Analysis of Organochlorine Pesticide Residues in Various Vegetable Oils Collected in Chinese Markets.

Organochlorine pesticides (OCPs), chemicals frequently used in agriculture, are a group of highly toxic and persistent organic pollutants. This study assesses the distribution and congener profiles of residual OCPs in 11 types of vegetable oils collected from Chinese markets. All samples were extracted using the modified QuEChERS method prior to analysis by gas chromatography-triple quadrupole mass spectrometry. The sesame oil samples had the highest concentration of OCPs, within the range of 15.30-59.38 ng/g, whereas the peanut oil samples had the lowest OCP concentrations, within the range of 10.83-35.65 ng/g. The possible effect of the processing technology on the pesticide residues in these vegetable oils was also evaluated. It was found that the pressing method leaves more OCPs in vegetable oils than the aqueous extraction and cold-pressing, but the result for leaching was not obvious. In light of the obtained results, it was estimated that the average daily intake of different pesticides is between 0.01 and 2.20 ng/kg bw/day for urban and rural households. Hence, it can be affirmed that, given the amount of the concentration of OCPs detected in the vegetable oils collected from Chinese markets, there are no obvious health risks for urban and rural households by intake.

Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approaches.

Three machine learning algorithms as least-squares support vector machine (LSSVM), random forest (RF) and Gaussian process (GP) were used to model the quantitative structure-retention relationship (QSRR) for predicting and explaining the retention behavior of proteome-wide peptides in the reverse-phase liquid chromatography. Peptides were parameterized using CODESSA approach and 145 descriptors were obtained for each peptide, including diverse structural information such as constitutional, topological, geometrical and physicochemical property. Based upon that, the nonlinear LSSVM, RF and GP as well as another sophisticated linear method (partial least-squares regression (PLS)) were employed in the QSRR model development. By a series of systematic validations as internal cross-validation, external test and Monte Carlo cross-validation, the stability and predictive power of the constructed models were confirmed. Results show that regression models developed using nonlinear approaches such as LSSVM, RF and GP predict better than linear PLS models. Considering the retention times used in this work were measured in different columns and thus have a relatively large uncertainty (reproducibility within 7%), the optimal statistics obtained from GP modeling are satisfactory, with the coefficients of determination (R2) for training set and test set of 0.894 and 0.866, respectively.

Comprehensive comparison of eight statistical modelling methods used in quantitative structure-retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome.

In this study, we propose a new peptide characterization method that gives attention to both the amino acid composition and the residue local environment. Using this approach, structural characteristics of peptides derived from Escherichia coli proteome were parameterized and, based upon that, the performance profile of eight statistical modelling methods were validated rigorously and compared comprehensively by applying them to modelling relationship between the sequence structure and retention ability for 816 experimentally measured peptides and to predicting normalized retention times for 121,273 unmeasured peptides in liquid chromatography. Results show that the regression models constructed by nonlinear approaches are more robust and predictable but time-consuming than those by linear ones. In these modelling methods, Gaussian process and back-propagation neural network possess the best stability, unbiased ability and predictive power, thus they can be used to accurately model the peptide structure-retention relationships; multiple linear regression and partial least squares regression perform worse compared to nonlinear modelling techniques but they are computationally efficient, so they are promising candidates for solving the qualitative problems involved in massive data. In addition, by investigating the descriptor importance in different models we found that the amino acid composition presents a significantly linear correlation with the retention time of peptides, whereas the residue environment is mainly correlated nonlinearly with peptide retention. The polar Arg and strongly hydrophobic amino acids such as Leu, Ile, Phe, Trp and Val are the critical factors influencing peptide retention behavior.