Nurlisa Yusuf

In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology

BackgroundEffective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.ResultsThis study investigates the performance of e-nose technique performing direct measurement of static headspace with algorithm and data interpretations which was validated by Headspace SPME-GC-MS, to determine the causative bacteria responsible for diabetic foot infection. The study was proposed to complement the wound swabbing method for bacterial culture and to serve as a rapid screening tool for bacteria species identification. The investigation focused on both single and poly microbial subjected to different agar media cultures. A multi-class technique was applied including statistical approaches such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA) as well as neural networks called Probability Neural Network (PNN). Most of classifiers successfully identified poly and single microbial species with up to 90% accuracy.ConclusionsThe results obtained from this study showed that the e-nose was able to identify and differentiate between poly and single microbial species comparable to the conventional clinical technique. It also indicates that even though poly and single bacterial species in different agar solution emit different headspace volatiles, they can still be discriminated and identified using multivariate techniques.

Bacteria Classification Using Electronic Nose for Diabetic Wound Monitoring

Array based gas sensor technology namely Electronic Nose (E-nose) now offers the potential of a rapid and robust analytical approach to odor measurement for medical use. Wounds become infected when a microorganism which is bacteria from the environment or patients body enters the open wound and multiply. The conventional method consumes more time to detect the bacteria growth. However, by using this E-Nose, the bacteria can be detected and classified according to their volatile organic compound (VOC) in shorter time. Readings were taken from headspace of samples by manually introducing the portable e-nose system into a special container that containing a volume of bacteria in suspension. The data will be processed by using statistical analysis which is Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods. The most common bacteria in diabetic foot are Staphylococcus aureus, Escherchia coli, Pseudomonas aeruginosa, and many more.

Water quality classification and monitoring using e-nose and e-tongue in aquaculture farming

Aquaculture is an important to national food security. Productivity of aquaculture farms hinges on water quality. Lack of appropriate instrumentation for measurement of water quality is a hindrance to the industry. This experiment proposed and verify the application of e-nose and e-tongue for water quality parameters for shrimp farming. Results indicated it has the potential but required additional analytical techniques. Thus, by using sensor array technologies, e-nose and e-tongue has been employed in classification of different type of water that has been used in aquaculture farming. E-nose consists of 10 metal oxide sensors meanwhile e-tongue consists of 13 working electrodes and one reference electrode. Linear Discriminant Analysis (LDA) was used as data classifier. The e-nose and e-tongue was able to classify different type of water with the accuracy up to 95%. These results show the potential use of e-nose and e-tongue to classify the different type of water used in aquaculture industry.

Understanding Mahalanobis distance criterion for feature selection

Distance criteria are widely applied in cluster analysis and classification techniques. One of the well known and most commonly used distance criteria is the Mahalanobis distance, introduced by P. C. Mahalanobis in 1936. The functions of this distance have been extended to different problems such as detection of multivariate outliers, multivariate statistical testing, and class prediction problems. In the class prediction problems, researcher is usually burdened with problems of excessive features where useful and useless features are all drawn for classification task. Therefore, this paper tries to highlight the procedure of exploiting this criterion in selecting the best features for further classification process. Classification performance for the feature subsets of the ordered features based on the Mahalanobis distance criterion is included.

Bio-inspired taste assessment of pure and adulterated honey using multi-sensing technique

Current studies document the effectiveness of multi-sensing technique implementation to mimic or to complement human senses. This work demonstrated the successful application of multi-sensing techniques such electronic tongue (e-tongue), electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR). The fusion of these modalities enhance the classification of pure Tualang honey using Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). KNN and PNN are able to classify between pure and adulterated honey samples, outperform LDA and SVM. By performing data fusion, SVM and LDA classifier can achieved more than 80% accuracy, while KNN and PNN obtained greater precision, up to 96% correct classification. The findings confirmed that, multi-sensing technique; either KNN or PNN was significantly superior compared to SVM and LDA classification methods. Thus, both analyses are able to discriminate between pure and adulterated honey.

A preliminary study on in-vitro lung cancer detection using E-nose technology

The existing clinical diagnostics for lung cancer are mostly based on physics, biochemical and imaging techniques. The use of electronic nose (E-nose) system to detect volatile organic compounds (VOCs) in lung cancer cells or exhaled air breath of a patient is expected to be able to classify different volatile components leading to the diagnosis of lung cancer at an early stage. In this preliminary study, a commercialized E-nose consists of an array of 32 conducting polymer sensors (Cyranose 320) was used to detect and discriminate the VOCs emitted from cancer cells which is A549 (lung cancer cell line) between MCF7 (breast cancer cell line). Blank medium was used to obtain controlled value. The VOC profiles of each sample were characterized using a classification algorithm called k-Nearest Neighbors (KNN) to test and benchmark the performance of Enose in identifying VOCs of lung cancer from different cancer cell lines. The E-nose with KNN classifier was able to classify the VOCs of lung cancer cell with over 90% successful accuracy in 30 seconds. This study can conclude that e-nose is capable to rapidly discriminate volatile organic compounds of cancerous cells which generated during cell growth.

Evaluation of E-nose technology for detection of the causative bacteria in different culture media on diabetic foot infection

The three different culture media namely blood agar, Mueller Hinton and MacConkey were used in this study to identify and classify the causative bacteria on diabetic foot infection using electronic nose (E-nose). All the samples were taken from the clinical specimens using standard swabbing technique. E-nose consisting an array of 32 conducting polymer sensors was used to detect volatile organic compounds (VOCs) released by the bacteria in the infected areas. The VOC profiles of three bacterial groups from three genera namely Escherichia coli (ECOLI), Staphylococcus aureus (SAU) and Pseudomonas aeruginosa (PAE) were characterized using statistical classification technique called Linear Discriminant Analysis (LDA) to differentiate between different agars used with individual bacteria species which accounted for all the data. Although these methods are still fundamental, there is an increasing shift toward molecular diagnostics of bacteria. This investigation showed that the E-nose was able to correctly classify different bacterial species in all three culture media with up to 90% accuracy.

Cancer detection using an electronic nose: A preliminary study on detection and discrimination of cancerous cells

Lack of effective tools to diagnose lung cancer at an early stage has caused high mortality in cancer patients especially in lung cancer patients. Electronic nose (E-Nose) technology is believed to offer non-invasive, rapid and reliable analytic approach by measuring the odour released from cancer to assist medical diagnosis. In this work, using a commercial E-nose (Cyranose-320), we aimed to detect the volatile organic compounds (VOCs) emitted by different types of cancerous cells. The lung cancer cell (A549) and breast cancer cell (MCF-7) were used for this study. Both cells were cultured using Dulbeccos Modified Eagles Medium (DMEM) with 10% of Fetal Bovine Serum (FBS) and incubated for three days. The static headspace of cell cultures and blank medium were directly sniffed by Cyranose-320. The preliminary results from this study showed that, the E-nose is able to detect and distinguish the presence of VOCs in cancerous cells with accuracy of 100% using LDA. To this end, the VOCs emitted from cancerous cells can potentially used as biomarker.

Review on exhaled volatile organic compounds from lung cancer and advances of E-nose as non-invasive detection method

Lung cancer (LC) is known as the most common cancers and becoming the leading cause of cancer related death in human. The high mortality in lung cancer patient occurs because of lack of efficient methods to diagnose the disease at an early stage. In this review, we highlighted the studies conducted on compounds in exhaled air breath and metabolic pathway alteration in lung cancer patient, which may influence the alterations of volatile organic compounds (VOCs) in exhaled air breath. This review has shown that VOCs from exhaled air breath of lung cancer patient has potential to be used as lung cancer biomarker to diagnose lung cancer at primary stage by developing advanced technology of electronic nose system.

Aroma and optical absorption spectroscopy for quality assessment of vegetable cooking oils

Vegetable oils from different type of sources may have a distinctive aroma and flavour. This work explored the ability of combining PEN3 and UV-Vis Spectrophotometer for aroma and volatiles analysis. Nine types of vegetable oils were characterized and classified into three categories based on aroma and volatiles absorption characteristics which are fresh, heated and used cooking oil. The results of PCA analysis showed a good separation among three groups of vegetable cooking oil. Data set from both PEN3 (e-nose) and UV-Vis Spectroscopy was subjected to Linear Discriminant Analysis. Our results propose that discriminant analysis provides a rapid, efficient and accurate study for multi-class classification difficulties. LDA is capable to provide 100.0% correct classification of original grouped cases. However, only 85.4% of un-known grouped cases are correctly classified.