对在建标技术报告中正确贯彻《测量不确定度表示指南》的理解

针对计量人员在贯彻《测量不确定表示指南》,尤其是在撰写“建标技术报告”时遇到的一些体问题,进行了探讨。     

Simultaneous spectrophotometric determination of ethinylestradiol and levonorgestrel by partial least squares and principal component regression multivariate calibration

Two spectrophotometric methods for the determination of Ethinylestradiol (ETE) and Levonorgestrel (LEV) by using the multivariate calibration technique of partial least square (PLS) and principal component regression (PCR) are presented. In this study the PLS and PCR are successfully applied to quantify both hormones using the information contained in the absorption spectra of appropriate solutions. In order to do this, a calibration set of standard samples composed of different mixtures of both compounds has been designed. The results found by application of the PLS and PCR methods to the simultaneous determination of mixtures, containing 4–11 μg ml⁻¹ of ETE and 2–23 μg ml⁻¹ of LEV, are reported. Five different oral contraceptives were analyzed and the results were very similar to that obtained by a reference liquid Chromatographic method.     

Simultaneous Kinetic‐Spectrophotometric Determination of Hydrazine and its Derivatives by Partial Least Squares and Principle Component Regression Methods

Simultaneous kinetic‐spectrophotometric determination of a ternary mixture of hydrazine (HZ) and its derivatives by principal component regression (PCR) and partial least squares (PLS) calibration is described. The methods were based on the difference observed in the reduction rate of iron(III) with HZ, thiosemicarbazide (TSCZ) and phenylhydrazine (PHZ) in the presence of 2,2′‐bipyridine (Bpy). The colored complex of [Fe(Bpy)₃]²⁺ was formed in sodium dodecyl sulfate (SDS) as micellar media, and then monitored at 520 nm. The results showed that simultaneous determination of HZ, TSCZ and PHZ could be performed in their concentration ranges of 1.0–70.0, 0.2–6.0 and 0.1–10.0 μg mL^?1, respectively. The root mean squares errors of prediction (RMSEP) of HZ, TSCZ and PHZ were 0.719, 0.164 and 0.105 (for PLS) 0.788, 0.166 and 0.993 (for PCR), respectively. Both methods (PCR and PLS) were validated using a set of synthetic sample mixtures and then applied for simultaneous determination of HZ, TSCZ and PHZ in water samples.     

Multivariate calibration techniques applied to the spectrophotometric analysis of one-to-four component systems

The UV spectrophotometric analysis of a multicomponent mixture containing paracetamol, caffeine, tripelenamine and salicylamide by using multivariate calibration methods, such as principal component regression (PCR) and partial least-squares regression (PLS), was described. The calibration set was based on 47 reference samples, consisting of quaternary, ternary, binary and single-component mixtures, with the aim to develop models able to predict the concentrations of unknown samples containing as many as one-to-four components. The calibration models were optimized by an appropriate selection of the number of factors as well as wavelength ranges to be used for building up the data matrix and excluding any information about the interfering excipients included in pharmaceutics. The PCR and PLS models were compared and their predictive performance was inferred by a successful application to the assays of synthetic mixtures and pharmaceutical formulations.     

Some aspects of quality assessment of the k0-based method of NAA

Neutron activation analysis is one of the analytical techniques often used for certification of reference materials. The k₀-based method of instrumental neutron activation analysis can also be applied in intercomparison runs in the certification process and therefore it is desirable to know its accuracy in advance. Possible systematic errors related to the application of nuclear data at given neutron flux rate parameters, that can affect the uncertainties of the results obtained by this specific method, are elucidated and error propagation factors calculated for a typical irradiation position in the TRIGA Mark II reactor of the Jozef Stefan Institute. It was found that these uncertainties are at the level of 1–2% on the average.     

Modelling the thermal behaviour of carboxylic acid derivatives with cylcodextrins in the solid-state

The application of classical QSAR and molecular modelling to the inclusion complexation of natural and modified cyclodextrins (CDs) with carboxylic acid derivatives as guest molecules was examined. Information was available on the thermal behaviour, in the solid-state of benzoic acid (BA), salicylic acid (SA), and various substituted aminosalicylic acids (3-aminosalicylic acid, 3-ASA, 4-aminosalicylic acid, 4-ASA and 5-aminosalicylic acid, 5-ASA), as well as on the thermal behaviour of 1:1 molar ratio physical and kneaded mixtures of these acids with each of three different cyclodextrins, β-, (BCD) 2-hydroxypropyl-β-, (HPBCD) and γ-cyclodextrin (GCD). The thermal behaviour of the binary (1:1 stoichiometry) mixtures was modelled using stepwise multiple regression (SMR). Two models for the prediction of the percentage mass loss and enthalpy of dehydration of the physical mixtures were established with correlation coefficients (r) of 0.79 and 0.92, respectively. Decreased correlation in the thermal behaviour of kneaded mixtures indicated significant interaction and possible formation of inclusion complexes. This revised version was published online in July 2006 with corrections to the Cover Date.     

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

ABSTRACT

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.     

Autoignition temperature: comprehensive data analysis and predictive models

ABSTRACT

Here we report a new predictive model for autoignition temperature (AIT), an important physical parameter widely used to assess potential safety hazards of combustible materials. Available structure-AIT data extracted from different sources were critically analysed. Support vector regression (SVR) models on different data subsets were built in order to identify a reliable compound set on which a realistic model could be built. This led to a selection of the dataset containing 875 compounds annotated with AIT values. The thereupon-based SVR model performs reasonably well in cross-validation with the determination coefficient r ² = 0.77 and mean absolute error MAE = 37.8°C. External validation on 20 industrial compounds missing in the training set confirmed its good predictive power (MAE = 28.7°C).     

Measurement of lactate in whole human blood with near-infrared transmission spectroscopy

We have evaluated the potential of near-infrared spectroscopy (NIRS) as a technique for rapid analysis of lactate in whole blood. To test the NIRS technique, a comparison was made with a standard clinical method using whole blood samples taken from five exercising human subjects at three different stage of exercise. To expand lactate concentration within the physiological range, standard additions method was used to generate 45 unique data points. Spectra were collected over the 2050-2400 nm spectral range with a 1 mm optical path length quartz cell. Reference lactate concentrations in the samples were determined by enzymatic measurements. Estimates and calibration of the lactate concentration with NIRS was made using partial least squares (PLS) regression analysis and leave-N-out cross validation on second derivative spectra. Separate calibrations were determined from each of the subject samples and cumulative PRESS was used to determine the number of PLS factors in the final model. The results from the PLS model presented are generated from the five individual calibration coefficient vectors and provided a correlation coefficient of 0.978 and a standard error of cross validation of 0.65 mmol l⁻¹ between the enzymatic assay and the NIRS technique. To study the parameters that impact the spectra baseline and the correlation between the calculated model and the data, referenced measurements of lactate against baseline spectrum were made for each individual. A correlation coefficient of 0.992 and a standard error of cross validation of 0.21 mmol l⁻¹ were found. The results suggest that NIRS may provide a valuable tool to assess physiological status for both research and clinical needs.