This study revealed that it was possible to determine the enantiomeric composition of with multivariate regression models of spectral data obtained by ordinary UV–vis spectrophotometry of enantiomeric guest–host complexes. The total 60 samples involving three concentration levels of metalaxyl as low, medium and high were prepared for spectral collecting. Four methods of modeling were subsequently proposed and compared including two common ways and two compensating ways for variations in total analyte concentration. Firstly, without normalization robust modeling was failed to achieve while employing the medium concentration levels as calibration and the other two levels as a validation. The same case occurred when full-cross validation was conducted. Besides, two enhanced methods were developed to account for the systematic variation. One of which normalized the spectra with respect to the total concentration of enantiomeric, along with spectral data, as a variable in the statistical analysis. The other one ignored variations in total concentration, relying on the specific band normalization to sort out any variations due to total concentration differences. The results clearly demonstrated that the spectra according to concentration provided the acceptable predictive ability in determining enantiomeric composition.Graphical abstractHighlights► We use sucrose as a chiral selector to determine the D-met by UV–vis spectral data with PLS models. ► We develop the model by concentration normalization to eliminate the variations the differences. ► We introduce a convenient normalization method–RBN to standardize the spectral of all samples. ► We use the model developed by the medium one to predict the high and low levels and the results are well.

A simple and efficient directly suspended droplet microextraction (DSDME) has been developed to extract and pre-concentrate organochlorine and pyrethrin pesticides from tea samples prior to analysis by a gas chromatography–electron capture detector (GC–ECD). The optimal experimental conditions of DSDME were: 100 μL isooctane exposed for 15 min to 5 mL of the tea aqueous sample stirred at 1100 rpm. For most of the target analytes, the optimal pretreatment of DSDME processes led to no significant interference of tea matrices. The approach was applied to the determination of organochlorine and pyrethroid pesticides in tea samples, with a linearity range of 0.0005–2 μg/mL. The relative recoveries of all the pesticides ranged between 80.0% and 120.8% with relative standard deviations (RSDs) in the range of 0.8–19.9% (n = 5). The limits of detections (LODs) ranged between 0.04 and 1 μg/L for all the target pesticides.Highlights► This manuscript presents the simple and efficient method of DSDME. ► We selected tea samples with a complex matrix employing DSDME technique. ► DSDME possesses potential in the fast analysis of trace compounds in tea samples.