The metal contents of eleven black tea samples, four cultivated in Iran and seven imported, and their tea infusions were determined. Twelve elements consisting toxic metals (Al, As, Pb, Cr, Cd, and Ni) and essential mineral elements (Fe, Zn, Cu, Mn, Ca, and Mg) were analyzed using inductively coupled plasma atomic emission spectroscopy (ICP-AES). Al, Ca, Mg, and Mn ranged in black tea leaves at mg g(-1) levels, while Cr, Fe, Ni, Cu, Zn were at microg g(-1) levels. Analysis of variance showed no statistically significant differences among most elements determined in cultivated and imported black teas in Iran except for Ni and Cu. The extraction efficiency of each element into tea infusions was evaluated. The solubility of measured metals in infusion extracts varied widely and ranged from 0 to 59.3%. Among the studied elements, Cr, Pb, and Cd showed the lowest rates of solubility and Ni had the highest rates of solubility. The amount of toxic metals and essential mineral elements that one may take up through consumption of black tea infusion was estimated. The amount of realizing each element into tea infusions and acceptable daily intake, for safety consumption of black tea, was compared.

Accurate computational prediction of melting points and aqueous solubilities of organic compounds would be very useful but is notoriously difficult. Predicting the lattice energies of compounds is key to understanding and predicting their melting behavior and ultimately their solubility behavior. We report robust, predictive, quantitative structure-property relationship (QSPR) models for enthalpies of sublimation, crystal lattice energies, and melting points for a very large and structurally diverse set of small organic compounds. Sparse Bayesian feature selection and machine learning methods were employed to select the most relevant molecular descriptors for the model and to generate parsimonious quantitative models. The final enthalpy of sublimation model is a four-parameter multilinear equation that has an r(2) value of 0.96 and an average absolute error of 7.9 ± 0.3 kJ.mol(-1). The melting point model can predict this property with a standard error of 45° ± 1 K and r(2) value of 0.79. Given the size and diversity of the training data, these conceptually transparent and accurate models can be used to predict sublimation enthalpy, lattice energy, and melting points of organic compounds in general.

Aqueous solubility is a very important physical property of small molecule drugs and drug candidates but also one of the most difficult to predict accurately. Aqueous solubility plays a major role in drug delivery and pharmacokinetics. It is believed that crystal lattice interactions are important in solubility and that including them in solubility models should improve the accuracy of the models. We used calculated values for lattice energy and sublimation enthalpy of organic molecules as descriptors to determine whether these would improve the accuracy of the aqueous solubility models. Multiple linear regression employing an expectation maximization algorithm and a sparse prior (MLREM) method and a nonlinear Bayesian regularized artificial neural network with a Laplacian prior (BRANNLP) were used to derive optimal predictive models of aqueous solubility of a large and highly diverse data set of 4558 organic compounds over a normal ambient temperature range of 20-30 °C (293-303 K). A randomly selected test set and compounds from a solubility challenge were used to estimate the predictive ability of the models. The BRANNLP method showed the best statistical results with squared correlation coefficients of 0.90 and standard errors of 0.645-0.665 log(S) for training and test sets. Surprisingly, including descriptors that captured crystal lattice interactions did not significantly improve the quality of these aqueous solubility models.

A simple, economical and reproducible benzoylation procedure was developed for the derivatization of biogenic amines (BAs) prior to high-performance liquid chromatography determination. The significant factors affecting biogenic amine benzoylation yield were optimized by central composite design. The derivatized BAs were extracted from basic aqueous solutions by 2-undecanol for direct HPLC injection. The obtained optimal conditions significantly decreased reagent consumption, uncertainty and time of analysis. The optimized method was used for further determination of BAs in real samples of nonalcoholic beers. The method showed good linearity (correlation coefficients > 0.997) and good recoveries (from 87.3 to 96.3%). The repeatability and reproducibility of the method were >4.6% and >6.7% respectively. Moreover, the detection limits of BAs were calculated between 0.02 and 0.09 mg ml À1 in non-alcoholic beers samples.