N. Heigl

Near-infrared reflection spectroscopy (NIRS) as a successful tool for simultaneous identification and particle size determination of amoxicillin trihydrate

A successful application of NIR spectroscopy (NIRS) in combination with multivariate data analysis (MVA) for the simultaneous identification and particle size determination of amoxicillin trihydrate particles was developed. Particle size analysis was ascertained by NIRS in diffuse reflection mode on different particle size fractions of amoxicillin trihydrate with D90 particle diameters ranging from 6.9 to 21.7 μm. The present problem of fractionating the powder into good enough size fractions to achieve a stable calibration model was solved. By probing dried suspensions measurement parameters were optimized and further combined with the best suitable chemometric operations. Thereby the quality of established regression models could be improved considerably. A linear coherence between particle size and absorbance signal was found at specific wavenumbers. Satisfactory clustering by particle size was achieved by principal component analysis (PCA) whereas partial least squares regression (PLSR) and principal component regression (PCR) was compared for quantitatively calibrating the NIRS data. PLSR turned out to predict unknown test samples slightly better than PCR.

Near Infrared Spectroscopy for Polymer Research, Quality Control and Reaction Monitoring:

This review covers recent applications of near infrared (NIR) spectroscopy in the determination of physico-chemical and morphological parameters of polymeric materials. Near infrared measurements in the diffuse reflection mode are highlighted, which analyse the structural parameters such as porosity, surface area and particle size. Fundamentals and applications of the technique are discussed and examples of quantitative and qualitative analysis are explained. Various approaches like on- and in-line techniques, bulk measurements and kinetic studies for recording spectra are discussed. Furthermore, this review addresses the development of calibrations, which allow for the differentiation and quantification of materials with varying physical and morphological properties. Parameters like constitution, composition and crystallinity have a strong affect on the material characteristics. Therefore, chemical, physical and mechanical properties of synthetic as well as natural substances, such as polymeric composites and cotton or wool, need to be studied in-depth. To sum up, NIR spectroscopy has been developed as a flexible, robust and high-throughput analytical method that can be combined with chemometric and multivariate data analysis for fast and reliable screening in material science.

Simultaneous determination of the micro-, meso-, and macropore size fractions of porous polymers by a combined use of Fourier transform near-infrared diffuse reflection spectroscopy and multivariate techniques.

Fourier transform near-infrared (FT-NIR) diffuse reflection spectroscopy was used in combination with principal component analysis and partial least-squares regression to simultaneously determine the physical and the chemical parameters of a porous poly(p-methylstyrene-co-1,2-bis(p-vinylphenyl)ethane) (MS/BVPE) monolithic polymer. Chemical variations during the synthesis of the polymer material can alter the pore volume and pore area distributions within the polymer scaffold. Furthermore, mid-infrared and near-infrared (NIR) spectroscopic chemical imaging was implemented as a tool to assess the uniformity of the samples. The presented study summarizes the comparative results derived from the spectral FT-NIR data combined with chemometric techniques. The relevance of the interrelation of physical and chemical parameters is highlighted whereas the amount of MS/BVPE (%, v/v) and the quantity (%) of micropores (diameter, d < 6 nm), mesopores (6 nm < d < 50 nm), and macropores (50 nm < d < 200 nm) could be determined with one measurement. For comparison of the quantitative data, the standard error of prediction (SEP) was used. The SEP for determining the MS/BVPE amount in the samples showed 0.35%, for pore volume quantiles 1.42-8.44%, and for pore area quantiles 0.38-1.45%, respectively. The implication of these results is that FT-NIR spectroscopy is a suitable technique for the screening of samples with varying physicochemical properties and to quantitatively determine the parameters simultaneously within a few seconds.

Near infrared spectroscopy, cluster and multivariate analysis hyphenated to thin layer chromatography for the analysis of amino acids

Summary.A method based on near-infrared spectroscopy (NIRS) was developed for the rapid and non-destructive determination and quantification of solid and dissolved amino acids. The statistical results obtained after optimisation of measurement conditions were evaluated on the basis of statistical parameters, Q-value (quality of calibrations), R2, standard error of estimation (SEE), standard error of prediction (SEP), BIAS applying cluster and different multivariate analytical procedures. Experimental optimisation comprised the selection of the highest suitable optical thin-layer (0.5, 1.0, 1.5, 2.0, 2.5, 3.0 mm), sample temperature (10–30 °C), measurement option (light fibre, 0.5 mm optical thin-layer; boiling point tube; different types of cuvettes) and sample concentration in the range between 100 and 500 ppm. Applying the optimised conditions and a 115-QS Suprasil® cuvette (V = 400 µl), the established qualitative model enabled to distinguish between different dissolved amino acids with a Q-value of 0.9555. Solid amino acids were investigated in the transflectance mode, allowing to differentiate them with a Q-value of 0.9155. For the qualitative and quantitative analysis of amino acids in complex matrices NIRS was established as a detection system directly onto the plate after prior separation on cellulose based thin-layer chromatography (TLC) sheets employing n-butanol, acetic acid and distilled water at a ratio of 8:4:2 (v/v/v) as an optimised mobile phase. Due to the prior separation step, the established calibration curve was found to be more stable than the one calculated from the dissolved amino acids. The found lower limit of detection was 0.01 mg/ml. Finally, this optimised TLC-NIRS method was successfully applied for the qualitative and quantitative analysis of L-lysine in apple juice. NIRS is shown not only to offer a fast, non-destructive detection tool but also to provide an easy-to-use alternative to more complicated detection methods such as mass spectrometry (MS) for qualitative and quantitative TLC analysis of amino acids in crude samples.

Quantification of Low-Density and High-Density Lipoproteins in Human Serum by Material Enhanced Infrared Spectroscopy (MEIRS)

A key risk factor in the development of atherosclerosis is a high concentration of serum low density lipoprotein (LDL)-cholesterol. The main purpose of this study was to assess the LDL and high density lipoprotein (HDL) content in human serum by employing near-infrared (NIR) spectroscopy and multivariate calibration techniques. Initially a qualitative principal component analysis (PCA) based cluster model was generated to evaluate the feasibility of NIRS for classifying and identifying the LDL and HDL-cholesterol. Therefore TiO(2) beads were used as an adsorbent for selectively immobilizing LDL and HDL-cholesterol and further analysing the incubated and washed samples via NIR diffuse reflection spectroscopy. A principle component regression (PCR) model of 24 LDL standards in a range from 500 - 3000 ppm (clinical value is 1500 ppm) and a partial least squares regression (PLSR) model of 25 HDL standards in a range from 100 - 1000 ppm (clinical value is 400 ppm) were computed. Furthermore, the wavenumber region between 4000 cm(-1) and 7240 cm(-1) was found comprising the main spectral information regarding the TiO(2)-LDL and TiO(2)-HDL composites. The regression coefficients (r) for LDL and HDL were > 0.99 (calibration curve) and > 0.97 (validation curve), respectively. The PCR model of TiO(2)-LDL showed a standard error of estimation (SEE) of 122.80 ppm and a standard error of prediction (SEP) of 121.15 ppm while the PLSR model of TiO(2)-HDL showed 47.70 and 47.14 ppm, respectively. In order to determine the concentration of HDL in real serum samples, LDL was removed by adding a precipitation reagent containing 10 mg/mL magnesium dextran-sulfate, followed by incubation and centrifugation. The pretreated serum samples were predicted by the PLSR model while the standard deviation (SD) from the reference to the NIR predicted values of six test samples in a concentration range from 500 - 2500 ppm showed < 10 %. These results indicate the usefulness of the NIR spectroscopy (NIRS) as a potential alternative or even supplementary clinical method for the quick determination of LDL and HDL in human serum.

Near infrared spectroscopy compared to liquid chromatography coupled to mass spectrometry and capillary electrophoresis as a detection tool for peptide reaction monitoring

Peptide interaction is normally monitored by liquid chromatography (LC), liquid chromatography coupled to mass spectrometry (LC-MS), mass spectrometric (MS) methods such as MALDI-TOF/MS or capillary electrophoresis (CE). These analytical techniques need to apply either high pressure or high voltages, which can cause cleavage of newly formed bondages. Therefore, near infrared reflectance spectroscopy (NIRS) is presented as a rapid alternative to monitor the interaction of glutathione and oxytocin, simulating physiological conditions. Thereby, glutathione can act as a nucleophile with oxytocin forming four new conjugates via a disulphide bondage. Liquid chromatography coupled to UV (LC-UV) and mass spectrometry via an electrospray ionisation interface (LC-ESI-MS) resulted in a 82% and a 78% degradation of oxytocin at pH 3 and a 5% and a 7% degradation at pH 6.5. Capillary electrophoresis employing UV-detection (CE-UV) showed a 44% degradation of oxytocin. LC and CE in addition to the NIRS are found to be authentic tools for quantitative analysis. Nevertheless, NIRS proved to be highly suitable for the detection of newly formed conjugates after separating them on a thin layer chromatography (TLC) plate. The recorded fingerprint in the near infrared region allows for a selective distinct qualitative identification of conjugates without the need for expensive instrumentation such as quadrupole or MALDI-TOF mass spectrometers. The performance of the established NIRS method is compared to LC and CE; its advantages are discussed in detail.

Carbon based sample supports and matrices for laser desorption/ ionization mass spectrometry.

Laser desorption/ionization mass spectrometry (LDI-MS) is a widespread and powerful technique for mass analysis allowing the soft ionization of molecules such as peptides, proteins and carbohydrates. In many applications, an energy absorbing matrix has to be added to the analytes in order to protect them from being fragmented by direct laser beam. LDI-MS in conjunction with matrix is commonly referred as matrix-assisted LDI (MALDI). One of the striking disadvantages of this method is the desorption of matrix molecules, which causes interferences originating from matrix background ions in lower mass range (< 1000 Da). This has been led to the development of a variety of different carbon based LDI sample supports, which are capable of absorbing laser light and simultaneously transfering energy to the analytes for desorption. Furthermore carbon containing sample supports are used as carrier materials for the specific binding and preconcentration of molecules out of complex samples. Their subsequent analysis with MALDI mass spectrometry allows performing studies in metabolomics and proteomics. Finally a thin layer of carbon significantly improves sensitivity concerning detection limit. Analytes in low femtomole and attomole range can be detected in this regard. In the present article, these aspects are reviewed from patents where nano-based carbon materials are comprehensively utilized.

Nano-structured support materials, their characterisation and serum protein profiling through MALDI/TOF-MS

Summary.In the bioanalytical era, novel nano-materials for the selective extraction, pre-concentration and purification of biomolecules prior to analysis are vital. Their application as affinity binding in this regard is needed to be authentic. We report here the comparative application of derivatised materials and surfaces on the basis of nano-crystalline diamond, carbon nanotubes and fullerenes for the analysis of marker peptides and proteins by material enhanced laser desorption ionisation mass spectrometry MELDI-MS. In this particular work, the emphasis is placed on the derivatization, termed as immobilised metal affinity chromatography (IMAC), with three different support materials, to show the effectiveness of MELDI technique. For the physicochemical characterisation of the phases, near infrared reflectance spectroscopy (NIRS) is used, which is a well-established method within the analytical chemistry, covering a wide range of applications. NIRS enables differentiation between silica materials and different fullerenes derivatives, in a 3-dimensional factor-plot, depending on their derivatizations and physical characteristics. The method offers a physicochemical quantitative description in the nano-scale level of particle size, specific surface area, pore diameter, pore porosity, pore volume and total porosity with high linearity and improved precision. The measurement takes only a few seconds while high sample throughput is guaranteed.

Near-infrared spectroscopic study on guest-host interactions among G0-G7 amine-terminated poly(amidoamine) dendrimers and porous silica materials for simultaneously determining the molecular weight and particle diameter by multivariate calibration techniques.

The guest-host interactions of poly(amidoamine) (PAMAM) dendrimers and porous silica surfaces were investigated by near-infrared (NIR) diffuse reflection spectroscopy. G0-G7 of amine-terminated PAMAM (PAMAM-NH2) dendrimers were analyzed comprising early, mid, and late generations. For early stages, the adsorption process of the partly protonated dendrimers to the negatively charged silica surface strongly depends on the size/shape characteristics of the guest (PAMAM-NH2 dendrimers) and host (porous silica) materials. G0-G4 (15-45 A) show smaller particle sizes than the pore diameter of the silica (60 A) and thus have access to the interior surface of the host material. For mid and later stages (G5-G7; 54-81 A) only low amounts of the dendrimers adsorb to the silica surface due to the inaccessibility to the interior surface. The loading capacity of the silica material with adsorbed PAMAM-NH(2) was evaluated by means of capillary zone electrophoresis (CZE), whereas deviations from the theoretical to the effective particle size and molecular weight (MW) was determined by gas-phase electrophoretic mobility molecular analysis (GEMMA) and matrix-assisted laser desorption/ionization linear time-of-flight mass spectrometry (MALDI-lin TOF-MS). Deviations from the theoretical to the actual values showed a maximum of 13.8% and 28.0% for the particle size and MW, respectively. The NIR absorption spectra show a distinct band at 4932 cm(-1) (nu(sym) (NH) + amide II) due to the adsorbed dendrimers. It was found that the absorbance tends to increase with decreasing generation number. On this basis multivariate calibration was performed with the theoretical data and the data obtained by GEMMA and MALDI-lin TOF-MS. All in all, the calculated partial least-squares regression (PLSR) model containing the GEMMA/MALDI-lin TOF-MS reference values showed better results than the models exclusively calculated from the theoretical values. This indicates that the theoretical values do not imply the structural imperfections arising during the synthesis that may be present in the PAMAM-NH2 dendrimers.

Near-Infrared Diffuse Reflection Spectroscopy and Multivariate Calibration Hyphenated with Thin-Layer Chromatography for Quality Control of a Phytomedicine and Simultaneous Quantification of Methoxylated Flavones

By use of near infrared (NIR) spectroscopy hyphenated to thinlayer chromatography (TLC) for analysis of methoxylated flavones in a phytomedicine we sought to achieve two objectives: first, to establish a method for rapid, qualitative identification of five methoxylated flavones, denoted G1, G2, G3, G4, and G5, in order of their RF values in normal-phase TLC, and, second, to produce a quantitative model for analysis of G4 (3′,4′,5′-trimethoxyflavone), the compound most representative of Primula veris flowers in phytomedicine. To provide appropriate reference analytical data for building the multivariate cluster and partial least-squares regression (PLS) model, TLC was performed on alumina with n-hexane-ethyl acetate 70:30 (v/v) as mobile phase. Forty-four spectra of eleven independent phytomedicine samples were analyzed with five scans to generate a qualitative cluster model based on PCA (principle-components analysis) that enabled differentiation between G1–G5 on the basis of their methoxylation pattern. This PLS model, in the calibration range between 0 and 1000 mg L−1, enabled quantification of G4 with a standard error of cross validation (SECV), <54.61 mg L−1. The possibility of conducting qualitative and quantitative analysis simultaneously by use of this method revealed NIRS to be an efficient alternative to conventional modes of detection used for analysis of G1–G5, especially in phytomedicines.