Detection of Different Lemon Pre-Harvest Additive Treatments by Means an Electronic Nose Prototype

Abstract

This study was carried out with a low-cost electronic nose prototype based on eight metal oxide sensors (MQ) in order to characterize samples of lemons treated with 0.5% and 0.1% of sodium benzoate. The MQ sensors designed are sensitive to one or more chemicals to detect the presence of a variety of chemicals in the air. The sensor MQ135 detects ammonia, hydrogen sulphide and benzene. Signal data were studied to obtain a pattern recognition of rotten in lemon fruits. Network analysis was used to obtain a calibration of measures among the stage of lemons. In this article, an electronic nose prototype based on 8 MQ metal oxide sensors has been used in order to analyze and characterize different lemon varieties to which different chemical treatments have been applied in pre-harvest. PCA-based data analyzes were used to observe clusters in the data. Through the combined use of the data obtained by the nose and these Sequential Neural Networks (SNNs) a classification tool for lemon varieties and applied treatments has been obtained. It is shown the ability of this device to be used as a reliable discrimination method, in addition to providing low cost and optimization of time and expert resources.

Code and data availability

The paper's e-nose sensor measurements (8 MQ sensors on treated/untreated Fino and Verna lemons), feature vectors, and analysis software are paper-specific assets, but no public deposit or URL is provided; the authors state data are available only on request. No code, model, or dataset repository is named.

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