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Progress and mechanism exploration of electrical impedance spectroscopy in the study of plant cold resistance

agricultural-science
bioengineering
biophysics
environmental-sciences
plant-science

Abstract

Background: Electrical Impedance Spectroscopy (EIS) is a non-destructive, efficient electrophysiological method that has proven valuable in assessing plant cold resistance and screening for cold-tolerant varieties. Low-temperature stress induces lipid phase transitions and permeability changes in plant cell membranes, resulting in physiological damage such as electrolyte leakage and enzyme inactivation. Traditional methods for assessing cold resistance, including electrolyte leakage rate determination, growth recovery tests, and LT₅₀ semi-lethal temperature assessments, suffer from limitations such as destructiveness, time consumption, and insufficient sensitivity.

Methodology: EIS measures impedance responses by applying a multi-frequency alternating current across plant tissues. This approach simultaneously provides parameters such as extracellular resistance, intracellular resistance, and cell membrane capacitance, which offer insights into cell structural integrity and physiological status from multiple dimensions.

Results: Studies have demonstrated that different cold-resistant genotypes of plants exhibit distinct impedance spectral characteristics under low-temperature stress, allowing EIS to effectively differentiate cold resistance across varieties or treatments.

Conclusions: This article reviews recent advancements in the application of EIS technology for plant cold resistance research. It explores the underlying electrical principles, equivalent circuit models, and biophysical mechanisms, and summarizes practical applications for screening cold-resistant plants, including woody plants and herbaceous crops, as well as the integration of EIS with traditional methods. Additionally, the article highlights the advantages of EIS in terms of accuracy, universality, and real-time monitoring, while discussing challenges related to species specificity, modeling standards, and data analysis. Finally, the article examines the potential future developments in EIS, including its integration with artificial intelligence, portable devices, and database standardization for plant stress resistance research.

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