Developing a highly selective method for preconcentration and determination of cobalt in water and nut samples using 1-(2-pyridylazo)-2-naphthol and UV-visible spectroscopy

Determination of cobalt in chamomile tea samples at trace levels by flame atomic absorption spectrophotometry after poly(vinyl alcohol)-magnetic hydrogel based dispersive solid phase extraction

In this study, an analytical strategy was proposed for the determination of cobalt at trace levels by using a flame atomic absorption spectrophotometry (FAAS) system after magnetic hydrogel based dispersive solid phase extraction (MH-DSPE). Poly(vinyl alcohol) based magnetic hydrogels (PVA-MH) were synthesized easily, quickly, and cost effectively in the laboratory and used as an adsorbent material in the microextraction process. Under the optimum experimental conditions, the limit of detection (LOD) and limit of quantitation (LOQ) values were recorded as 4.2 and 14.1 μg L^-1, respectively. To investigate the matrix effects on the analyte signal, spike experiments were performed using chamomile tea extracts and good recovery results were obtained between 85.7 and 113.8%. A 57.8-fold improvement was achieved in the detection power compared to that of a conventional FAAS system. The results obtained throughout all experimental studies demonstrated the applicability in addition to the accuracy of the method for the quantification of trace levels of cobalt with high accuracy in a chamomile tea matrix.

Switchable-hydrophilicity solvent-based liquid-phase microextraction in an on-line system: Cobalt determination in food and water samples

An on-line system employing switchable-hydrophilicity solvent-based liquid-phase microextraction (SHS-LPME) is described in this work. The method is based on the preconcentration of the species formed between cobalt and the reagent 1-nitroso-2-naphthol (NN), with subsequent detection by digital image colorimetry. The system's operation begins with the on-line mixture of sample, switchable solvent, and an alkaline agent in a reaction coil. Then the mixture is transported to an extraction chamber. The introduction of a proton donor leads to the passage of the solvent to its hydrophobic form, which allows phase separation. The rich phase is then directed to a glass tube, where detection is performed. Octanoic acid, sodium carbonate, and sulfuric acid were used as the extraction solvent, the alkaline agent, and the proton donor, respectively. Under optimized conditions, the method presented a detection limit of 0.8 μg L^-1 and an enrichment factor of 41. The precision obtained was 4.8% (20 μg L^-1). The accuracy of the method was tested by the analysis of Tomato Leaves certified reference material (NIST 1573a). The method was applied to the determination of cobalt in food, dietary supplements, and water samples. The method is presented as a green alternative and very accessible to the determination of cobalt in the analyzed samples.

Application of genetic algorithm and multivariate methods for the detection and measurement of milk-surfactant adulteration by attenuated total reflection and near-infrared spectroscopy

BACKGROUND

The adulteration of milk by hazardous chemicals like surfactants has recently increased. It conceals the quality of the product to gain profit. As milk and milk-based products are consumed by many people, novel analytical procedures are needed to detect these adulterants. This study focused on Fourier-transform infrared (FTIR) spectroscopy equipped with an attenuated total reflection (ATR) accessory, and near-infrared (NIR) spectroscopy for the determination of milk-surfactant adulteration using a genetic algorithm (GA) coupled with multivariate methods. The model surfactant was sodium dodecyl sulfate (SDS), and its concentration varied from 1.94-19.4 gkg^-1 in adulterated samples.

RESULTS

Prominent peaks in the spectral range of 5500-6400 cm^-1 , 1160-1260 cm^-1 and 1049-1080 cm^-1 may correspond to the sulfonate group in SDS. A genetic algorithm could significantly reduce the number of variables to almost one third by selecting the specific wavenumber region. Principal component analysis (PCA) for ATR and NIR data indicated separate clusters of samples in terms of the concentration level of SDS (P ≤ 0.05). Partial least squares regression (PLSR) was used to determine the maximum R² value for ATR and NIR data for calibration, cross-validation and prediction, which were 0.980, 0.972, 0.980, and 0.970, 0.937, and 0.956 respectively. The results showed apparent differences between unadulterated and adulterated samples using partial least squares-discriminant analysis (PLS-DA), which was validated by the permutation test.

CONCLUSION

The results clearly show the successful application of the proposed methods with multivariate analysis in the selection of variables, classification, clustering, and identification of the adulterant in amounts as low as 1.94 gkg^-1 in milk. © 2020 Society of Chemical Industry.