Abstract

The widespread use of nitrogen chemical fertilizers in modern agricultural practices has raised concerns over hazardous accumulations of nitrogen-based compounds in crop foods and in agricultural soils due to nitrogen overfertilization. Many vegetables accumulate and retain large amounts of nitrites and nitrates due to repeated nitrogen applications or excess use of nitrogen fertilizers. Consequently, the consumption of high-nitrate crop foods may cause health risks to humans. The effects of varying urea–nitrogen fertilizer application rates on VOC emissions from cucumber fruits were investigated using an experimental MOS electronic-nose (e-nose) device based on differences in sensor-array responses to volatile emissions from fruits, recorded following different urea fertilizer treatments. Urea fertilizer was applied to cucumber plants at treatment rates equivalent to 0, 100, 200, 300, and 400 kg/ha. Cucumber fruits were then harvested twice, 4 and 5 months after seed planting, and evaluated for VOC emissions using an e-nose technology to assess differences in smellprint signatures associated with different urea application rates. The electrical signals from the e-nose sensor array data outputs were subjected to four aroma classification methods, including: linear and quadratic discriminant analysis (LDA-QDA), support vector machines (SVM), and artificial neural networks (ANN). The results suggest that combining the MOS e-nose technology with QDA is a promising method for rapidly monitoring urea fertilizer application rates applied to cucumber plants based on changes in VOC emissions from cucumber fruits. This new monitoring tool could be useful in adjusting future urea fertilizer application rates to help prevent nitrogen overfertilization.

Highlights

  • Fresh market produce, such as raw fruits and vegetables, are popular nutritional foods, because they are rich in important vitamins, minerals, antioxidants, and nutrients that provide positive benefits to human health [1,2]

  • The objectives of this study were to: (1) determine the effects of different rates of urea fertilizer applications on cucumber fruit volatile organic compounds (VOCs) emissions based on measured differences in the collective responses of an experimental MOS enose sensor array; (2) determine whether differences in the e-nose smellprint signatures or sensor array output data may be used as a tool to discriminate and classify cucumbers fruits derived from plants receiving varying rates of urea fertilizer; and (3) evaluate four statistical aroma classification methods (including linear and quadratic discriminant analysis (LDA and QDA), support vector machines (SVM), and artificial neural networks (ANN), for being capable of classifying volatile emissions from cucumber fruits based on e-nose data associated with the amount of urea fertilizer applied to individual cucumber plants

  • An experimental electronic nose device, consisting of eight commercial MOS gas sensors used in combination with four statistical models (LDA, QDA, SVM, and ANN) in this study, was capable of classifying cucumber fruits based on differences in the enose sensor array outputs resulting from different amounts of urea fertilizer applied to cultivated plants

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Summary

Introduction

Fresh market produce, such as raw fruits and vegetables, are popular nutritional foods, because they are rich in important vitamins, minerals, antioxidants, and nutrients that provide positive benefits to human health [1,2]. Cucumber (Cucumis sativus L.), a member of the Cucurbitaceae plant family, is an economically important crop cultivated throughout the world. Cucumbers are popular in North American, Asian, and European countries, where their consumption is highest [6,7]. The demand for fresh produce is growing worldwide as consumers increasingly recognize the nutritional value and health benefits provided by these foods, but this situation may be contributing to higher rates of consumption of produce with excessive levels of nitrates due to the overapplication of nitrogen fertilizers [8,9]

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