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
To test the potential of different analytical tools to determine the geographical origin of Ethiopian coffee, 103 green arabica coffee samples from four coffee regions in Ethiopia were subjected to multi-elements and δC, δN and δO determinations. Multi-elements were determined by using inductively coupled plasma (ICP)- and X-ray fluorescence spectrometry (XRF)-based techniques, and δC, δN and δO were determined by using elemental analyzer-isotope ratio mass spectrometry. Using linear discriminant analysis, XRF-based multi-elements with and without δC appeared to be most effective in discriminating the geographical origin of coffee, giving higher classification accuracy (89 and 86%, respectively) than ICP-based multi-elements with and without stable isotopes (80%, each). These results demonstrate the potential of XRF-based multi-element profiling as a relatively fast and low-cost tool to trace the geographical origin of Ethiopian coffee. All together this study offers the proof of concept for a promising method that, upon standardization, could be used for coffee provenance authentication and fraud detection.
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Source |
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http://dx.doi.org/10.1016/j.foodchem.2019.03.135 | DOI Listing |
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