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.

Rapid determination of baicalin and total baicalein content in Scutellariae radix by ATR-IR and NIR spectroscopy.

In this study methods for the quantification of baicalin and total baicalein in Scutellariae radix with near infrared (NIR) spectroscopy and attenuated-total-reflectance mid-infrared (ATR-IR) spectroscopy in hyphenation with multivariate analysis were developed and compared. The reference analysis was performed by high performance liquid chromatography coupled to diode array detection (HPLC-DAD). Different pretreatments like standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivative Savitzky-Golay were applied on the spectra to optimize the calibrations. A principal component analysis was performed with both spectroscopic methods to distinguish wild and cultivated samples. Quality parameters obtained for test-set calibration models of ATR-IR spectroscopy (baicalin: standard error of prediction (SEP)=1.31, ratio performance to deviation (RPD)=2.91 and R(2)=0.88; total baicalein: SEP=1.02, RPD=3.24 and R(2)=0.89) and NIR spectroscopy (baicalin: SEP=1.50, RPD=2.54 and R(2)=0.88; total baicalein: SEP=1.19, RPD=2.76 and R(2)=0.84) demonstrate that both spectroscopic techniques in combination with multivariate analysis are successful tools for the quantification of baicalin and total baicalein in Scutellariae radix, but it was found that ATR-IR spectroscopy provides higher accuracy in the given application. Furthermore it was proved that wild and cultivated samples can be distinguished by 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.

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.

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.