Voltammetric Electrochemical Sensor for Phylogenetic Study in Acer Linn
Abstract
Acer Linn. is a highly divergent species morphology in the maple family (Aceraceae). It is one of the genera facing a very difficult taxonomic situation. The phylogeny of the genus and the taxonomic system under the genus remain unclear. The use of electrochemical fingerprints for plant phylogenetic study is an emerging application in biosensors. In this work, leaves of 18 species of Acer Linn. with an exo-taxa were selected for electrochemical fingerprint recording. Two different conditions were used for improving the data abundance. The fingerprint of all species showed a series of oxidation peaks. These peaks can be ascribed to the oxidation of flavonols, phenolic acids, procyanidins, alkaloids, and pigments in plant tissue. These electrochemical fingerprints can be used for the identification of plant species. We also performed a phylogenetic study with data from electrochemical fingerprinting. The phylogenetic tree of Acer is divided into three main clades. The result is in full agreement with A. shangszeense var. anfuense, A. pictum subsp. mono, A. amplum, A. truncatum, and A. miaotaiense, belonging to the subsection Platanoidea. A. nikoense and A. griseum were clustered together in the dendrogram. Another group that fits the traditional classification results is in the subsection Integrifolia.
Read the paper
Related studies
- Nano Biosensors: Properties, applications and electrochemical techniques
- Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics
- Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment
- Electrochemical determination of rutin by using NiFe2O4 nanoparticles-loaded reduced graphene oxide
- Application of Polyoxometalate-based composites for sensor systems: A review
- Sensitive Electrochemical and Thermal Detection of Human Noroviruses Using Molecularly Imprinted Polymer Nanoparticles Generated against a Viral Target
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.