Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
In this work, first and second dimensional retention index (I and I) based calculation approach is established to simulate peak retention times (t and t) of samples for the given sets of volatile compounds in comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS). For the result without t and t data of alkane references (t and t), the following steps were applied: (1) curve fitting based on van den Dool and Kratz relationship in order to simulate t using a training set of volatile compounds in a sample with their experimental t data, and (2) simulation of t at different t to construct their isovolatility curves based on a nonlinear equation with p-p parameters and a constant (within the ranges of -0.0052 to 0.0049, -0.6181 to -0.0230, -26.4775 to -0.2698, 0.0050 to 9.6259, -7.2976 to -3.9524 and 0.9157 to 4.0779, respectively). These parameters were obtained by performing curve fitting according to the experimental t data of the same training set with the least square values ranging from 4.58×10 to 32.55. Simulation of t and t of target analytes (t and t) with known I and I were performed using t and the simulated isovolatility curves. All the calculations and curve fittings were carried out by using Solver in Microsoft Excel. The approach was applied to simulate results for 542 compounds in several samples including analysis of saffron (Crocus sativas L.), Boswellia papyrifera, acacia honey and incense powder/smoke, perfume and cannabis either reported from literature or from the experiments in this work using different experimental approaches. These were compared with the experimental data showing correlation with the R ranges of 0.98-1.00 and 0.80-0.97 for t and t, respectively. This approach was then applied to propose 6 compounds which may be incorrectly identified based on the differences of >2 times of the standard deviations between t and the experimental t in both residue and leave-one-out analyses.
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Source |
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http://dx.doi.org/10.1016/j.chroma.2022.463394 | DOI Listing |
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