Ali Yeon Md Shakaff
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Preferred name
Ali Yeon Md Shakaff
Official Name
Ali Yeon, Md Shakaff
Alternative Name
Shakaff, A. Y. M.
Shakaff, A. Y.Md
Shakaff, A. Y.
Shakaff, Ali Y.M.
Main Affiliation
Scopus Author ID
8721012500
Researcher ID
DPT-4421-2022

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Labview™ for Nutra-Biostrip in Herbal Quality Assessment

2004 , Mohd Noor Ahmad , Maxsim Yap Mee Sim , Mohd Kamal Mohamed Ramly Nil , Ali Yeon Md Shakaff , Chang Chew Cheen

In this work, we introduce the approach on the development of a stand-alone laptop based data acquisition of an array sensor system, namely Nutra-BioStrip coupled with pattern recognition algorithm for herbal quality assessment. The array sensor system control program, developed in Lab View 6. 1 programming languages allow data acquired from the array sensor to be analyzed by means of Principal Component Analysis (PCA) and displayed in the form of an interactive twodimensional cluster mapping with detail statistical analysis results for rapid and real-time herbal quality assessment.

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Publication

Monitoring of milk quality with disposable taste sensor

2003 , Maxsim Sim , Teo Jau Shya , Mohd Noor Ahmad , Ali Yeon Md Shakaff , Abdul Othman , Muhammad Hitam

A disposable screen-printed multi channel taste sensor composed of several types of lipid as transducers and a computer as data analyzer could detect taste in a manner similar to human gustatory sensation. The disposable taste sensor was used to measure the electrical potential resulted from the interaction between lipid membranes and taste substances. In the present study, two types of packaged commercial milk, the ultra high temperature (UHT) and the pasteurized milk were tested. It was found that the disposable taste sensor is capable to discriminate reliably between fresh and spoiled milk and to follow the deterioration of the milk quality when it is stored at room temperature based on a pattern recognition principle namely Principle Component Analysis (PCA). This research could provide a new monitoring method ideally for simple and cheap decentralized testing for controlling the quality of milk, which may be of great use in the dairy industries.

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Disposable E-Tongue for the assessment of water quality in fish tanks

2008 , Chew-Cheen Chang , Bahruddin Saad , Misni Surif , Mohd Affendi Ahmad Pozin , Ali Yeon Md Shakaff

A disposable screen-printed e-tongue based on sensor array and pattern recognition that is suitable for the assessment of water quality in fish tanks is described. The characteristics of sensors fabricated using two kinds of sensing materials, namely (i) lipids (referred to as Type 1), and (ii) alternative electroactive materials comprising liquid ion-exchangers and macrocyclic compounds (Type 2) were evaluated for their performance stability, sensitivity and reproducibility. The Type 2 e-tongue was found to have better sensing performance in terms of sensitivity and reproducibility and was thus used for application studies. By using a pattern recognition tool i.e. principal component analysis (PCA), the e-tongue was able to discriminate the changes in the water quality in tilapia and catfish tanks monitored over eight days. E-tongues coupled with partial least squares (PLS) was used for the quantitative analysis of nitrate and ammonium ions in catfish tank water and good agreement were found with the ion-chromatography method (relative error, ±1.04- 4.10 %).

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Classifying sources influencing Indoor Air Quality (IAQ) using Artificial Neural Network (ANN)

2015 , Shaharil Mad Saad , Allan Melvin Andrew , Ali Yeon Md Shakaff , Abdul Rahman Mohd Saad , Azman Muhamad Yusof @ Kamarudin , Ammar Zakaria

Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

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