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
Metabolomics is a new genomics approach that aims at measuring all or a subset of metabolites in the cell. Several approaches to plant metabolomics are currently used in plant research. These include targeted analysis, metabolite profiling, and metabolic fingerprinting. Metabolic fingerprinting, unlike metabolite profiling, does not aim at separating or identifying all the metabolites present in the sample, but rather generates a fingerprint that characterizes a specific metabolic state of the plant system under investigation. This chapter describes the implementation of metabolic fingerprinting approach using gas chromatography coupled to mass spectrometry (GC-MS) and discriminant function analysis combined with genetic algorithm (GA-DFA). This approach enables the identification of specific metabolites that are biologically relevant, and which may go undetected if direct infusion-based fingerprinting approaches were used due to the sample complexity and matrix suppression effects.
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Source |
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http://dx.doi.org/10.1007/978-1-60761-682-5_17 | DOI Listing |
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