Environmental biomonitoring of essential and toxic elements in human scalp hair using accelerated microwave-assisted sample digestion and inductively coupled plasma optical emission spectroscopy.

Human scalp hair samples were collected and used to assess exposure to toxic elements and essential elements in the state of North Carolina, USA using accelerated microwave assisted acid digestion and inductively coupled plasma optical emission spectroscopy (ICP-OES). The figures-of-merit of the ICP-OES were appropriate for elemental analysis in scalp hair with detection limits as low as 0.0001 mg/L for Cd, good linearity (R2 > 0.9978), and percent recoveries that ranged from 96 to 106% for laboratory-fortified-blanks and 88-112% for sample spike recovery study. The concentrations of essential elements in scalp hair were larger than those of toxic elements, with Ca having the highest average concentration (3080 µg/g, s = 14,500, n = 194). Some of the maximum concentrations observed for As (65 µg/g), Ni (331 µg/g), Cd (2.96 µg/g), and Cr (84.6 µg/g) in individual samples were concerning, however. Samples were statistically analyzed to determine the influence of race, gender, smoking habits, or age on the elemental concentrations in scalp hair. Higher concentrations of essential elements were observed in the scalp hair of Caucasians, females, and non-smokers, and the differences were often significant at a 90% confidence level. Several pairs of essential elements, for example Ca-K, Ca-Mg, and Ca-Zn, were strongly correlated in Caucasian hair but uncorrelated in African-American hair. Similarly, essential elements were strongly correlated in female hair but weakly correlated in male hair. Toxic element pairs (As-Cd, As-Se, Pb-As, and Se-Cd) were strongly correlated in the hair of smokers but uncorrelated in that of non-smokers, suggesting that cigarette smoke is a common source of toxic elements in humans.

Simultaneous determination of estrogens (ethinylestradiol and norgestimate) concentrations in human and bovine serum albumin by use of fluorescence spectroscopy and multivariate regression analysis.

The endocrine disruption property of estrogens necessitates the immediate need for effective monitoring and development of analytical protocols for their analyses in biological and human specimens. This study explores the first combined utility of a steady-state fluorescence spectroscopy and multivariate partial-least-square (PLS) regression analysis for the simultaneous determination of two estrogens (17α-ethinylestradiol (EE) and norgestimate (NOR)) concentrations in bovine serum albumin (BSA) and human serum albumin (HSA) samples. The influence of EE and NOR concentrations and temperature on the emission spectra of EE-HSA EE-BSA, NOR-HSA, and NOR-BSA complexes was also investigated. The binding of EE with HSA and BSA resulted in increase in emission characteristics of HSA and BSA and a significant blue spectra shift. In contrast, the interaction of NOR with HSA and BSA quenched the emission characteristics of HSA and BSA. The observed emission spectral shifts preclude the effective use of traditional univariate regression analysis of fluorescent data for the determination of EE and NOR concentrations in HSA and BSA samples. Multivariate partial-least-squares (PLS) regression analysis was utilized to correlate the changes in emission spectra with EE and NOR concentrations in HSA and BSA samples. The figures-of-merit of the developed PLS regression models were excellent, with limits of detection as low as 1.6×10(-8) M for EE and 2.4×10(-7) M for NOR and good linearity (R(2)>0.994985). The PLS models correctly predicted EE and NOR concentrations in independent validation HSA and BSA samples with a root-mean-square-percent-relative-error (RMS%RE) of less than 6.0% at physiological condition. On the contrary, the use of univariate regression resulted in poor predictions of EE and NOR in HSA and BSA samples, with RMS%RE larger than 40% at physiological conditions. High accuracy, low sensitivity, simplicity, low-cost with no prior analyte extraction or separation required makes this method promising, compelling, and attractive alternative for the rapid determination of estrogen concentrations in biomedical and biological specimens, pharmaceuticals, or environmental samples.