Standard Error Square Research Articles

Chemometric Methods for the Quantification of Crystalline Tacrolimus in Solid Dispersion by Powder X‐Ray Diffractrometry

The objective of this study was to develop powder X-ray diffraction (XRPD) chemometric model for quantifying crystalline tacrolimus from solid dispersion (SD). Three SDs (amorphous tacrolimus component) with varying drug to excipient ratios (24.4%, 6.7%, and 4.3% drug) were prepared. Placebo SDs were mixed with crystalline tacrolimus to make their composition equivalent to three SD (crystalline tacrolimus component). These two components were mixed to cover 0%-100% of crystalline drug. Uniformity of the sample mixtures was confirmed by near-infrared chemical imaging. XRPD showed three distinct peaks of crystalline drug at 8.5°, 10.3°, and 11.2° (2θ), which were nonoverlapping with the excipients. Principal component regressions (PCR) and partial least square (PLS) regression used in model development showed high R(2) (>0.99) for all the mixtures. Overall, the model showed low root mean square of standard error, standard error, and bias, which was smaller in PLS than PCR-based model. Furthermore, the model performance was evaluated on the formulations with known percentage of crystalline drug. Model-calculated crystalline drug percentage values were close to actual value. Therefore, these studies strongly suggest the application of chemometric-XRPD models as a quality control tool to quantitatively predict the crystalline drug in the formulation.

Application of chemometric methods to differential scanning calorimeter (DSC) to estimate nimodipine polymorphs from cosolvent system

The focus of this study was to evaluate the applicability of chemometrics to differential scanning calorimetry data (DSC) to evaluate nimodipine polymorphs. Multivariate calibration models were built using DSC data from known mixtures of the nimodipine modification. The linear baseline correction treatment of data was used to reduce dispersion in thermograms. Principal component analysis of the treated and untreated data explained 96% and 89% of the data variability, respectively. Score and loading plots correlated variability between samples with change in proportion of nimodipine modifications. The R2 for principal component regression (PCR) and partial lease square regression (PLS) were found to be 0.91 and 0.92. The root mean square of standard error of the treated samples for calibration and validation in PCR and PLS was found to be lower than the untreated sample. These models were applied to samples recrystallized from a cosolvent system, which indicated different proportion of modifications in the mixtures than those obtained by placing samples under different storage conditions. The model was able to predict the nimodipine modifications with known margin of error. Therefore, these models can be used as a quality control tool to expediently determine the nimodipine modification in an unknown mixture.

Surface properties of bentonite and illite complexes with humus acids

AbstractIn this study, the reaction between humus acids (humic acid and β-humus fulvic acid fraction) and bentonite and illite was studied at a variety of pH values. The degree of reaction was determined from the specific surface area and molar energy of adsorption. Characteristic parameters of adsorption isotherms for the formation of a mono-layer of the adsorbent, such as the constant C from the BET equation, mono-layer capacity (Nm), and standard error square (R2), were also included in the study. After the addition of humus acids, the specific surface area of illite and bentonite decreased at all pH values, reaching a minimum at pH 4. This indicates different degrees of reaction of humus acids with the clays and, probably, partial hydrophobization of the materials. The degree of reaction of humus acids with the minerals depended on the pH and, for certain combinations, it was highest at pH 4. This value is relatively close to that at which the humus acid fractions in question precipitate from solution.

Flood Peak Estimates from Limited At‐Site Historic Data

Estimates of peak flood discharge on ungauged watersheds are often determined from regional relationships. This approach, however, ignores sitespecific information that might be available from sources such as historic high water marks, etc. A systematic procedure to estimate a given quantile flood, employing the limited at‐site historic data, is developed for the ungauged watershed. In this procedure, the estimates of discharge based upon historic sitespecific evidence are combined with independent regional estimates by inverse variance weighting, to obtain weighted estimates at the ungauged site. The variance of flow determined by the regional method is shown to be adequately represented by the square of the standard error of regression. The variance of flow determined on the basis of historic evidence is represented by the square of the regional standard error determined for gauged sites within the study region. The methodology based on inverse variance weighting appears to give better results than eith...