A Simple and Highly Sensitive Method for Quantitative Detection of Methyl Paraben and Phenol in Cosmetics Using Derivative Spectrophotometry and Multivariate Chemometric Techniques

Application of Rank Annihilation Factor Analysis for Antibacterial Drugs Determination by Means of pH Gradual Change-UV Spectral Data

The main objective of this study was to develop a simple and efficient spectrophotometric technique combined with chemometrics for the simultaneous determination of sulfamethoxazole (SMX) and trimethoprim (TMP) in drug formulations. Specifically, we sought: (i) to evaluate the potential use of rank annihilation factor analysis (RAFA) to pH gradual change spectrophotometric data in order to provide sufficient accuracy and model robustness; and (ii) to determine SMX and TMP concentration in drug formulations without tedious pre-treatments such as derivatization or extraction techniques which are time-consuming and require hazardous solvents. In the proposed method, the spectra of the sample solutions at different pH values were recorded and the pH-spectra bilinear data matrix was generated. On these data, RAFA was then applied to estimate the concentrations of SMX and TMP in synthetic and real samples. Applying RAFA showed that the two drugs could be determined simultaneously with concentration ratios of SMX to TMP varying from 1:30 to 30:1 in the mixed samples (concentration range is 1-30 µg mL^-1 for both components). The limits of detection were 0.25 and 0.38 µg mL^-1 for SMX and TMP, respectively. The proposed method was successfully applied to the simultaneous determination of SMX and TMP in some synthetic, pharmaceutical formulation and biological fluid samples. In addition, the means of the estimated RSD (%) were 1.71 and 2.18 for SMX and TMP, respectively, in synthetic mixtures. The accuracy of the proposed method was confirmed by spiked recovery test on biological samples with satisfactory results (90.50-109.80%).

Quantification of heavy metal pollution for environmental assessment of soil condition

The aim of this study was to quantify heavy metal pollution for environmental assessment of soil quality using a flexible approach based on multivariate analysis. The study was conducted using 241 soil samples collected from agricultural, urban and rangeland areas in northwestern Iran. The heavy metals causing soil pollution (SP) in the study area were determined. The efficiency of principal component analysis (PCA) and discriminate analysis (DA) were compared to identify the critical heavy metals causing SP. Fourteen soil pollution indices were developed using non-linear and linear scoring equations and different integration methods. The indices were validated using the integrated pollution and potential ecological risk indices and by comparing their ability to detect soil pollution risk levels. Chromium (Cr), lead (Pb), Zinc (Zn) and copper (Cu) were identified as the significant pollutant elements using PCA, and the main pollutant elements identified using DA comprised cadmium (Cd), Zn and Pb. DA yielded a better data set for indexing SP and indicated high pollution risks for Cd > Pb > Zn. Sources of heavy metals were reliably identified using PCA, variation assessment and interrelationship evaluation of soil variables. Cr, nickel (Ni) and cobalt (Co) were found to have geogenic sources, and anthropogenic sources controlled the accumulation of Pb, Zn, Cd and Cu in soil. Linear function and additive integration were the best scoring and integrating methods for indexing HMP. The multivariate analysis provided a reliable and rapid method for indexing and mapping soil HMP.