A gas chromatographic-mass spectrometric method was developed, validated and demonstrated by measuring the levels of nitrogen mustard hydrolysis products in the urine collected from dosed rats. The recovery values for trimethylsilyl derivatives of EDEA and MDEA are between 82–95% and 88–112%, respectively. In vivo studies performed by using three different doses (0.5 mg/kg, 1.0 mg/kg, and 2.0 mg/kg) of HN2 base of nitrogen mustard. MDEA concentrations were between 43.1–232.2 ng/mL. The limit of detection (S/N = 3) values are 2.5 ng/mL and 1.6 ng/mL for EDEA and MDEA, respectively, and the precision of the method in terms of RSD is between 5–8%.

A simple, cost effective hydride generation atomic florescence spectrometry (HG-AFS) method was used for determination of total arsenic (As) in poultry and calf meat samples. The samples were digested in long necked glass digestion tubes using concentrated HNO3, HClO4 and H2SO4 as a mixture. The volume of acids (HNO3, HClO4) and the amount of sample to be used for digestion were optimized to achieve appropriate digestion. The accuracy of the proposed HG-AFS method was tested with certified reference material (DOLT 3 Dogfish Liver, NRC, Canada) and obtained results were in good agreement with certified value. The method limit of detection (LOD) value was calculated as 0.3 ng/g and dynamic range was 25 – 5000 pg/ml. Arsenic concentrations of poultry and calf meat samples were determined accurately by using aqueous calibration standards. Totally 31 samples (calf, chicken and turkey) obtained from local markets were analyzed. It was found that the average As concentration in calf meat (12.1 ± 3.9 ng/g) was significantly higher than the poultry samples whereas the arsenic concentrations were similar in turkey (3.1 ± 1.2 ng/g) and chicken (2.8 ± 1.1 ng/g) samples. In addition, dietary intake estimation of arsenic through consumption of calf and poultry meat was calculated and according to the gathered results daily intake of arsenic via calf meat was almost two times higher than poultry meat.

In this study, the classification performance of the extreme gradient boosting algorithm on a small-sized dataset was improved by using a synthetically generated dataset created with kernel density estimation to classify wine samples. The concentration of 29 elements in wine samples produced in Turkey (domestic) and Italy (imported) was determined by inductively coupled plasma-mass spectrometry and obtained results were used to generate the dataset. Classification of wine samples was firstly assessed with extreme gradient boosting, which is known for overfitting in small-sized datasets, resulting in poor classification performance. To improve the classification performance, a synthetic dataset was created and the algorithm was trained on the synthetic dataset instead of the original dataset. With the proposed method, the accuracy of the model was improved from 76.7% to 81.7%. The precision values for Turkish and Italian wines were increased from 78.4% to 84.1% and from 70.9% to 79.4%, respectively. The variable importance determined by the extreme gradient boosting algorithm showed that beryllium and cesium were significantly more important compared to other elements followed by tin, phosphorus, cobalt, lead, calcium, copper, zinc, and aluminum as the top 10 elements to classify Turkish and Italian wine samples.

In this study the silver species were identified that leached into artificial sweat from commercially available clothing products that were in direct contact with skin. Single particle-inductively coupled plasma mass-spectrometry (SP-ICP-MS) is capable of simultaneously determining silver nanoparticles (AgNPs) and dissolved silver (Ag⁺), especially when dwell time is used in microsecond range. The effect of Ag⁺ concentration for the determination of 40 and 70 nm AgNPs was investigated and AgNPs were characterized accurately up to 2 ng mL⁻¹ and 15 ng mL⁻¹ Ag⁺, respectively. Under the optimized conditions, the limit of detection for nanoparticle number concentration (LOD_conc) and the limit of detection for nanoparticle size (LOD_size) of the proposed SP-ICP-MS method were 5.5 × 10² particle mL⁻¹ and 17 nm, respectively. According to the results, silver was present in artificial sweat solution in the dissolved form, which was confirmed by a spectrofluorometric assay and ultracentrifugation. In addition, the released quantity of silver from underwear was higher than from socks, which may be related to the differences in the textiles and their manufacturing processes.