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handle: 10261/244975
Plant stress responses are mediated by the release of chemical compounds called exudates into the rhizosphere. These chemical substances include primary and secondary plant metabolites and play an important role in the plant defense mechanism. The identification, characterization and study of these compounds can open the door to numerous applications, from greener agriculture to enhanced phytoremediation. This paper critically reviews the most relevant sampling strategies, analytical methodologies, and data-mining approaches to study root exudates.Common analytical techniques are grounded in mass spectrometry or nuclear mass spectrometry, but less common biospectroscopy techniques could offer a new perspective in plant metabolomics due to the minimal sample processing they require. Finally, after analysis, the collected raw data must then be analyzed by means of different multivariate and univariate statistical approaches to test biological-response hypotheses. All in all, the assessment of root exudates calls for the development of hyphenated analytical methodologies, as well as efforts to consolidate data-preprocessing workflows.
The authors gratefully acknowledge the financial support of the Spanish Ministry of Science, Innovation, and Universities under Project CTM2017-91355-EXP. Mònica Escolà Casas wants to thank the Beatriu de Pinós 2018 grant-programme (MSCA grant agreement number 801370) for the funding. IDAEA-CSIC is a Centre of Excellence Severo Ochoa (Spanish Ministry of Science and Innovation, Project CEX2018-000794-S).
Peer reviewed
Data processing, Analytical techniques, Root exudates, Sampling, Experimental design
Data processing, Analytical techniques, Root exudates, Sampling, Experimental design
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 52 | |
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
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