ArticleSensors (Basel, Switzerland)2018
Early Detection of Freeze Damage in Navelate Oranges with Electrochemical Impedance Spectroscopy.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 22 citations in OpenAlex.
- Coupling electrochemical and spectroscopic methods for river water dissolved organic matter characterization.Environmental monitoring and assessment · 2025Article
- Classifying Storage Temperature for Mandarin (Sensors (Basel, Switzerland) · 2025Article
- Plant impedance spectroscopy: a review of modeling approaches and applications.Frontiers in plant science · 2023Review
- Quality Attributes and Dielectric Properties of Sea Buckthorn Berries under Differing Freezing Regimes and Their Interrelationships.Foods (Basel, Switzerland) · 2022Article
- Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
The early detection of freeze damage in Navelate oranges (Citrus sinensis L. Osbeck) was studied using electrochemical impedance spectroscopy (EIS), which is associated with a specific double-needle sensor. The objective was to identify this problem early in order to help to determine when a freeze phenomenon occurs. Thus, we selected a set of Navelate oranges without external defects, belonging to the same batch. Next, an intense cold process was simulated to analyze the oranges before and after freezing. The results of the spectroscopy analysis revealed different signals for oranges depending on whether they had experienced freezing or not. Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) of the obtained data demonstrated that it is possible to discriminate the samples, explaining 88.5% of the total variability (PCA) and being able to design a mathematical model with a prediction sensitivity of 80% (PLS-DA). Additionally, a designed artificial neural network (ANN) prediction model managed to correctly classify 100% of the studied samples. Therefore, EIS together with ANN-based data treatment is proposed as a viable alternative to the traditional techniques for the early detection of freeze damage in oranges.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.