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The extended multiplicative signal correction (EMSC) preprocessing method allows a separation of physical light-scattering effects from chemical (vibrational) light absorbance effects in spectra from, for example, powders or turbid solutions. It is here applied to diffuse near infrared transmission (NIT) spectra of mixtures of wheat gluten (protein) and starch (carbohydrate) powders, linearized by conventional log(1/T). Without any correction for uncontrolled light scattering variation between the powder samples, these absorbance spectra could give reasonable predictions of the analyte (gluten), but only when using multivariate calibration with a much more complex model than expected. Standard MSC preprocessing did not work for these data at all; it removed too much analyte information. However, the EMSC preprocessing yielded powder spectra that obeyed Beer's Law more or less as if they had been obtained from transparent liquid solutions, apparently by isolating the chemical light absorption from additive, multiplicative, and wavelength-dependent effects of uncontrolled light-scattering variations. The model-based EMSC and its converse, the extended inverted signal correction (EISC), gave rather complete descriptions of the diffuse absorbance spectra and virtually indistinguishable performance in the calibration set and the test set of samples.

No abstract

In this study, near-infrared (NIR) transmittance and Raman spectroscopy chemometric calibrations of the active substance content of a pharmaceutical tablet were developed using partial least-squares regression (PLS). Although the active substance contained the strongly Raman active C≡N functional group, the best results were obtained with NIR transmittance, which highlights the difference between (microscopic) surface sampling and whole tablet diffuse transmittance sampling. The tablets exist in four dosages with only two different concentrations of active substance (5 mg (5.6% w/w), and 10, 15, and 20 mg (8.0% w/w) active substance per tablet). A calibration on all four dosages resulted in a prediction error expressed as the root mean squared error of cross-validation (RMSECV) of 0.30% w/w for the NIR transmittance calibration. The corresponding error when using Raman spectra was 0.56% w/w. Specially prepared calibration batches covering the range 85–115% of the nominal content for each dosage were added to the first sample set, and NIR transmittance calibrations on this set—containing coated as well as uncoated tablets—gave a further reduction in prediction errors to 0.21–0.289% w/w. This corresponds to relative prediction errors (RMSECV/ynom) of 2.6–3.7%. This is a reasonably low error when compared to the error of the chromatographic reference method, which was estimated to 3.5%.

No abstract

Predicting individual fatty acids (FA) in bovine milk from Fourier transform infrared (FT-IR) measurements is desirable. However, such predictions may rely on covariance structures among individual FA and total fat content. These covariance structures may change with factors such as breed and feed, among others. The aim of this study was to estimate how spectral variation associated with total fat content and breed contributes to predictions of individual FA. This study comprised 890 bovine milk samples from 2 breeds (455 Holstein and 435 Jersey). Holstein samples were collected from 20 Danish dairy herds from October to December 2009; Jersey samples were collected from 22 Danish dairy herds from February to April 2010. All samples were from conventional herds and taken while cows were housed. Moreover, in a spiking experiment, FA (C14:0, C16:0, and C18:1 cis-9) were added (spiked) to a background of commercial skim milk to determine whether signals specific to those individual FA could be obtained from the FT-IR measurements. This study demonstrated that variation associated with total fat content and breed was responsible for successful FT-IR-based predictions of FA in the raw milk samples. This was confirmed in the spiking experiment, which showed that signals specific to individual FA could not be identified in FT-IR measurements when several FA were present in the same mixture. Hence, predicted concentrations of individual FA in milk rely on covariance structures with total fat content rather than absorption bands directly associated with individual FA. If covariance structures between FA and total fat used to calibrate partial least squares (PLS) models are not conserved in future samples, these samples will show incorrect and biased FA predictions. This was demonstrated by using samples of one breed to calibrate and samples of the other breed to validate PLS models for individual FA. The 2 breeds had different covariance structures between individual FA and total fat content. The results showed that the validation samples yielded biased predictions. This may limit the usefulness of FT-IR-based predictions of individual FA in milk recording as indirect covariance structures in the calibration set must be valid for future samples. Otherwise, future samples will show incorrect predictions.

The hydration behavior of two model disaccharides, methyl-alpha-D-maltoside (1) and methyl-alpha-D-isomaltoside (2), has been investigated by a comparative 10 ns molecular dynamics study. The detailed hydration of the two disaccharides was described using three force fields especially developed for modeling of carbohydrates in explicit solvent. To validate the theoretical results the two compounds were synthesized and subjected to 500 MHz NMR spectroscopy, including pulsed field gradient diffusion measurements (1: 4.0. 10(-6) cm(2). s(-1); 2: 4.2. 10(-6) cm(2). s(-1)). In short, the older CHARMM-based force field exhibited a more structured carbohydrate-water interaction leading to better agreement with the diffusional properties of the two compounds, whereas especially the alpha-(1-->6) linkage and the primary hydroxyl groups were inaccurately modeled. In contrast, the new generation of the CHARMM-based force field (CSFF) and the most recent version of the AMBER-based force field (GLYCAM-2000a) exhibited less structured carbohydrate-water interactions with the result that the diffusional properties of the two disaccharides were underestimated, whereas the simulations of the alpha-(1-->6) linkage and the primary hydroxyl groups were significantly improved and in excellent agreement with homo- and heteronuclear coupling constants. The difference between the two classes of force field (more structured and less structured carbohydrate-water interaction) was underlined by calculation of the isotropic hydration as calculated by radial pair distributions. At one extreme, the radial O em leader O pair distribution function yielded a peak density of 2.3 times the bulk density in the first hydration shell when using the older CHARMM force field, whereas the maximum density observed in the GLYCAM force field was calculated to be 1.0, at the other extreme.

A new extended method for separating, e.g., scattering from absorbance in spectroscopic measurements, extended inverted signal correction (EISC), is presented and compared to multiplicative signal correction (MSC) and existing modifications of this. EISC preprocessing is applied to near-infrared transmittance (NIT) spectra of single wheat kernels with the aim of improving the multivariate calibration for protein content by partial least-squares regression (PLSR). The primary justification of the EISC method is to facilitate removal of spectral artifacts and interferences that are uncorrelated to target analyte concentration. In this study, EISC is applied in a general form, including additive terms, multiplicative terms, wavelength dependency of the light scatter coefficient, and simple polynomial terms. It is compared to conventional MSC and derivative methods for spectral preprocessing. Performance of the EISC was found to be comparable to a more complex dual-transformation model obtained by first calculating the second derivative NIT spectra followed by MSC. The calibration model based on EISC preprocessing performed better than models based on the raw data, second derivatives, MSC, and MSC followed by second derivatives.