Abdul Hallis Abdul Aziz
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Preferred name
Abdul Hallis Abdul Aziz
Official Name
Abdul Hallis, Abdul Aziz
Alternative Name
Aziz, Abdul Hallis
Main Affiliation
Scopus Author ID
7103371906
Researcher ID
AAA-1798-2019

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Implementation and performance of a taste sensing system for quality assessment of agrobased products

2011 , Abdul Hallis Abdul Aziz

The trend in human food consumption is seeing a shift in demand, from consumer’s side demanding increase of health elements in food and from manufacturer’s side demanding higher palatability. Currently, quality of farm production is not verifiable and its quality claim is very dependent on farmers very own personal validation and rationale made by selected human panelist. Laboratory verification on the other hand, is cost prohibitive, requires trained labor to operate and yet does not indicate taste, which is a mixture of chemicals. This thesis presents the design, characterization and performance analysis of a taste sensing system (TSS) in assessing the quality of agro-based products. The system integrates off-the-shelf components to incorporate simplicity in reproducing the system while maintaining affordable cost. Operation interface is presented as graphical user interface (GUI) to allow operation by non-technical personnel in other discipline. The TSS exhibit exceptional ability (above 80%) in discriminating quantitative taste samples and perfect (100%) discrimination of qualitative samples.

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Improved classification of Orthosiphon stamineus by data fusion of electronic nose and tongue sensors

2010-10-01 , Ammar Zakaria , Ali Yeon Md Shakaff , Abdul Hamid Adom , Mohd Noor Ahmad , Maz Jamilah Masnan , Abdul Hallis Abdul Aziz , Fikri N. , 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. © 2010 by the authors.

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Publication

Test frequency optimization using single factor ANOVA for capacitive oil palm fruit ripeness sensor

2020 , Abdul Hallis Abdul Aziz , Rohaslida Liyana Mohmad , Siti Azura Abuzar , Mas Elyna Azol , Nurol Husna Che Rose

The traditional method of palm oil fruit ripeness grading requires manual human observation imposing time consumption. This paper describes the work to determine best frequency gritted for capacitive ripeness grading. To find the ripeness, the frequency response of ripe and unripe fruitlet in between capacitive plates is evaluated. Parallel plate that is connected to resistor in series is used as the sensor. The function generator is used to give a sinusoidal wave frequency or known as Vin that gives input from 20 kHz to 300 kHz and the oscilloscope is used to obtain the output reading which is the difference in value of the Vpp. From the result, it is observed that there is difference between ripe and unripe fruitlet also the frequency of 100 kHz and below shows that the reading can be differentiate between fruitlet. After the frequency past the 100 kHz the reading is hard to differentiate before at one point the graph will become static even the weight of each fruitlet is difference.

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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.

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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.

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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.

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