RESUMEN
In this paper, a novel ceramic preconcentrator is manufactured using aluminum nitride (ALN) ceramics. The preconcentrator consists of a heater, a preconcentrator body, a gas inlet and a gas outlet. The adsorption material, Carbosieve SII, is loaded into the preconcentrator. The preconcentrator is integrated with a previously developed micro gas chromatographic system filled with ethylene. When operated, adequate ethylene gas is adsorbed into the preconcentrator. The application of heat pulse also successfully desorbs the ethylene gas. Tests are conducted with ethylene gas at concentrations of 10 ppm, 5 ppm and 2.5 ppm and 400 ppb, respectively. The system is also tested with ethylene gas from ripening bananas over a period of three days. No interference signal is observed in the chromatogram because of other ripening gases (e.g., carbon dioxide, oxygen, alcohol) and humidity. A detection limit of 25 ppb is realized with this system. The developed preconcentrator has several applications, e.g., in food industry and environmental monitoring.
RESUMEN
Ethylene gas is a naturally occurring gas that has an influence on the shelf life of fruit during their transportation in cargo ships. An unintentional exposure of ethylene gas during transportation results in a loss of fruit. A gas chromatographic system is presented here for the detection of ethylene gas. The gas chromatographic system was assembled using a preconcentrator, a printed 3D printed gas chromatographic column, a humidity sensor, solenoid valves, and an electrochemical ethylene gas sensor. Ambient air was used as a carrier gas in the gas chromatographic system. The flow rate was fixed to 10 sccm. It was generated through a mini-pump connected in series with a mass flow controller. The metal oxide gas sensor is discussed with its limitation in ambient air. The results show the chromatogram obtained from metal oxide gas sensor has low stability, drifts, and has uncertain peaks, while the chromatogram from the electrochemical sensor is stable and precise. Furthermore, ethylene gas measurements at higher ppb concentration and at lower ppb concentration were demonstrated with the electrochemical ethylene gas sensor. The system separates ethylene gas and humidity. The chromatograms obtained from the system are stable, and the results are 1.2% repeatable in five similar measurements. The statistical calculation of the gas chromatographic system shows that a concentration of 2.3 ppb of ethylene gas can be detected through this system.
RESUMEN
Fungus is enormously notorious for food, human health, and archives. Fungus sign and symptoms in medical science are non-specific and asymmetrical for extremely large areas resulting into a challenging task of fungal detection. Various traditional and computer vision techniques were applied to meet the challenge of early fungus detection. On the other hand, features learned through the convolutional neural network (CNN) provided state-of-the-art results in many other applications of object detection and classification. However, the large amount of data is an essential prerequisite for its effective application. In pursuing this idea, we present a novel fungus dataset of its kind, with the goal of advancing the state of the art in fungus classification by placing the question of fungus detection. This is achieved by gathering various images of complex fungal spores by extracting samples from contaminated fruits, archives, and laboratory-incubated fungus colonies. These images primarily consisted of five different types of fungus spores and dirt. An optical sensor system was utilized to obtain these images, which were further annotated to mark fungal spores as a region of interest using specially designed graphical user interface. As a result, 40,800 labeled images were used to develop the fungus dataset to aid in precise fungus detection and classification. The other main objective of this research was to develop a CNN-based approach for the detection of fungus and distinguish different types of fungus. A CNN architecture was designed, and it showed the promising results with an accuracy of 94.8%. The obtained results proved the possibility of early detection of several types of fungus spores using CNN and could estimate all possible threats due to fungus.