Rapid Detection of Urea Fertilizer Effects on VOC Emissions from Cucumber Fruits Using a MOS E-Nose Sensor Array

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.

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

“…For the six-group classification, based on the type of extract, the polynomial and RBF functions had a classification accuracy of 98.9 in the C-SVM method, while in the Nu-SVM method the classification accuracy of linear functions and RBF was 100% in learning and 98.9% in validation. Similar results have been reported for other crops, such as grape leaves [ 44 ], fruit juices [ 45 ], essential oils [ 4 , 16 ], coffee bean [ 46 ], corn [ 47 ], and cucumbers [ 48 ].…”

Review on Discrimination of Hazardous Gases by Smart Sensing Technology

“…MOS (metal oxide Semiconductor) e-nose sensor array was used for sensing and detection of VOC differences in small print signatures associated with different urea application rates. The signals received from the sensor array were subjected to four different statistical models linear and quadratic discriminant analysis (LDA-QDA), support vector machines (SVM), and artificial neural networks (ANN) (Tatli et al, 2021) . A comparision of gas sensor and their sensing technique is shown in table 3.A gas sensor was fabricated for detection and discrimination of chloroform, methane, and ethanol by using an array of 4 sensors by using a sensing layer as pTh.…”

Nondestructive Technique for Identifying Adulteration and Additives in Lemon Juice Based on Analyzing Volatile Organic Compounds (VOCs)

“…Gas sensors play an important role in many areas of human life, including the monitoring of production processes, occupational safety, food quality assessment, and air pollution monitoring [24]. Essentially, an electronic nose is a device that includes a multisensor array and multidimensional signal processing through pattern recognition algorithms that can detect the presence of volatile compounds associated with food aromas [25]. In this technique, the focus of the analysis process is not on the identification and quantification of volatile compounds, but on the quantitative description of the complete characteristics of the fragrance, including the relationships between its components [26].…”