ArticleSensors (Basel, Switzerland)2019
Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Electrical Impedance Spectroscopy and Tomography for Fruit Quality Monitoring: A State-of-the-Art Analysis and Experimental Insights.Sensors (Basel, Switzerland) · 2026Article
- Optimizing unsupervised clustering of electrochemical impedance spectra via normalization and dimensionality reduction.Scientific reports · 2026Article
- A FPGA based recurrent neural networks-based impedance spectroscopy system for detection of YAKE in tuna.Scientific reports · 2025Article
- Classifying Storage Temperature for Mandarin (Sensors (Basel, Switzerland) · 2025Article
- Experimental investigation of freeze injury temperatures in trees and their contributing factors based on electrical impedance spectroscopy.Frontiers in plant science · 2024Article
- Plant impedance spectroscopy: a review of modeling approaches and applications.Frontiers in plant science · 2023Review
- Electrical Impedance Spectroscopy for Moisture and Oil Content Prediction in Oil Palm (Plants (Basel, Switzerland) · 2022Article
- LbCas12a-D156R Efficiently EditsCells · 2022Article
- Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data.Scientific reports · 2021Article
- A CMOS Low Pass Filter for SoC Lock-in-Based Measurement Devices.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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%).
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Registered trials
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