Fourier transform infrared imaging analysis in discrimination studies of squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) of the oral cavity and oropharynx represents more than 95% of all malignant neoplasms in the oral cavity. Histomorphological evaluation of this cancer type is invasive and remains a time consuming and subjective technique. Therefore, novel approaches for histological recognition are necessary to identify malignancy at an early stage. Fourier transform infrared (FTIR) imaging has become an essential tool for the detection and characterization of the molecular components of biological processes, such as those responsible for the dynamic properties of tumor progression. FTIR imaging is a modern analytical technique enabling molecular imaging of a complex biological sample and is based on the absorption of IR radiation by vibrational transitions in covalent bonds. One major advantage of this technique is the acquisition of local molecular expression profiles, while maintaining the topographic integrity of the tissue and avoiding time-consuming extraction, purification, and separation steps. With this imaging technique, it is possible to obtain unique images of the spatial distribution of proteins, lipids, carbohydrates, cholesterols, nucleic acids, phospholipids, and small molecules with high spatial resolution. Analysis and visualization of FTIR imaging datasets are challenging and the use of chemometric tools is crucial in order to take advantage of the full measurement. Therefore, methodologies for this task based on the novel developed algorithm for multivariate image analysis (MIA) are often necessary. In the present study, FTIR imaging and data analysis methods were combined to optimize the tissue measurement mode after deparaffinization and subsequent data evaluation (univariate analysis and MIAs). We demonstrate that it is possible to collect excellent IR spectra from formalin-fixed paraffin-embedded (FFPE) tissue microarrays (TMAs) of OSCC tissue sections employing an optimised analytical protocol. The correlation of FTIR imaging to the morphological tissue features obtained by histological staining of the sections demonstrated that many histomorphological tissue patterns can be visualized in the colour images. The different algorithms used for MIAs of FTIR imaging data dramatically increased the information content of the IR images from squamous cell tissue sections. These findings indicate that intra-operative and surgical specimens of squamous cell carcinoma tissue can be characterized by FTIR imaging.

Morphological and tissue characterization of the medicinal fungus Hericium coralloides by a structural and molecular imaging platform.

In this study the potential of new imaging techniques such as Magnetic Resonance Imaging (MRI), Matrix-Assisted Laser Desorption/Ionization (MALDI) profiling mass spectrometry ("MALDI Profiling") and Fourier Transform Infrared (FTIR) spectroscopic imaging was evaluated to study morphological and molecular patterns of the potential medicinal fungus Hericium coralloides. For interpretation, the MALDI profiling, FTIR imaging and MRI results were correlated with histological information gained from Scanning Electron Microscopy (SEM) and Light Microscopy (LM). Additionally we tested several evaluation processes and optimized the methodology for use of complex FTIR images to monitor molecular patterns. It is demonstrated that the combination of these spectroscopic methods enables to gain a more distinct picture concerning morphology and distribution of active ingredients. We were able to obtain high quality FTIR imaging and MALDI-profiling results and to distinguish different tissue types with their chemical ingredients. Beside this, we have created a 3-D reconstruction of a mature Hericium basidioma, based on the MRI dataset: analyses allowed, for the first time, a realistic approximation of the "evolutionary effectiveness" of this bizarrely formed basidioma type, concerning the investment of sterile tissue and its reproductive output (production of basidiospores).

Fourier transform infrared imaging analysis in discrimination studies of St. John's wort (Hypericum perforatum).

In the present study, Fourier transform infrared (FTIR) imaging and data analysis methods were combined to study morphological and molecular patterns of St. John's wort (Hypericum perforatum) in detail. For interpretation, FTIR imaging results were correlated with histological information gained from light microscopy (LM). Additionally, we tested several evaluation processes and optimized the methodology for use of complex FTIR microscopic images to monitor molecular patterns. It is demonstrated that the combination of the used spectroscopic method with LM enables a more distinct picture, concerning morphology and distribution of active ingredients, to be gained. We were able to obtain high-quality FTIR microscopic imaging results and to distinguish different tissue types with their chemical ingredients.

Vibrational spectroscopic methods for the overall quality analysis of washing powders.

The aim of this study was to compare and evaluate the ability of near infrared- (NIR), Raman- and attenuated-total-reflection infrared (ATR-IR) spectroscopy as tools for the identification of washing powder brands as well as for an overall quantitative analysis of all ingredients of the analyzed laundry detergents. The laundry detergents used in this work were composed of 22 different ingredients. For this purpose, principal component analysis (PCA) cluster models and partial least-squares (PLS) regression models were developed and different data pre-processing algorithms such as standard-normal-variate (SNV), multiplicative scatter correction (MSC), first derivative BCAP (db1), second derivative smoothing (ds2), smoothing Savitzky Golay 9 points (sg9) as well as different normalization procedures such as normalization between 0 and 1 (n01), normalization unit length (nle) or normalization by closure (ncl) were applied to reduce the influence of systematic disturbances. The performance of the methods was evaluated by comparison of the number of principal components (PCs), regression coefficient (r), Bias, Standard error of prediction (SEP), ratio performance deviation (RPD) and range error ratio (RER) for each calibration model. For each of the 22 ingredients separate calibration models were developed. Raman spectroscopy was suitable for the analysis of only two ingredients (dye transfer inhibitor 1 and surfactant 6) and it was not possible to record all Raman spectra due to high fluorescence. NIR and ATR-IR are powerful methods to analyze washing detergents with low numbers of PCs being necessary, regression coefficients of only little below 1, small Biases and SEPs compared to the range and high RPDs and RERs.

Comparison of NIR chemical imaging with conventional NIR, Raman and ATR-IR spectroscopy for quantification of furosemide crystal polymorphs in ternary powder mixtures.

The aim of this study was to evaluate the ability of near-infrared chemical imaging (NIR-CI), near-infrared (NIR), Raman and attenuated-total-reflectance infrared (ATR-IR) spectroscopy to quantify three polymorphic forms (I, II, III) of furosemide in ternary powder mixtures. For this purpose, partial least-squares (PLS) regression models were developed, and different data preprocessing algorithms such as normalization, standard normal variate (SNV), multiplicative scatter correction (MSC) and 1st to 3rd derivatives were applied to reduce the influence of systematic disturbances. The performance of the methods was evaluated by comparison of the standard error of cross-validation (SECV), R(2), and the ratio performance deviation (RPD). Limits of detection (LOD) and limits of quantification (LOQ) of all methods were determined. For NIR-CI, a SECVcorr-spec and a SECVsingle-pixel corrected were calculated to assess the loss of accuracy by taking advantage of the spatial information. NIR-CI showed a SECVcorr-spec (SECVsingle-pixel corrected) of 2.82% (3.71%), 3.49% (4.65%), and 4.10% (5.06%) for form I, II, III. NIR had a SECV of 2.98%, 3.62%, and 2.75%, and Raman reached 3.25%, 3.08%, and 3.18%. The SECV of the ATR-IR models were 7.46%, 7.18%, and 12.08%. This study proves that NIR-CI, NIR, and Raman are well suited to quantify forms I-III of furosemide in ternary mixtures. Because of the pressure-dependent conversion of form II to form I, ATR-IR was found to be less appropriate for an accurate quantification of the mixtures. In this study, the capability of NIR-CI for the quantification of polymorphic ternary mixtures was compared with conventional spectroscopic techniques for the first time. For this purpose, a new way of spectra selection was chosen, and two kinds of SECVs were calculated to achieve a better comparability of NIR-CI to NIR, Raman, and ATR-IR.

Simultaneous quantification of verbenalin and verbascoside in Verbena officinalis by ATR-IR and NIR spectroscopy.

Attenuated-total-reflectance infrared spectroscopy (ATR-IR) and near-infrared diffuse reflectance spectroscopy (NIR) in hyphenation with multivariate analysis was utilized to quantify verbenalin and verbascoside in Verbena officinalis. A new high performance liquid chromatography (HPLC) method as a reference was established and validated. For both vibrational spectroscopic methods test-set and cross validation were performed. Different data-pre-treatments like SNV, 1st and 2nd derivative were applied to remove systematic errors and were evaluated. Quality parameters obtained for the test-set validation revealed that ATR-IR (verbenalin: R(2)=0.94, RPD=4.23; verbascoside: R(2)=0.93, RPD=3.63) has advantages over NIR (verbenalin: R(2)=0.91, RPD=3.75; verbascoside: R(2)=0.80, RPD=2.35) in the given application.

Near infrared spectroscopy patents for the physicochemical characterization of nanomaterials: the road from production to routine high-throughput quality control.

The measurement of the physical and chemical ("physicochemical") properties of nanomaterials used in industry and science including chemistry, pharmacy, medicine, toxicology, etc., is time-consuming, expensive and requires a lot of experience of a well trained lab staff. Near-infrared spectroscopy (NIR; 4.000-12.000 cm(-1)), working in the wavelength region with the highest IR energy, allows obtaining multifactorial information of the material under investigation due to the occurrence of a high number of combination and overtone vibrations. Coupling of an optimized and well-designed measurement technique with multivariate data analysis (MVA) leads to a non-destructive, fast, reliable and robust novel NIR technique for the fast and non-invasive physicochemical characterization, which is suitable for high-throughput quality control due to the short analyses times of only a few seconds. In the following chapters, the patented basic NIR techniques full-filling these aims are introduced, described, summarized and critically discussed.

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.