3 resultados para extraction and separation techniques

em Brock University, Canada


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Factors affecting the detennination of PAHs by capillary GC/MS were studied. The effect of the initial column temperature and the injection solvent on the peak areas and heights of sixteen PAHs, considered as priority pollutants, USillg crosslinked methyl silicone (DB!) and 5% diphenyl, 94% dimethyl, 1% vinyl polysiloxane (DBS) columns was examined. The possibility of using high boiling point alcohols especially butanol, pentanol, cyclopentanol, and hexanol as injection solvents was investigated. Studies were carried out to optimize the initial column temperature for each of the alcohols. It was found that the optimum initial column temperature is dependent on the solvent employed. The peak areas and heights of the PAHs are enhanced when the initial column temperature is 10-20 c above the boiling point of the solvent using DB5 column, and the same or 10 C above the boiling point of the solvent using DB1 column. Comparing the peak signals of the PAHs using the alcohols, p-xylene, n-octane, and nonane as injection solvents, hexanol gave the greatest peak areas and heights of the PAHs particularly the late-eluted peaks. The detection limits were at low pg levels, ranging from 6.0 pg for fluorene t9 83.6 pg for benzo(a)pyrene. The effect of the initial column temperature on the peak shape and the separation efficiency of the PARs was also studied using DB1 and DB5 columns. Fronting or splitting of the peaks was obseIVed at very low initial column temperature. When high initial column temperature was used, tailing of the peaks appeared. Great difference between DB! and.DB5 columns in the range of the initial column temperature in which symmetrical.peaks of PAHs can be obtained is observed. Wider ranges were shown using DB5 column. Resolution of the closely-eluted PAHs was also affected by the initial column temperature depending on the stationary phase employed. In the case of DB5, only the earlyeluted PAHs were affected; whereas, with DB1, all PAHs were affected. An analytical procedure utilizing solid phase extraction with bonded phase silica (C8) cartridges combined with GC/MS was developed to analyze PAHs in water as an alternative method to those based on the extraction with organic solvent. This simple procedure involved passing a 50 ml of spiked water sample through C8 bonded phase silica cartridges at 10 ml/min, dried by passing a gentle flow of nitrogen at 20 ml/min for 30 sec, and eluting the trapped PAHs with 500 Jll of p-xylene at 0.3 ml/min. The recoveries of PAHs were greater than 80%, with less than 10% relative standard deviations of nine determinations. No major contaminants were present that could interfere with the recognition of PAHs. It was also found that these bonded phase silica cartridges can be re-used for the extraction of PAHs from water.

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Several automated reversed-phase HPLC methods have been developed to determine trace concentrations of carbamate pesticides (which are of concern in Ontario environmental samples) in water by utilizing two solid sorbent extraction techniques. One of the methods is known as on-line pre-concentration'. This technique involves passing 100 milliliters of sample water through a 3 cm pre-column, packed with 5 micron ODS sorbent, at flow rates varying from 5-10 mUmin. By the use of a valve apparatus, the HPLC system is then switched to a gradient mobile phase program consisting of acetonitrile and water. The analytes, Propoxur, Carbofuran, Carbaryl, Propham, Captan, Chloropropham, Barban, and Butylate, which are pre-concentrated on the pre-column, are eluted and separated on a 25 cm C-8 analytical column and determined by UV absorption at 220 nm. The total analytical time is 60 minutes, and the pre-column can be used repeatedly for the analysis of as many as thirty samples. The method is highly sensitive as 100 percent of the analytes present in the sample can be injected into the HPLC. No breakthrough of any of the analytes was observed and the minimum detectable concentrations range from 10 to 480 ng/L. The developed method is totally automated for the analysis of one sample. When the above mobile phase is modified with a buffer solution, Aminocarb, Benomyl, and its degradation product, MBC, can also be detected along with the above pesticides with baseline resolution for all of the analytes. The method can also be easily modified to determine Benomyl and MBC both as solute and as particulate matter. By using a commercially available solid phase extraction cartridge, in lieu of a pre-column, for the extraction and concentration of analytes, a completely automated method has been developed with the aid of the Waters Millilab Workstation. Sample water is loaded at 10 mL/min through a cartridge and the concentrated analytes are eluted from the sorbent with acetonitrile. The resulting eluate is blown-down under nitrogen, made up to volume with water, and injected into the HPLC. The total analytical time is 90 minutes. Fifty percent of the analytes present in the sample can be injected into the HPLC, and recoveries for the above eight pesticides ranged from 84 to 93 percent. The minimum detectable concentrations range from 20 to 960 ng/L. The developed method is totally automated for the analysis of up to thirty consecutive samples. The method has proven to be applicable to both purer water samples as well as untreated lake water samples.

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Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).