Abstract
The physiological processes underlying fruit ripening can lead to different electrical signatures at each ripening stage, making it possible to classify tomato fruit through the analysis of electrical signals. Here, the electrical activity of tomato fruit (Solanum lycopersicum var. cerasiforme) during ripening was investigated as tissue voltage variations, and Machine Learning (ML) techniques were used for the classification of different ripening stages. Tomato fruit was harvested at the mature green stage and placed in a Faraday's cage under laboratory-controlled conditions. Two electrodes per fruit were inserted 1 cm apart from each other. The measures were carried out continuously until the entire fruits reached the light red stage. The time series were analyzed by the following techniques: Fast Fourier Transform (FFT), Wavelet Transform, Power Spectral Density (PSD), and Approximate Entropy. Descriptive analysis from FFT, PSD, and Wavelet Transform were used for PCA (Principal Component Analysis). Finally, ApEn, PCA1, PCA2, and PCA3 were obtained. These features were used in ML analyses for looking for classifiable patterns of the three different ripening stages: mature green, breaker, and light red. The results showed that it is possible to classify the ripening stages using the fruit's electrical activity. It was also observed, using precision, sensitivity, and F1-score techniques, that the breaker stage was the most classifiable among all stages. It was found a more accurate distinction between mature green × breaker than between breaker × light red. The ML techniques used seem to be a novel tool for classifying ripening stages. The features obtained from electrophysiological time series have the potential to be used for supervised training, being able to help in more accurate classification of fruit ripening stages.
Highlights
Ripening is a part of fruit development in which biochemical and physiological changes occur, making the fruit more attractive to seed dispersers and consumers (Prasanna et al, 2007; Corpas et al, 2018)
We propose the use of the fruit electrical activity, more precisely, the analysis of the electrome recordings as a source for parameters to support the classification of the ripening stages by Machine Learning (ML) techniques
The bioelectrical signals were acquired during the fruit ripening in data acquisitions throughout 24 h until all fruits were in the light red (LR) stage
Summary
Ripening is a part of fruit development in which biochemical and physiological changes occur, making the fruit more attractive to seed dispersers and consumers (Prasanna et al, 2007; Corpas et al, 2018). Fleshy fruits are usually categorized in climacteric and non-climacteric, according to the ripening pattern in terms of respiratory rate and the production of the phytohormone ethylene (Pérez-Llorca et al, 2019). Non-climacteric fruits do not show an increase in respiration and ethylene synthesis, and generally the respiratory rate decreases as the ripening progresses. This type of fruit must end the ripening process connected to the plant (Corpas et al, 2018). Tomato (Solanum lycopersicum L.) is an example of climacteric fruit widely used as a model in research that involves ripening and ethylene signaling (Klee and Giovannoni, 2011)
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