained by test-set validation). Since Pearson correlation analysis revealed that the reference analyses correlated with each other just one of the PSLR models is required. Therefore, it is suggested that PLSR model (b) be used for monitoring the extraction process of Rosmarini folium. The application of NIR spectroscopy provides a fast and non-invasive alternative analysis method, which can subsequently be implemented for on- or in-line process control.

Critical Evaluation of NIR and ATR-IR Spectroscopic Quantifications of Rosmarinic Acid in Rosmarini folium Supported by Quantum Chemical Calculations.

The present study evaluates the analytical performance of near infrared as well as attenuated total reflection infrared spectroscopy for the determination of the rosmarinic acid content in Rosmarini folium. Therefore, the recorded near infrared and attenuated total reflection infrared spectra of 42 milled Rosmarini folium samples were correlated with reference data (range: 1.138-2.199 rosmarinic acid %) obtained by HPLC analysis. Partial least squares regression models were established as a quantitative multivariate data analysis tool. Evaluation via full cross-validation and test set validation resulted in comparable performances for both techniques: near infrared [coefficient of determination: 0.90 (test set validation); standard error of cross-validation: 0.060 rosmarinic acid %; standard error of prediction: 0.058 rosmarinic acid %] and attenuated total reflection infrared [coefficient of determination: 0.91 (test set validation); standard error of cross-validation: 0.063 rosmarinic acid %; standard error of prediction: 0.060 rosmarinic acid %]. Furthermore, quantum chemical calculations were applied to obtain a theoretical infrared spectrum of rosmarinic acid. Good agreement to the spectrum of pure rosmarinic acid was achieved in the lower wavenumber region, whereas the higher wavenumber region showed less compliance. The knowledge of the vibrational modes of rosmarinic acid was used for the association with the high values of the regression coefficient plots of the established partial least squares regression models.

Double orthogonal sample design scheme and corresponding basic patterns in two-dimensional correlation spectra for probing subtle spectral variations caused by intermolecular interactions.

This paper introduces a new approach named double orthogonal sample design scheme (DOSD) to probe intermolecular interactions based on a framework of two-dimensional (2D) correlated spectroscopy. In this approach, specifically designed concentration series are selected according to the mathematical analysis on orthogonal vectors to generate useful 2D correlated spectra. As a result, the interfering portion can be completely removed from both synchronous and asynchronous spectra, and complementary information concerning intermolecular interactions can be obtained from the set of 2D spectra. A model system, where intermolecular interactions occur between two solutes in a solution, is used to investigate the behavior of 2D correlated spectra generated by using the DOSD approach. Simulation results demonstrate that the resultant spectral patterns can reflect subtle spectral variation in bandwidths, peak positions, and absorptivities brought about by intermolecular interaction, which are hardly visualized in conventional 1D spectra because of the severe band-overlapping problem. The ability to reveal a subtle variation in a characteristic peak in detail by using the DOSD approach provides a new opportunity to understand the nature of intermolecular interactions from a molecular structural point of view. Intermolecular interactions between iodine and benzene in CCl(4) solutions were investigated by using the proposed DOSD approach to prove the applicability of the DOSD method in real chemical systems.

Modified secured principal component regression for detection of unexpected chromatographic features in herbal fingerprints.

Secured principal component regression is modified for the qualitative analysis of chromatographic fingerprint data sets of herbal samples with residual concentrations. After chromatographic shift-correction and autoscaling are performed on the data, this modified secured principal component regression (msPCR) can detect unexpected chromatographic features in various herbal fingerprints. The successful application of msPCR to two real herbal medicines of Erigeron breviscapus from different geographical origins and Ginkgo biloba from various sources or vendors demonstrates that the proposed method can detect reasonably unexpected features differing from the regulars or not being modeled. From a chemical point of view, the causes have also been explained to corroborate the results. Moreover, it presents a viable approach for the qualitative evaluation of diverse herbal objects with a regular class of chromatographic fingerprints.

The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics.

A new procedure has been developed for the classification and quantification of the adulteration of pure olive oil by soya oil, sun flower oil, corn oil, walnut oil and hazelnut oil. The study was based on a chemometric analysis of the near-infrared (NIR) spectra of olive-oil mixtures containing different adulterants. The adulteration of olive oil was carefully carried out gravimetrically in a 4 mm quartz cuvette, starting with pure olive oil in the cuvette first. NIR spectra of the 525 adulterated mixtures were measured in the region of 12,000-4000 cm(-1). The spectra were subjected batch wise to multiplicative signal correction (MSC) before calculating the principal component (PCA) models. The MSC-corrected data were subjected to Savitzky-Golay smoothing and a mean normalization procedure before developing partial least-squares calibration (PLS) models. The results revealed that the models predicted the adulterants, corn oil, sun flower oil, soya oil, walnut oil and hazelnut oil involved in olive oil with error limits +/-0.57, +/-1.32, +/-0.96, +/-0.56 and +/-0.57% weight/weight, respectively. Furthermore, the PCA developed models were able to classify unknown adulterated olive oil mixtures with almost 100% certainty. Quantification of the adulterants was carried out using their respective PLS models within the same error limits as mentioned above.

Immunoassay using probe-labelling immunogold nanoparticles with silver staining enhancement via surface-enhanced Raman scattering.

This paper reports a novel immunoassay based on surface-enhanced Raman scattering (SERS) and immunogold labelling with silver staining enhancement. Immunoreactions between immunogold colloids modified by a Raman-active probe molecule (e.g., 4-mercaptobenzoic acid) and antigens, which were captured by antibody-assembled chips such as silicon or quartz, were detected via SERS signals of Raman-active probe molecule. All the self-assembled steps were subjected to the measurements of ultraviolet-visible (UV-vis) spectra to monitor the formation of a sandwich structure onto a substrate. The immunoassay was performed by a sandwich structure consisting of three layers. The first layer was composed of immobilized antibody molecules of mouse polyclonal antibody against Hepatitis B virus surface antigen (PAb) on a silicon or quartz substrate. The second layer was the complementary Hepatitis B virus surface antigen (Antigen) molecules captured by PAb on the substrate. The third layer was composed of the probe-labelling immunogold nanoparticles, which were modified by mouse monoclonal antibody against Hepatitis B virus surface antigen (MAb) and 4-mercaptobenzoic acid (MBA) as the Raman-active probe on the surface of gold colloids. After silver staining enhancement, the antigen is identified by a SERS spectrum of MBA. A working curve of the intensity of a SERS signal at 1585 cm(-1) due to the [small nu](8a) aromatic ring vibration of MBA versus the concentration of analyte (Antigen) was obtained and the non-optimized detection limit for the Hepatitis B virus surface antigen was found to be as low as 0.5 [micro sign]g mL(-1).