Simultaneous determination of furosemide and carbamazepine in biological matrices by solvent bar microextraction combined with high-performance liquid chromatography-diode array detector and central composite design.

A sensitive and simple sample pretreatment method based on a two-phase solvent bar microextraction (SBME) technique coupled with HPLC-diode array detector (DAD) was developed for simultaneous extraction and determination of trace amounts of furosemide and carbamazepine in human urine and plasma samples. The significance of operational factors on carbamazepine and furosemide extraction efficiency % (EE%) was screened using full factorial design (FFD) while central composite design (CCD) was used to model the entire process. A quadratic model was found convenient to correlate the extraction EE% of selected drugs with dominant experimental factors. A Pareto chart was also used to examine the importance of factors on drugs' EE%. The analytical performance of the method in urine and plasma samples demonstrated good linearity (R2 ˃ 0.992) with detection limits ranging from 4.2 to 10.9 µg L-1 , and extraction recovery over 89.45% for both drugs in urine and plasma samples. A comparison against published methods was also performed and the results revealed that the developed method exhibits a confident sensitivity, feasible operation, and simple analysis for both drugs. Finally, the practicability of the validated SBME-HPLC-DAD method was demonstrated by successfully applying it to the analysis of furosemide and carbamazepine in real patient urine samples.

Removal of Cadmium, Copper, and Lead From Water Using Bio-Sorbent From Treated Olive Mill Solid Residue.

Olive Mill Solid Residue (OMSR) can be utilized as a bio-sorbent in wastewater treatment. Even though several studies on OMSR as a bio-sorbent were carried out, there is still a need to investigate a simple and relatively inexpensive OMSR treatment that increases pollutant removal. In this study; OMSR is used in batch experiments to remove toxic heavy metals from aqueous solutions including Cd2+, Cu2+, and Pb2+ ions. The effect of OMSR treatment (untreated; OMSR-U, treated with n-hexane; OMSR-H, and treated with water; OMSR-W) was investigated by chemical oxygen demand and cation exchange capacity. It was confirmed by both tests that OMSR-W was the best treatment. The same result was re-confirmed by batch uptake experiments of the heavy metal ions. Using OMSR-W as a bio-sorbent; the effect of several parameters such as pH, contact time, bio-sorbent concentration, metal ions concentration, and the presence of other metal species were studied to figure their influence on the metal ions uptake. The optimum conditions for single metal systems were found to occur at pH 5.5, an initial metal concentration of 50 mg/L, a shaking time of 60 minutes, a bio-sorbent concentration of 20 g/L. In binary metal ions solutions; Cd2+ uptake was increased in presence of Cu2+ or Pb2+. However, the uptake of Cu2+ and Pb2+ was decreased in presence of other metals. The equilibrium sorption data for single metal systems were described by the Langmuir isotherm model. The highest value of maximum uptake was found for Pb2+ (4.587 mg/g) followed by Cd2+ (4.525 mg/g) and Cu2+ (4.367 mg/g). These results show that OMSR-W, which has a very low economical value, could be used for the treatment of wastewater contaminated with heavy metals.

In vitro discrimination and classification of Microbial Flora of Poultry using two dispersive Raman spectrometers (microscope and Portable Fiber-Optic systems) in tandem with chemometric analysis.

Discrimination and classification of eight strains related to meat spoilage and pathogenic microorganisms commonly found in poultry meat were successfully carried out using two dispersive Raman spectrometers (Microscope and Portable Fiber-Optic systems) in combination with chemometric methods. Principal components analysis (PCA) and multi-class support vector machines (MC-SVM) were applied to develop discrimination and classification models. These models were certified using validation data sets which were successfully assigned to the correct bacterial species and even to the right strain. The discrimination of bacteria down to the strain level was performed for the pre-processed spectral data using a 3-stage model based on PCA. The spectral features and differences among the species on which the discrimination was based were clarified through PCA loadings. In MC-SVM the pre-processed spectral data was subjected to PCA and utilized to build a classification model. When using the first two components, the accuracy of the MC-SVM model was 97.64% and 93.23% for the validation data collected by the Raman Microscope and the Portable Fiber-Optic Raman system, respectively. The accuracy reached 100% for the validation data by using the first eight and ten PC's from the data collected by Raman Microscope and by Portable Fiber-Optic Raman system, respectively. The results reflect the strong discriminative power and the high performance of the developed models, the suitability of the pre-processing method used in this study and that the low accuracy of the Portable Fiber-Optic Raman system does not adversely affect the discriminative power of the developed models.