Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy.

Abstract

Lemon is the most sensitive citrus fruit to cold. Therefore, it is of capital importance to detect and avoid temperatures that could damage the fruit both when it is still in the tree and in its subsequent commercialization. In order to rapidly identify frost damage in this fruit, a system based on the electrochemical impedance spectroscopy technique (EIS) was used. This system consists of a signal generator device associated with a personal computer (PC) to control the system and a double-needle stainless steel electrode. Tests with a set of fruits both natural and subsequently frozen-thawed allowed us to differentiate the behavior of the impedance value depending on whether the sample had been previously frozen or not by means of a single principal components analysis (PCA) and a partial least squares discriminant analysis (PLS-DA). Artificial neural networks (ANNs) were used to generate a prediction model able to identify the damaged fruits just 24 hours after the cold phenomenon occurred, with sufficient robustness and reliability (CCR = 100%).

Plant phenotyping relevance

EISを用いてレモン果実の凍害状態を非破壊的に識別・予測する測定システムと解析モデルが研究の中心であり、果実という植物器官の状態を定量化している。

Code and data availability

The article describes EIS measurements on lemons with PCA/PLS-DA and ANN analysis, but no public phenotype dataset, image/sensor data deposit, author analysis code, or trained model checkpoint is mentioned. Data were stored on a PC and analyzed with commercial software (SOLO, Alyuda Neurointelligence); no availability,

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