Abu Hassan Abdullah
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Abu Hassan Abdullah
Official Name
Abdullah, Abu Hassan
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Scopus Author ID
26029734700

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  • Publication
    Improved classification of orthosiphon stamineus by data fusion of electronic nose and tongue sensors
    ( 2010)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Masnan, Maz Jamilah
    ;
    Abdul Hallis Abdul Aziz
    ;
    Nazifah Ahmad Fikri
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    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.
  • Publication
    Development of portable, application specific electronic nose for agriculture
    ( 2014)
    Abu Hassan Abdullah
    Research groups around the world are working to develop electronic nose systems that are able mimicking the functions and operations of the human nose. The instrument is used to identify and classify different types of odour or smell. The instrument will complement the existing odour assessment techniques; human sensory panels and Gas Chromatography Mass Spectrum (GC-MS) analysis which require long training time and detailed operating procedures. However most of the generic instruments are of laboratories type which are costly and may not operate efficiently for every possible application. The instruments’ broad non-specific sensor arrays’ will limit the detection capabilities. The existing portable instruments in the market are still lacking in reliability, data processing capabilities and quite costly. Therefore, the purpose of this research is to develop a portable Application Specific Electronic Nose (ASEN) to improve their capabilities. The developed instrument uses specific selected sensor arrays which were identified based on experiment and key volatile compounds of the target odorant. Humidity and temperature sensor are also being included in the instrument to measure the environmental condition. The instrument utilises multivariate statistical analysis (PCA, LDA and KNN) and Artificial Neural Network (ANN) as well as an embedded ANN classification algorithm for the data processing. This will increase the instrument’s capability while the portability will minimise the size, cost and operational complexity. A commercial instrument (Cyranose C320 from Smith Detection) is used to evaluate the performance of the instrument. The instrument was successfully developed, tested and calibrated odour samples with variable concentrations. The instrument provides a feasible alternative for non-destructive testing system for the odour samples. The results revealed that the developed instrument is able to identify, discriminate and classify the odour samples with an acceptable percentage of accuracy. This will contribute significantly to acquiring a new and alternative method of using the instrument for agriculture applications i.e., plant disease detection, food quality assurance and poultry farm malodour monitoring. The future works include the development of specific sensors for the application and simplified the training process i.e., performs on-line ANN training by the instrument itself.
  • Publication
    Improved maturity and ripeness classifications of magnifera indica cv. Harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor
    ( 2012)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Masnan, Maz Jamilah
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Mahmad Nor Jaafar
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    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.
      7  16
  • Publication
    A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration
    ( 2011-08)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Masnan, Maz Jamilah
    ;
    Norazian Subari
    ;
    Nazifah Ahmad Fikri
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Mahmad Nor Jaafar
    ;
    Latifah Munirah Kamarudin
    ;
    Abdul Hallis Abdul Aziz
    ;
    Abu Hassan Abdullah
    ;
    Supri A. Ghani
    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.
      18  19
  • Publication
    Improved Classification of Orthosiphon stamineus by Data Fusion of Electronic Nose and Tongue Sensors
    ( 2010)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Masnan, Maz Jamilah
    ;
    Abdul Hallis Abdul Aziz
    ;
    Nazifah Ahmad Fikri
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    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.
      14  15