A. H. Abdullah

A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration.

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.

Improved Classification of Orthosiphon stamineus by Data Fusion of Electronic Nose and Tongue Sensors

An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.

Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor

In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.

Hand-Held Electronic Nose Sensor Selection System for Basal Stamp Rot (BSR) Disease Detection

Electronic Nose (e-nose) is an intelligent instrument that is able to classify different types of odours. The e-nose applications include food quality assurance, fragrance industry, medical diagnosis, environmental monitoring, agricultural industry and homeland security. The current e-nose design trend are portable, small size, low power consumption, high processing power using embedded controller and easy to operate to enable it to perform the designed tasks effectively. This paper deals with the design issues of a hand-held e-nose based on sensor selection and optimum embedded controller capabilities. A summary of proposed hardware and software solutions are provided with emphasis on data processing. The data processing utilizes multivariate statistical analysis i.e. Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Linear Discriminate Analysis (LDA). The developed instrument was tested to discriminate the Ganoderma boninense fruiting body (basidiocarp). Initial results show that the instrument is able to discriminate the samples based on their odour chemical fingerprint profile.

Method to convert Kinect's 3D depth data to a 2D map for indoor SLAM

Mobile robotics has been strongly linked to localization and mapping especially for navigation purpose. A robot needs a sensor to see objects around it, avoid them and also map the surrounding area. The use of 1D and 2D proximity sensors such as ultrasonic sensor, sonar and laser range finder for area mapping is believed to be less effective since they do not provide information in Y or Z (horizontal and vertical) direction. The robot may miss an object due to its shape and position; thus increasing the risk of collision as well as inaccurate map. In this paper, a 3D visual device particularly Microsoft Kinect was used to perform area mapping. The 3D depth data from the devices depth sensor was retrieved and converted into 2D map using the presented method. A Graphical User Interface (GUI) was also implemented on the base station to depict the real-time map. It was found that the method applied has successfully mapped the potentially missing objects when using 1D or 2D sensor. The convincing results shown in this paper suggest that the Kinect is suitable for indoor SLAM application given that the devices limitations are solved.

P2.1.7 Exploring MIP Sensor of Basal Stem Rot (BSR) Disease in Palm Oil Plantation

In Malaysia, the production of palm oil is hampered by the infection of Basal Stem Rot (BSR) disease. Unfortunately, the existing BSR detection techniques are complex, time-consuming and still not fully developed. So, this research proposes an investigation to identify the volatile compounds (biomarkers) of the infected oil palm trees. The biomarkers will be proposed to develop specific Molecularly Imprinted Polymer (MIP) sensors. The sensor will be used for the development of ‘Application Specific Electronic Nose’ (ASEN). The samples were taken from healthy and infected BSR disease tree. Fourier Transform Infrared (FTIR) and Headspace Gas Chromatography Mass Spectrometry (GC-MS) analysis is used to identify the biomarkers. The results show that the technique has successfully detected and identified the volatile compounds biomarkers for BSR disease of infected oil palm tree.

Flexible and autonomous integrated system for characterizing metal oxide gas sensor response in dynamic environment

Characterization and calibration of gas sensor is a complex problem due to the dynamic behavior of gases and the limitations of current technology. This article reports a flexible, robust, and autonomous integrated system that is able to perform characterization on metal oxide-based gas sensors in dynamic environments. The system controls the concentration and flow of the relevant gases into the gas chamber and simultaneously measuring the sensor response. This feature allows the characterization of the sensor under continuous dynamic flow of gases similar to conditions on a robot or flow pipes. Several experiments have been performed on the system using hydrogen sulfide. The results provide information on the general characteristics of the sensor as well as its sensitivity. The noise levels were studied with different reference voltages. Overall, the results verify that the system is reliable and able to produce repeatable measurements.

Classification of Malaysia aromatic rice using multivariate statistical analysis

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic...

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.

Braitenberg swarm vehicles for odour plume tracking in laminar airflow

This paper presents an algorithm to trace an odour plume using swarm robots in laminar airflow. The algorithm proposed here aims to bridge the gap between single and multiple element systems by mimicking and enhancing biologically derived strategies for odor plume tracking. Simulations were carried out on Webots to verify the potential of the algorithm. A simple gas sensor model was introduced to mimic the response of a real metal oxide sensor in the simulation. A gas sensor model was introduced based on the response of metal oxide sensor (MOS) to closely mimic and provide real environment condition. Different weightage configurations of the gas sensor, kg and wind sensor, kw are compared to find its effects on the performance and behavior of the purposed algorithm. It was found that robots separated from the swarm can still perform the plume tracking task. Also, multiple entity systems show an increase in performance compared to single entity robots.