Use of On-Site Soil Vapor Analysis in UST Site Assessments: A Summary of Results at 635 Sites

2009 ◽  
pp. 197-197-14
Author(s):  
RD Golding ◽  
TA Wichman
Keyword(s):  
Vapor Analysis

scholarly journals Calibration and Uncertainty Estimation for Water Content Measurement in Solids

2021 ◽  
Vol 42 (3) ◽  
Author(s):  
Rudolf Aro ◽  
Mohamed Wajdi Ben Ayoub ◽  
Ivo Leito ◽  
Éric Georgin ◽  
Benoit Savanier
Keyword(s):  
Water Vapor ◽  
Water Content ◽  
Software Tool ◽  
Uncertainty Estimation ◽  
Multiple Point ◽  
Content Measurement ◽  
Analysis Technique ◽  
Calibration Methods ◽  
Vapor Analysis ◽  
Secondary Methods

AbstractIn the field of water content measurement, the calibration of coulometric methods (e.g., coulometric Karl Fischer titration or evolved water vapor analysis) is often overlooked. However, as coulometric water content measurement methods are used to calibrate secondary methods, their results must be obtained with the highest degree of confidence. The utility of calibrating such instruments has been recently demonstrated. Both single and multiple point calibration methods have been suggested. This work compares these calibration methods for the evolved water vapor analysis technique. Two uncertainty estimation approaches (Kragten’s spreadsheet and M-CARE software tool) were compared as well, both based on the ISO GUM method.

scholarly journals The Quality Estimation of Different Tobacco Types Examined by Headspace Vapor Analysis

1999 ◽  
Vol 18 (5) ◽  
pp. 213-222 ◽  
Author(s):  
H Hasebe ◽  
S Suhara
Keyword(s):  
Regression Model ◽  
Heating Temperature ◽  
Headspace Analysis ◽  
Principal Component ◽  
Quality Estimation ◽  
Sensory Study ◽  
Gas Chromatography Mass Spectroscopy ◽  
Vapor Analysis ◽  
Headspace Sampler

AbstractIn order to judge the quality of tobacco leaf, it is necessary to conduct sensory smoke evaluations. However, these are subjective and the results are difficult to quantify. Therefore, we have attempted to establish a quantitative method for evaluating tobacco quality by comparing results of headspace analysis. Forty-seven leaf samples of different types (flue-cured, Burley, Oriental) were analyzed. The first step in this study was to have a panel of experts smoke cigarettes made from the test tobaccos and have them evaluate 10 sensory attributes. The scores were then analyzed by the technique of principal component analysis (PCA). Results showed that the score for the flavor note attribute indicated the type of tobacco and the scores of the other 9 attributes were combined as a total to indicate smoking quality. Following the sensory study, headspace vapors of the test tobaccos were analyzed with a headspace sampler, gas chromatography, mass spectroscopy system (HS-GC-MS), in which the gas sampling loop and the HS-GC transfer line were deactivated. In order to obtain conditions for good reproducibility, the heating temperature and time of the headspace vials were examined. PCA was performed for the headspace vapor (HSV) analysis results for 31 selected peaks. The first and second principal components could be used to classify tobacco types. The third principal component partially indicated differences of smoking qualities. Finally, multiple regression analysis was performed on the HSV analysis results in order to estimate the smoking quality scores. The regression model of all samples combined had a low regression coefficient. Then, we separated the results of the three tobacco types, as we considered that the headspace data might reveal information about the classifications themselves. The final outcome was a regression model that could be applied to each type with a higher accuracy. The variables that entered the models were compared.

Use of Mass Spectrometric Vapor Analysis To Improve Canine Explosive Detection Efficiency

2017 ◽  
Vol 89 (12) ◽  
pp. 6482-6490 ◽  
Author(s):  
Ta-Hsuan Ong ◽  
Ted Mendum ◽  
Geoff Geurtsen ◽  
Jude Kelley ◽  
Alla Ostrinskaya ◽  
...  
Keyword(s):  
Detection Efficiency ◽  
Mass Spectrometric ◽  
Explosive Detection ◽  
Vapor Analysis
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