Muhammad Waseem Tahir

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.

Comparison of Several Optical Methods for an Automated Fungal Spore Sensor System Concept

Molds and their spores are important to the global ecosystem. Thus, the thousands of different mold species are present worldwide and their spores can be found almost everywhere. Dangerous fungal contaminations that pose threats to the economy and human health are problematic especially in the areas of public buildings (hospitals), food industry (food transport), and private residences. A simple and cost-effective sensor system for the detection of fungal spore concentrations in air would minimize this risk. This paper presents an optical sensor system developed for the detection of airborne spores. Results from both this paper and the functional tests of the fabricated parts were used to develop a concept for a fully automated fungal spore sensor system, which can detect a contamination within minutes. This sensor system is based on a commonly used manual technique, in which the pictures of particles in air samples are analyzed. In the first step, the particles in an air sample were collected and fixed onto an adhesive surface. Afterward, the images were captured using a color camera and a microscope objective. The system allows images to be captured under three different microscopy methods: bright-field, dark-field, and fluorescence microscopy. The images were subsequently analyzed by software, and the analysis results from the three microscopy methods were compared. The results showed that the system was able to detect, differentiate, and count spores within minutes. This system also offers the major advantage of not requiring any prior incubation. Furthermore, based on the innovative use of adhesive tape that can serve as an automated microscopy surface, the system can be made simple and cost-effective to operate.

Optical sensor system for the detection of mold: Concept for a fully automated sensor system for the detection of airborne fungal spores

Fungal contaminations in public buildings, food industry or private residences are a risk to economy and human health. A new innovative optical sensor system developed for the detection of airborne fungal spores is presented in this document. Furthermore, the results from the study were used to develop a concept for a fully automated mold sensor system which detects a contamination in minutes. The functionality of the sensor is based on the analysis of air samples via image processing. Air aerosol is collected and prepared on adhesive tape. Images were captured afterwards using a color camera and a microscope objective. As last step the images were analyzed by software. The end results of this work showed that the system was able to detect, differ and count spores within minutes without the need of incubation. Furthermore, based on the innovative use of an adhesive tape the automation will be easy and cost-effective.

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.

Fungus Detection Through Optical Sensor System Using Two Different Kinds of Feature Vectors for the Classification

This revolutionary era emphasizes the need of novel, simple, automatic fungal detection system to control the devastation caused by fungal species. Fungus is highly notorious for human health, food, and archives. We have developed a device and algorithms to automatically detect fungus from air by employing optical sensor system. Several air samples were collected and examined under high magnification microscopic camera in order to take images of samples through the designed system. System collects three different types of images (dark-field, bright-field, and auto fluorescence) with the help of different light sources. This provided detailed temporal variation of fungus spores to effectively analyze fungus images. Two different algorithms were developed for fungus spore detection: hand crafted features and histogram of oriented gradients as features. Then, support vector machine was used for the purpose of classification. Furthermore, we compared the fungus spores count obtained with our algorithm and manual counting method. The performance of the proposed system on new images obtained by the optical sensor system was evaluated and all results of precision, recall, and accuracy are also presented in the paper. Convincing results were obtained in favor of the proposed approach for precision recall ratios. The obtained results proved the possibility of developing a device for early detection of several types of fungus spores. The system successfully detected the number of fungus spores present in the air and could estimate all possible threats.

Fungus Detection System

Fungus is the main risk for food logistics and millions of euro lost per annum just due to different kinds of fungus. The main aim of this research is to develop an automated system for the detection of fungus spores in air. We are developing a novel system for fungus detection which is based on computer vision. Air samples are collected on the glass slides and then placed these samples under microscopic camera and images of air samples are obtained. Pre processing techniques, as images have noise regions and spores boundaries are not clear, have been applied and different filters have been used. Which is followed by thresholding and morphologic operation. Then features are extracted and feature vector were formed. After that Support Vector Machine (SVM) was used for the purpose of classification.

Detection of fungus through an optical sensor system using the histogram of oriented gradients

Fungus is an important component in our ecosystem. It performs an important task of decomposition. But on the other hand, it is the main risk for human health, archives, food logistics and millions of euros lost per annum just due to different kinds of fungus. The main aim of this research is to develop an automated system for the detection of fungus spores in air. We have developed a novel system for fungus detection through an optical sensor system. First of all, our system will collect air samples. Then, the handling system moved them under a microscopic camera and get images of the sample. Since images have noise regions and the spore boundaries were unclear, pre-processing techniques have been applied and different filters have been used. Then, the histogram of oriented gradient (HOG) features were extracted and the feature vector was formed. Following this, a trained Support Vector Machine (SVM) was used for the purpose of classification.

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.

Development of a Fungal Risk Monitor for the next generation of intelligent containersaper

The global food logistics play an important role in our daily lives. Fruits, meat and other food products are transported all over the world. This fact comes with challenges which have to be solved for a sustainable and economical food supply. For example, fruits farmed in South America are usually transported by cargo ships over 21 days to Europe. During this long trip, the fruits could already start to ripen or turn moldy. In situations where a shipment of produce have already turned bad, the food products will have to be discarded away, with losses that can amount up to millions of euros every year. This paper presents a concept for a Fungal Risk Monitor (FRM) which could minimize losses through early warning in the event of stressful environmental conditions. The concept aims to tackle the logistical challenges involved in banana transport. The FRM consists of a sensor network which measures environmental conditions at the banana farm and during the transport inside the container. Based on the results from the “Intelligent Container”, the measurement data is collected centrally and the information can be used to calculate the risk of a fungal contamination during transport. In addition, the here discussed sensor network in the form of a banana box showed its ability to measure mold triggering conditions with accuracy. This is thereby the first step to the FRM and represents the container environment sensor network.