Evaluation of Evaporation Fluxes for Pesticides and Low Volatile Hazardous Materials Based on Evaporation Kinetics of Net Liquids.

Evaporation is the phase transition process that plays a significant role in many spheres of life and science. Volatilization of hazardous materials, pesticides, petroleum spills, etc., impacts the environment and biosphere. Predicting evaporation fluxes under specific environmental conditions is challenging from theoretical and empirical points of view. A new practical method for estimating fluxes is proposed based on our experimental results and previously published data. It is demonstrated that some parameters in theoretical equations for near-equilibrium evaporation can be estimated from experiments, and these formulas can be exploited to predict steady-state evaporation fluxes in the air in a range of 8 orders of magnitude based on a single experiment carried out for nontoxic volatile compounds.

Quantification and Detection of Ground Garlic Adulteration Using Fourier-Transform Near-Infrared Reflectance Spectra.

This study demonstrates the rapid and cost-effective possibility of quantifying adulterant amounts (corn flour or corn starch) in ground and dried garlic samples. Prepared mixtures with different concentrations of selected adulterant were effectively characterized using Fourier-transform near-infrared reflectance spectra (FT-NIR), and multivariate calibration models were developed using two methods: principal component regression (PCR) and partial least squares regression (PLSR). They were constructed for optimally preprocessed FT-NIR spectra, and PLSR models generally performed better regarding model fit and predictions than PCR. The optimal PLSR model, built to estimate the amount of corn flour present in the ground and dried garlic samples, was constructed for the first derivative spectra obtained after Savitzky-Golay smoothing (fifteen sampling points and polynomial of the second degree). It demonstrated root mean squared errors for calibration and validation samples equal to 1.8841 and 1.8844 (i.e., 1.88% concerning the calibration range), respectively, and coefficients of determination equal to 0.9955 and 0.9858. The optimal PLSR model constructed for spectra after inverse scattering correction to assess the amount of corn starch had root mean squared errors for calibration and validation samples equal to 1.7679 and 1.7812 (i.e., 1.77% and 1.78% concerning the calibration range), respectively, and coefficients of determination equal to 0.9961 and 0.9873. It was also possible to discriminate samples adulterated with corn flour or corn starch using partial least squares discriminant analysis (PLS-DA). The optimal PLS-DA model had a very high correct classification rate (99.66%), sensitivity (99.96%), and specificity (99.36%), calculated for external validation samples. Uncertainties of these figures of merit, estimated using the Monte Carlo validation approach, were relatively small. One-class classification partial least squares models, developed to detect the adulterant type, presented very optimistic sensitivity for validation samples (above 99%) but low specificity (64% and 45.33% for models recognizing corn flour or corn starch adulterants, respectively). Through experimental investigation, chemometric data analysis, and modeling, we have verified that the FT-NIR technique exhibits the required sensitivity to quantify adulteration in dried ground garlic, whether it involves corn flour or corn starch.

Assessment of the Kernel Gram Matrix Representation of Data in Order to Avoid the Alignment of Chromatographic Signals.

This article discusses the possibility of exploratory data analysis of samples described by second-order chromatographic data affected by peak shifts. In particular, the potential of the kernel Gram matrix representation as an alternative to the necessary and time-consuming alignment step is evaluated. It was demonstrated through several simulation studies and comparisons that even small peak shifts can be a substantial source of data variance, and they can easily hamper the interpretation of chromatographic data. When peak shifts are small, their negative effect is far more destructive than the impact of relatively large levels of the Gaussian noise, heteroscedastic noise, and signal's baseline. The Gram principal component analysis approach has proven to be a well-suited tool for exploratory analysis of chromatographic signals collected using the diode-array detector in which sample-to-sample peak shifts were observed.

Testing of complementarity of PDA and MS detectors using chromatographic fingerprinting of genuine and counterfeit samples containing sildenafil citrate.

Counterfeit medicines are a global threat to public health. High amounts enter the European market, which is why characterization of these products is a very important issue. In this study, a high-performance liquid chromatography-photodiode array (HPLC-PDA) and high-performance liquid chromatography-mass spectrometry (HPLC-MS) method were developed for the analysis of genuine Viagra®, generic products of Viagra®, and counterfeit samples in order to obtain different types of fingerprints. These data were included in the chemometric data analysis, aiming to test whether PDA and MS are complementary detection techniques. The MS data comprise both MS1 and MS2 fingerprints; the PDA data consist of fingerprints measured at three different wavelengths, i.e., 254, 270, and 290 nm, and all possible combinations of these wavelengths. First, it was verified if both groups of fingerprints can discriminate between genuine, generic, and counterfeit medicines separately; next, it was studied if the obtained results could be ameliorated by combining both fingerprint types. This data analysis showed that MS1 does not provide suitable classification models since several genuines and generics are classified as counterfeits and vice versa. However, when analyzing the MS1_MS2 data in combination with partial least squares-discriminant analysis (PLS-DA), a perfect discrimination was obtained. When only using data measured at 254 nm, good classification models can be obtained by k nearest neighbors (kNN) and soft independent modelling of class analogy (SIMCA), which might be interesting for the characterization of counterfeit drugs in developing countries. However, in general, the combination of PDA and MS data (254 nm_MS1) is preferred due to less classification errors between the genuines/generics and counterfeits compared to PDA and MS data separately.

Prediction of the hydrophilic antioxidant capacity of tomato pastes from the IR and fluorescence excitation-emission spectra of extracts and intact samples.

The performance of the recently proposed excitation-emission fluorescence method was compared to the method using infrared measurements for the evaluation of the antioxidant properties of intact samples and extracts that had been obtained from tomato pastes. The oxygen radical absorbance capacity assay (ORAC) was applied in order to estimate the antioxidant capacity of the extracts, while the Folin-Ciocalteu reagent was adopted for the evaluation of the total phenolic content. The optimal extraction conditions for tomato pastes (three minutes of sonication under 80°C) were determined using the central composite design. Chemometric models such as the partial least squares regression and its N-way variant were further constructed in order to predict the antioxidant capacity or total phenolic content of the samples using either the IR or fluorescence spectra. The prediction errors that were obtained for the total antioxidant content were evaluated as the Trolox equivalents from the ORAC assay and were found to be equal to 2.011 (14.21%) for the fluorescence and 2.426 (17.15%) for the IR spectra, respectively. The prediction errors of the total phenolic content expressed as gallic acid equivalents were 0.067 (10.78%) for the fluorescence and 0.033 (5.36%) for the IR spectra, which were used as independent variables in the regression models.

Detection of discoloration in diesel fuel based on gas chromatographic fingerprints.

In the countries of the European Community, diesel fuel samples are spiked with Solvent Yellow 124 and either Solvent Red 19 or Solvent Red 164. Their presence at a given concentration indicates the specific tax rate and determines the usage of fuel. The removal of these so-called excise duty components, which is known as fuel "laundering", is an illegal action that causes a substantial loss in a government's budget. The aim of our study was to prove that genuine diesel fuel samples and their counterfeit variants (obtained from a simulated sorption process) can be differentiated by using their gas chromatographic fingerprints that are registered with a flame ionization detector. To achieve this aim, a discriminant partial least squares analysis, PLS-DA, for the genuine and counterfeit oil fingerprints after a baseline correction and the alignment of peaks was constructed and validated. Uninformative variables elimination (UVE), variable importance in projection (VIP), and selectivity ratio (SR), which were coupled with a bootstrap procedure, were adapted in PLS-DA in order to limit the possibility of model overfitting. Several major chemical components within the regions that are relevant to the discriminant problem were suggested as being the most influential. We also found that the bootstrap variants of UVE-PLS-DA and SR-PLS-DA have excellent predictive abilities for a limited number of gas chromatographic features, 14 and 16, respectively. This conclusion was also supported by the unitary values that were obtained for the area under the receiver operating curve (AUC) independently for the model and test sets.

Modeling of the total antioxidant capacity of rooibos (Aspalathus linearis) tea infusions from chromatographic fingerprints and identification of potential antioxidant markers.

Models to predict the total antioxidant capacity (TAC) of rooibos tea infusions from their chromatographic fingerprints and peak table data (content of individual phenolic compounds), obtained using HPLC with diode array detection, were developed in order to identify potential antioxidant markers. Peak table data included the content of 12 compounds, namely phenylpyruvic acid-2-O-glucoside, aspalathin, nothofagin, isoorientin, orientin, ferulic acid, quercetin-3-O-robinobioside, vitexin, hyperoside, rutin, isovitexin and isoquercitrin. The TAC values, measured using the oxygen radical absorbance capacity (ORAC) and DPPH radical scavenging assays, could be predicted from the peak table data or the chromatographic fingerprints (prediction errors 9-12%) using partial least squares (PLS) regression. Prediction models created from samples of only two production years could additionally be used to predict the TAC of samples from another production year (prediction errors<13%) indicating the robustness of the models in a quality control environment. Furthermore, the uninformative variable elimination (UVE)-PLS method was used to identify potential antioxidant markers for rooibos infusions. All individual phenolic compounds that were quantified were selected as informative variables, except vitexin, while UVE-PLS models developed from chromatographic fingerprints indicated additional antioxidant markers, namely (S)-eriodictyol-6-C-glucoside, (R)-eriodictyol-6-C-glucoside, aspalalinin and two unidentified compounds. The potential antioxidant markers should be validated prior to use in quality control of rooibos tea.

Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) and lung cancer are widespread lung diseases. Cigarette smoking is a high risk factor for both the diseases. COPD may increase the risk of developing lung cancer. Thus, it is crucial to be able to distinguish between these two pathological states, especially considering the early stages of lung cancer. Novel diagnostic and monitoring tools are required to properly determine lung cancer progression because this information directly impacts the type of the treatment prescribed. In this study, serum samples collected from 22 COPD and 77 lung cancer (TNM stages I, II, III, and IV) patients were analyzed. Then, a collection of NMR metabolic fingerprints was modeled using discriminant orthogonal partial least squares regression (OPLS-DA) and further interpreted by univariate statistics. The constructed discriminant models helped to successfully distinguish between the metabolic fingerprints of COPD and lung cancer patients (AUC training=0.972, AUC test=0.993), COPD and early lung cancer patients (AUC training=1.000, AUC test=1.000), and COPD and advanced lung cancer patients (AUC training=0.983, AUC test=1.000). Decreased acetate, citrate, and methanol levels together with the increased N-acetylated glycoproteins, leucine, lysine, mannose, choline, and lipid (CH3-(CH2)n-) levels were observed in all lung cancer patients compared with the COPD group. The evaluation of lung cancer progression was also successful using OPLS-DA (AUC training=0.811, AUC test=0.904). Based on the results, the following metabolite biomarkers may prove useful in distinguishing lung cancer states: isoleucine, acetoacetate, and creatine as well as the two NMR signals of N-acetylated glycoproteins and glycerol.

Clustering in analytical chemistry.

Data clustering plays an important role in the exploratory analysis of analytical data, and the use of clustering methods has been acknowledged in different fields of science. In this paper, principles of data clustering are presented with a direct focus on clustering of analytical data. The role of the clustering process in the analytical workflow is underlined, and its potential impact on the analytical workflow is emphasized.

Probing an artificial polypeptide receptor library using a series of novel histamine H3 receptor ligands.

An artificial polypeptide receptor (APR) library was created by using the self-organization of N-lipidated peptides attached to cellulose via m-aminophenylamino-1,3,5-triazine. The response of the library was probed using a series of novel H3 receptor ligands. Since no guidelines on how to design an APRs selective vs certain receptor types exist, a diverse set of amino acids (Ala, Trp, Pro, Glu, His, Lys and Ser) were used and coupled with one of three gating fatty acids (palmitic, ricinoleic or capric). A competitive adsorption-desorption of an appropriate reporter dye was used for the indirect visualization of the interactions of guests with particular receptors. The resulted library response to individual inhibitors was then arranged in a matrix, preprocessed and analyzed using the principal component analysis (PCA) and partial least squares (PLS) method. The most important conclusion obtained from the PCA analysis is that the library differentiates the probed compounds according to the lipophilicity of the gating unit. The PC3 with a dominant absolute contribution of the receptors containing Glu allowed for the best separation of the ligands with respect to their activity. This conclusion is in agreement with the fact that Glu 206 is a genuine ligand counterpart in the natural histamine receptor.

Impurity fingerprints for the identification of counterfeit medicines--a feasibility study.

Most of the counterfeit medicines are manufactured in non good manufacturing practices (GMP) conditions by uncontrolled or street laboratories. Their chemical composition and purity of raw materials may, therefore, change in the course of time. The public health problem of counterfeit drugs is mostly due to this qualitative and quantitative variability in their formulation and impurity profiles. In this study, impurity profiles were treated like fingerprints representing the quality of the samples. A total of 73 samples of counterfeit and imitations of Viagra(®) and 44 samples of counterfeit and imitations of Cialis(®) were analysed on a HPLC-UV system. A clear distinction has been obtained between genuine and illegal tablets by the mean of a discriminant partial least squares analysis of the log transformed chromatograms. Following exploratory analysis of the data, two classification algorithms were applied and compared. In our study, the k-nearest neighbour classifier offered the best performance in terms of correct classification rate obtained with cross-validation and during external validation. For Viagra(®), both cross-validation and external validation sets returned a 100% correct classification rate. For Cialis(®) 92.3% and 100% correct classification rates were obtained from cross-validation and external validation, respectively.

The proteomic analysis of primary cortical astrocyte cell culture after morphine administration.

Astrocytes are supportive cells, necessary for ensure optimal environment for neural cells functioning. They are involved in extracellular K+ level regulation and neurotransmitters removal. They are also dependent for myelination and synapses formation. They may make a contribution in signal propagation in the central nervous system, for example, through Ca2+ signaling. With the use of neonatal pure astrocyte cell culture, we investigated changes in astrocyte's proteomes under the influence of morphine. We found 10 major proteins, which show different expression between physiological cell culture and morphine treatment. With 2D gel electrophoresis and nanoLC-ESI-MS/MS, we identified proteins and characterized their potential role in morphine dependence. Observed differences were also confirmed by Western blotting. Our data suggests a role for astrocytes in the formation of the morphine dependence at the molecular level. This finding may support interpretation of causes of morphine dependence formation based only on behavioral data.

Peak alignment of urine NMR spectra using fuzzy warping.

Proton nuclear magnetic resonance (1H NMR) spectroscopic analysis of mixtures has been used extensively for a variety of applications ranging from the analysis of plant extracts, wine, and food to the evaluation of toxicity in animals. For example, NMR analysis of urine samples has been used extensively for biomarker discovery and, more simply, for the construction of classification models of toxicity, disease, and biochemical phenotype. However, NMR spectra of complex mixtures typically show unwanted local peak shifts caused by matrix and instrument variability, which must be compensated for prior to statistical analysis and interpretation of the data. One approach is to align the spectral peaks across the data set. An efficient and fast warping algorithm is required as the signals typically contain ca. 32,000-64,000 data points and there can be several thousand spectra in a data set. As demonstrated in our study, the iterative fuzzy warping algorithm fulfills these requirements and can be used on-line for an alignment of the NMR spectra. Correlation coefficients between the aligned and target spectra are used as the evaluation function for the algorithm, and its performance is compared with those of other published warping methods.