Development and application of Fourier-transform infrared chemical imaging of tumour in human tissue.

Fourier-transform infrared (FT-IR) based mapping and imaging is a fast emerging technology which is being increasingly applied to investigate tissues in the high-throughput mode. The high resolution close to the cellular level, the possibility to determine the bio-distribution of molecules of interest (proteins, peptides, lipids, carbohydrates) without any pre-treatment and the offer to yield molecular structure information have brought evidence that this technique allows to gain new insights in cancer pathology. Thus, several individual mainly protein and peptide cancer markers ("biomarkers") can be identified from FT-IR tissue images, enabling accurate discrimination between healthy and tumour areas. Optimal data acquisition (spatial resolution, spectral resolution, signal to noise ratio), classification, and validation are necessary to establish practical protocols that can be translated to the qualitative and quantitative clinical routine analysis. Thereby, the development of modern fast infrared imaging systems has strongly supported its acceptance in clinical histopathology. In this review, the necessity of analysis based on global cancer statistics, instrumental setups and developments, experimental state of the art are summarised and applications to investigate different kinds of cancer (e.g., prostate, breast, cervical, colon, oral cavity) are shown and discussed in detail.

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.

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.

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

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 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, cluster and multivariate analysis hyphenated to thin layer chromatography for the analysis of amino acids.

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), R(2), 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 degrees 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 microl), 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.