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Official websites use. Share sensitive information only on official, secure websites. The purpose of this work is to promote and facilitate forensic profiling and chemical analysis of illicit drug samples in order to determine their origin, methods of production and transfer through the country. The article is based on the gas chromatography analysis of heroin samples seized from three different locations in Serbia. Chemometric approach with appropriate statistical tools multiple-linear regression MLR , hierarchical cluster analysis HCA and Wald-Wolfowitz run WWR test were applied on chromatographic data of heroin samples in order to correlate and examine the geographic origin of seized heroin samples. The best MLR models were further validated by leave-one-out technique as well as by the calculation of basic statistical parameters for the established models. To confirm the predictive power of the models, external set of heroin samples was used. High agreement between experimental and predicted values of acetyl thebaol and diacetyl morphine peak ratio, obtained in the validation procedure, indicated the good quality of derived MLR models. WWR test showed which examined heroin samples come from the same population, and HCA was applied in order to overview the similarities among the studied heroine samples. Illicit drug profiling provides law and police authorities essential physicochemical information that may assist in identification and disruption of drug trafficking in one country. Results of chemical analysis may allow investigators to determine geographical origin of the illicit drug, synthetic path and chemical precursors of synthetic drugs. The physical evidence combined with chemical analysis can be also used to establish links between different seizures of illicit drugs. This is the first attempt in Serbia to acquire chemical and profiling data on seized heroin samples and disseminate information to appropriate national and regional governmental agencies. Serbia was chosen as the entering point to European Union, since the common trafficking routes from Middle East to EU are passing its territory mostly through the mentioned border crossings 1 with Croatia and 2 with Hungary. Heroin is a semi-synthetic derivative of morphine. Due to differences in the way of growing opium poppies and the different synthetic routes during the synthesis of heroin, the presence and concentration of opium alkaloids vary in the final product. Also, during the acetylation of morphine, the other alkaloids of opium may react. Thus, their presence and concentrations vary in the final product. The presence of diluents mannitol, glucose and adulterants caffeine, acetaminophen provide additional information on the geographical origin of heroin and the way of production. Determining the concentration of opium alkaloids, adulterants and diluents makes chemical «profile» of heroin. Due to the presence of a large number of compounds, the chemical profiles of heroin can be very complex, which further complicates the analysis and profiling of the illicit drug 1 — 3. One of the most recent ways to use large amounts of data collected during different chemical analysis of illicit drugs is the application of the chemometric tools suitable for data mining and forensic profiling. In this way, investigators can get insight of the drug production, drug trafficking and geographical origin of the sample. Chemometric approach studies are undoubtedly of a great importance in modern chemistry and biochemistry, especially because of possibility to screen a large number of chemical data i. Multivariate chemometrics is a very useful and powerful tool when the main issue includes dealing with multicomponent data sets 8 , 9. It allows the extraction of maximum information from complicated datasets. The conclusions in forensic science must be drawn from objective sources as much as possible. The forensic scientists must always follow rigid statistical protocols in the process of making decisions based on experimental data. Hence, our present paper explores the usefulness of multivariate chemometrics with regression and classification approaches in the discrimination of seized heroin samples. The aim of the study was to establish a simple analytical procedure followed by chemometric approach that can recover batch links among limited number of seized heroin samples. All samples used in this work were seized during various actions by the Serbian police at the border crossings 1 and 2 , together with those seized on the territory of Novi Sad municipality 3. Heroin samples were homogenized first in a mortar. Mass of 0. Chromatographic separation was achieved on a capillary column DB-1 length 30 m, internal diameter 0. Carrier gas was nitrogen at a pressure of This temperature was maintained for 10 min. The general purpose of MLR analysis is to quantitate the relationship between several independent or predictor variables and a dependent variable. MLR model is built with descriptive variables using the least squares methods to minimize the residuals General MLR model is:. HCA is a method for dividing a group of objects into classes so that similar objects are in the same class cluster. The groups of entities are not known prior to the mathematical analysis and no assumptions are made about the distribution of the variables. Cluster analysis searches for objects which are close together in the variable space. The data in each cluster share some common trait, often proximity according to some defined distance measure Wald-Wolfowitz run test WWR can be applied to examine if two random samples come from populations with the same distribution. WWR test can detect differences in averages or spread or any other important aspect between the two populations This test is efficient when each sample size is greater than or equal to 10 This method includes testing the null hypothesis - H 0 : two samples come from populations having the same distribution. We can calculate this value based on the following equations 15 :. Observations from sample 1 should be denoted as X s and other as Y s, and then the number of runs can be counted. Afterwards, the r and r cr numbers can be compared. In the first step of the present study, gas chromatography GC analyses were applied on thirty eight different samples of heroin from three locations in Serbia. The data of gas chromatography GC analyses are summarized in Table 1. All these components were identified according to their retention times. In the second step, we focused our efforts on developing the MLR models that can determine the geographical origin of heroin samples. A set of twenty eight collected data samples was used for MLR modeling. MLR procedure was used to model the relationships between the data of GC analyses. The specifications for the best selected MLR models are shown in Table 2. Correlation coefficient r or coefficient of multiple determination is a relative measure of the fit by the regression equation. Correspondingly, it represents the part of variation in the observed data that is explained by the regression. Standard deviation is measured by the error mean square, which expresses the variation of the residuals or the variation from the regression line. Thus, standard deviation is an absolute measure of the quality of the fit and should have a low value for the regression to be significant. The F -test reflects the ratio of the variance explained by the model and the variance due to the error in regression. High value of the F -test indicates that the model is statistically significant. It is well known that there are three important components in any chemometric-regression analysis: the development of the models, validation of the models and the utilization of developed models. Validation is a crucial aspect of any regression analysis For testing the validity of the predictive power of selected models leave one out LOO technique was used. These parameters were calculated from the following equations:. PRESS is an acronym for prediction sum of squares. It is used to validate a regression model regarding to its predictability. The remaining n -1 observations are used to calculate a regression and estimate the value of the omitted observation. This is done n times, once for each observation. The difference between the actual Y value, Y obs , and the predicted Y calc , is so-called the prediction error. SSY are the sums of squares associated with the corresponding sources of variation. These values are in terms of the dependent variable, Y. The above PRESS value can be used to compute an r 2 CV statistic, called r 2 cross validated parameter, which reflects the prediction ability of the model. This is a good way to validate the prediction of a regression model without selecting another sample or splitting the data. It is very possible to have a high r 2 and a very low r 2 CV. When this occurs, it implies that the fitted model is data dependent. This parameter ranges from below zero to above one. When outside the range of , it is truncated to stay within this range. Adjusted r -squared r 2 adj is an adjusted version of r 2. The adjustment seeks to remove the distortion due to a small sample size. However, some recent reports have proved the opposite Although, the low value of r 2 CV for the training set can indeed serve as an indicator of a low predictive ability of a model, the opposite is not necessarily true. Thus, the high value of LOO r 2 CV is the necessary condition for a model to have a high predictive power, but it is not a sufficient one. Although models showed good internal consistency, they may not be applicable for the analogs which were never used in the generation of the correlation. It is proven that the only way to estimate the true predictive power of a model is to test it on a sufficiently large collection of the samples from an external test set. The test set must include no less than five samples, whose properties and structures must cover the range of properties and structures of the samples from the training set. This application is necessary for obtaining trustful statistics for comparison between the observed and predicted values for these compounds. Therefore, the external extrapolation power of the model was further authenticated by a test set of ten heroin samples. Figure 1. From the data presented in Table 4. According to the reference 16 without the validation of the MLR models by using the external test set, we could not come to a right conclusion about high predictive ability of derived models. The propagation of the residuals on both sides of zero indicates that no systematic error exists in the development of regression models as suggested by Jalali-Heravi et al. It indicates that these models can be successfully applied to predict the geographic origin of seized heroin samples using the GC results. Therefore, the randomness of the residuals and their low values indicate that the obtained mathematical models can predict the dependent variable with acceptable error. According to the Variance Inflation Factor VIF , which was lower than 10 for all the obtained models, it can be concluded that there is no multi collinearity present in the established models. Clustering was based on the Euclidean distance and single linkage algorithm. The obtained dendrogram is presented in Figure 3. These entities are placed into the separate cluster, while the samples that belong to border crossing 1 are significantly different than the others. This result confirms the finding obtained with HCA method. According to WWR test, the samples from Novi Sad municipality and border crossing 1 do not belong to the same population, as well as the samples from border crossings 1 and 2. In this case, all the three types of samples do not belong to the same population. As it is shown in the MLR analysis, this ratio actually is dependent variable which is predicted based on the other determined peak ratios. Mathematical dependences that can determine the geographical origin of heroin samples were obtained. The validity of the models has been evaluated by the determination of suitable statistical parameters. The low residual activity and high cross-validated r 2 values r 2 CV indicated the predictive ability of the developed MLR models. Since the correlation was extremely good, our mathematical models can be used to predict geographic origin of seized heroin samples in Serbia, using the GC results. As a library, NLM provides access to scientific literature. Iran J Pharm Res. Find articles by Marija Petrin. Find articles by Tatjana Bugarski. Received Sep; Accepted May. Open in a new tab. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel.

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