Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy

Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 19; no. 18; p. 4051
Main Authors: Fernández, Adrián Ochandio, Cristian Ariel Olguín Pinatti, Rafael Masot Peris, Laguarda-Miró, Nicolás
Format: Journal Article
Language:English
Published: Basel MDPI AG 19-09-2019
MDPI
Subjects:
Algorithms
Citrus fruits
Commercialization
Damage detection
detection
Discriminant analysis
Electrochemical impedance spectroscopy
Electrodes
Food
Fourier transforms
freeze damage
Frost
Frost damage
Fruits
Laboratories
lemon
Lemons
Measurement techniques
Neural networks
Personal computers
Prediction models
Principal components analysis
Sensors
Signal generators
Software
Spectrum analysis
Spain
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Summary: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s19184051