Design of Novel Ceramic Preconcentrator and Integration in Gas Chromatographic System for Detection of Ethylene Gas from Ripening Bananas.

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.

A Gas Chromatographic System for the Detection of Ethylene Gas Using Ambient Air as a Carrier Gas.

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.

An Impedance-Based Mold Sensor with on-Chip Optical Reference.

A new miniaturized sensor system with an internal optical reference for the detection of mold growth is presented. The sensor chip comprises a reaction chamber provided with a culture medium that promotes the growth of mold species from mold spores. The mold detection is performed by measuring impedance changes with integrated electrodes fabricated inside the reaction chamber. The impedance change in the culture medium is caused by shifts in the pH (i.e., from 5.5 to 8) as the mold grows. In order to determine the absolute pH value without the need for calibration, a methyl red indicator dye has been added to the culture medium. It changes the color of the medium as the pH passes specific values. This colorimetric principle now acts as a reference measurement. It also allows the sensitivity of the impedance sensor to be established in terms of impedance change per pH unit. Major mold species that are involved in the contamination of food, paper and indoor environments, like Fusarium oxysporum, Fusarium incarnatum, Eurotium amstelodami, Aspergillus penicillioides and Aspergillus restrictus, have been successfully analyzed on-chip.

Amelioration of toxicopathological effects of cadmium with silymarin and milk thistle in male Japanese quail (Coturnix japonica).

Cadmium is an important widely distributed heavy metal in the environment due to its several industrial uses, while milk thistle is an important herb and is a source of several antioxidant particularly silymarin which is a pharmacological active substance present in seeds of milk thistle plant (Silybum marianum). The current study investigated pathological effects of cadmium (Cd) and their amelioration with silymarin (SL) and milk thistle (MT) quails. A total of 144 quails were equally divided into 9 groups and given different combinations of cadmium chloride (150 and 300 mg/kg feed), SL (250 mg/kg), and MT (10 g/kg) feed. Parameters studied were clinical signs, mortality, organ weights, testes weight and volume, and gross and microscopic pathology. Results of this study indicated an increased mortality and reduced body weight in cadmium-treated quails. Quails were dull, depressed compared with control. Swollen hemorrhagic liver along with atrophied testes were also observed in these groups. No active spermatozoa were observed in lumen of seminiferous tubules of Cd-treated birds presenting arrest of spermatogenesis. Supplementing MT and SL ameliorated mortality, organ weights, spermatogenesis, and histopathological lesions. It may be concluded that MT and SL proved beneficial in cadmium-induced toxicities in Japanese quails.

A Fungus Spores Dataset and a Convolutional Neural Network Based Approach for Fungus Detection.

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.