Ion composition profiling and pattern recognition of vegetable sap using a solid-contact ion-selective electrode array

This is the first study to develop an effective tool for plant sap analysis based on a solid-contact ion-selective electrode (SCISE) array, which has the advantages of on-site, direct and fast analysis. SCISEs are all-solid-state ion-selective electrodes with a conducting polymer for ion-to-electron...

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Autores principales: Sheng-Feng Huang, Wei-Li Shih, Yi-Yi Chen, Yi-Min Wu, Lin-Chi Chen
Formato: article
Lenguaje:EN
Publicado: Elsevier 2021
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Acceso en línea:https://doaj.org/article/f44790d427bd4a81adcc13841a980c19
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Sumario:This is the first study to develop an effective tool for plant sap analysis based on a solid-contact ion-selective electrode (SCISE) array, which has the advantages of on-site, direct and fast analysis. SCISEs are all-solid-state ion-selective electrodes with a conducting polymer for ion-to-electron transduction. With the conducting polymer solid-contact, the electrodes perform high stability and short response time (<30 s) during potentiometric sensing. The developed SCISE array consists of potassium (SEN = 51.9 mV/decade), sodium (64.2 mV/decade), ammonium (59.3 mV/decade), calcium (32.1 mV/decade), and magnesium (33.0 mV/decade) selective electrodes. To verify the application, seven types of fresh crude vegetable leaf juices (ion concentration range: 10−3–10−4 M) were measured with the array, and the result was compared with ion chromatography. It was found that the array was able to obtain the unique, distinguishable ion composition profile as a radar plot of each vegetable sap, implying the fingerprint application of the present technology. Furthermore, applying principle component analysis (PCA) and K-means clustering, lettuces grown in different environments (deficiency either in potassium or in nitrate) are able to be discriminated. In summary, we demonstrate a tool for on-site, high-throughput and direct plant sap analysis based on SCISE array. Moreover, it could combine with pattern recognition and become a promising tools which could provide a diagnosis of the nutritive status of vegetables.