The combined effects of pH, water activity (a(w)), oxygen (O2) and carbon dioxide (CO2) levels on growth and sporulation of 10 common food-borne fungi were studied. The use of a multivariate statistical method (PLS) for the analysis of data showed that the fungi could be grouped according to their physiological response to changes in the four tested factors. Carbon dioxide, a(w) and pH were found to be the most significant factors describing differences and similarities among the fungi. Maximal inhibitory effect of elevated levels of CO2 (5-25%) and decreased a(w) (0.99-0.95) varied among the 10 species from 6 to 77% and from 52 to 100%, respectively. Sporulation of the fungi was sensitive to all tested factors. Furthermore, interaction of CO2 and a(w) displayed a significant effect on sporulation. It was shown that different fungal species associated with the same ecosystem responded similarly to changes in the tested factors. Thus, fungi which are not phylogenetically related may be physiologically related or show a common strategy of life.

Here we demonstrate a facile, two step formation of silver core - gold shell (Ag–Au) nanostructures using microelectrodes and assess their performance as surface-enhanced Raman scattering (SERS) substrates to detect and quantify toxicants. Ag nanostructures, serving as the scaffolds for the bimetallic structures, were grown first by using electrochemical deposition on the edges of microelectrodes functionalized with the alkanethiol, 11-mercaptoundecanoic acid. Subsequently, different concentrations of HAuCl₄ were used to perform a galvanic reaction on the surfaces of the Ag nanostructures with aqueous droplets being placed on the microelectrodes for 10 min before the substrate was rinsed and dried. Lower HAuCl₄ concentrations were found to better preserve the fractal morphology of the formed Ag–Au nanostructures, while higher concentrations resulted in Ag–Au fragments. The SERS enhancement factor for the Ag–Au nanostructures was estimated to have a max value of 6.51 x 10⁵. Combining a data reduction technique with a linear classifier, both identification and quantification were demonstrated with 100% success. The toxicants thiram, thiabendazole, malachite green and biphenyl-4-thiol were all detected and identified at 1 ppm. Lastly, as a proof of concept, the Ag–Au nanostructures were transferred to a PDMS film resulting in a flexible SERS substrate capable of direct detection of thiram on an apple peel without any additional sample pre-treatment.

A green and innovative eutectic solvent based extraction method was proposed for the determination of trace level vanadium in water and food samples by graphite furnace atomic absorption spectrometry. In this extraction technique magnetic stirrer was used for preparation of eutectic solvent by mixing of zinc chloride and acetamide at different molar ratios. Extraction capability of eutectic solvent was increased by adding a non ionic surfactant (Triton X-114) to enhanced phase transfer ratio, to significantly increase the recovery of hydrophobic complex of vanadium with ammonium pyrrolidine dithiocarbamate. A multivariate technique was applied to evaluate the important extraction parameters, which plays important role for optimum recovery of the targeted analyte by proposed extraction method. Multivariate techniques such as (factorial design and central composite design) were applied to screening out the most significant extraction parameters and optimized them. Under optimized extraction conditions, limit of detection and enhancement factor were found to be 0.01 µg L⁻¹ and 64.6, respectively. The relative standard deviation for the determination of trace level vanadium at 0.32 µg L⁻¹ concentration, was achieved to be <3.0% (n = 10). Validity and accuracy of the proposed extraction method was checked by analysis of certified reference materials of Canadian lake water and tomato leaves with % age recovery >98%. The eutectic solvent extraction method was successfully applied for the determination of the trace level vanadium in real water samples of different sources and acid digested food samples, collected from different locations of Tokat city, Turkey.

A multivariate method based on solvent terminated dispersive liquid–liquid microextraction was developed for the determination of Cu²⁺ ions in aqueous samples. In the proposed approach, di-2-ethylhexylphosphoric acid, xylene and acetone were used as chelating agent, dispersive and extraction solvents, respectively. The effects of various factors on the extraction efficiency such as extraction and dispersive solvent volumes, salt addition and pH were studied using central composite design (CCD) and artificial neural networks coupled bees algorithm (ANN-BA). Upon comparison of these techniques, ANN-BA model was considered to be better optimization method due to its higher percentage relative recovery (about 5%) as compared to the CCD approach. The linear range and the limits of detection (S/N = 3) and quantitation (S/N = 10) were 0.22–140, 0.08 and 0.22 µg L⁻¹, respectively. Under the optimal conditions, the recoveries for real samples spiked with 0.1 and 0.3 mg L⁻¹ were in the range of 85–98%.