A simple, low cost GC/MS method for the sub-nanogram per litre measurement of organotins in coastal water

Possibilities and Limitations of ICP-Spectrometric Determination of the Total Content of Tin and Its Inorganic and Organic Speciations in Waters with Different Salinity Levels-Part 2: Separate Determination of Inorganic and Organic Speciations of Tin

In this study, determination of the inorganic and organic forms of tin in waters of different salinities is considered. The possibility of the separation of speciations of tin using liquid-liquid extraction (LLE); precipitation with fluorides, iodides, ammonia, and iron (III) chloride; and sorption of organotin compounds (OTCs) was studied. The LLE and analyte precipitation methods proved to be ineffective. Inorganic and organic forms of tin were separated by the sorption of OTCs using silica gel sorbent Diapak C18. Under optimized conditions, a technique for the separate determination of the forms of tin in natural waters was developed. The technique combines hydride generation and microwave mineralization of solutions followed by ICP spectrometry. The inorganic forms of tin were determined after their solid-phase separation from organotin compounds. The lower limits of analyte quantification were 0.03 μg/L (ICP-MS) and 0.05 μg/L (ICP-OES), which provide separate determinations of inorganic and organic forms of tin in waters with different salinities. The content of OTCs in water was determined by subtracting the inorganic concentration from the total concentration of tin. The technique will allow a comprehensive assessment of the toxicological impact of tin speciations on the aquatic ecosystem.

Quantification of tributyltin in seawater using triple isotope dilution gas chromatography-inductively coupled plasma mass spectrometry achieving high accuracy and complying with European Water Framework Directive limits

A triple isotope dilution GC-ICPMS method for the determination of tributyltin (TBT) was developed and validated to meet the European Water Framework Directive (WFD) requirements. The validation procedure involved the evaluation of trueness, precision (repeatability, intermediate precision), limit of detection (LOD) and limit of quantification (LOQ), stability, measurement uncertainty and traceability studies. The method is one of the most sensitive methods published to date with good accuracy, 103% average recovery in the range with %RSDs of 2.8-6.7%. A LOD value of 0.015 ng L^-1 for the TBT cation was achieved with a sample volume of 12 mL seawater. TBT was derivatized using 20 µL sodium tetraethylborate solution (0.05% NaBEt₄) to make volatile for GC-ICPMS. Measurement uncertainty was in the range of 4.8-13% which was achieved through dissolution of tributyltinchloride (TBTCl) in 1-propanol, a low-volatility solvent combined with the use of a triple isotope dilution (ID) calibration technique. Isotope dilution calibration was performed by adding ¹¹⁷Sn isotopically enriched TBT to the seawater samples. The stability test results showed that TBT concentration was stable for three months in seawater samples after passing through a 0.2 µm filter and stored in amber glass bottles at 4°C. The response surface methodology (RSM) approach was successfully implemented to provide optimal conditions for large volume injection (LVI) to obtain the maximum analytical signal. The key variables selected in the experimental design were evaporation time, evaporation temperature, carrier flow, and injection speed. This method was applied to seawater samples collected from the Bay of Izmit, Kocaeli, Turkey, where TBT pollution has not been measured yet.

Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies

BACKGROUND

High throughput metabolomics makes it possible to measure the relative abundances of numerous metabolites in biological samples, which is useful to many areas of biomedical research. However, missing values (MVs) in metabolomics datasets are common and can arise due to both technical and biological reasons. Typically, such MVs are substituted by a minimum value, which may lead to different results in downstream analyses.

RESULTS

Here we present a modified version of the K-nearest neighbor (KNN) approach which accounts for truncation at the minimum value, i.e., KNN truncation (KNN-TN). We compare imputation results based on KNN-TN with results from other KNN approaches such as KNN based on correlation (KNN-CR) and KNN based on Euclidean distance (KNN-EU). Our approach assumes that the data follow a truncated normal distribution with the truncation point at the detection limit (LOD). The effectiveness of each approach was analyzed by the root mean square error (RMSE) measure as well as the metabolite list concordance index (MLCI) for influence on downstream statistical testing. Through extensive simulation studies and application to three real data sets, we show that KNN-TN has lower RMSE values compared to the other two KNN procedures as well as simpler imputation methods based on substituting missing values with the metabolite mean, zero values, or the LOD. MLCI values between KNN-TN and KNN-EU were roughly equivalent, and superior to the other four methods in most cases.

CONCLUSION

Our findings demonstrate that KNN-TN generally has improved performance in imputing the missing values of the different datasets compared to KNN-CR and KNN-EU when there is missingness due to missing at random combined with an LOD. The results shown in this study are in the field of metabolomics but this method could be applicable with any high throughput technology which has missing due to LOD.