Papers by DR. MOHD. ZULI JAAFAR
PIXE analysis of PM 2.5 and PM 2.5-10 for air quality assessment of Islamabad, Pakistan: Applicat... more PIXE analysis of PM 2.5 and PM 2.5-10 for air quality assessment of Islamabad, Pakistan: Application of chemometrics for source identification
The Malaysian Journal of Analytical Sciences, 2014
Advanced Science Letters, 2017
The Malaysian Journal of Analytical Sciences, 2013
AIP Conference Proceedings, 2018
Differential effect of heated and non-heated gelatin samples on the quality and quantity of extra... more Differential effect of heated and non-heated gelatin samples on the quality and quantity of extracted DNA
MATEC Web of Conferences, 2016
Analysis of gunshot residue (GSR) is a crucial evidences for a forensic analyst in the fastest wa... more Analysis of gunshot residue (GSR) is a crucial evidences for a forensic analyst in the fastest way. GSR analysis insists a suitable method provides a relatively simple, rapid and precise information on the spot at the crime scene. Therefore, the analysis of Cu(II) in GSR using cyclic voltammetry (CV) on screen printed carbon electrode (SPCE) is a better choice compared to previous alternative methods such as Inductive Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) those required a long time for analysis. SPCE is specially designed to handle with microvolumes of sample such as GSR sample. It gives advantages for identification of copper in GSR on-site preliminary test to prevent the sample loss on the process to be analyzed in the laboratory. SPCE was swabbed directly on the shooter's arm immediately after firing and acetate buffer was dropped on SPCE before CV analysis. For ICP-OES analysis, cotton that had been soaked in 0.5 M nitric acid was swabbed on the shooter's arm immediately after firing and kept in a tightly closed sampling tube. Gold coated SPCE that had been through nanoparticles modification exhibits excellent performance on voltammograms. The calibration was linear from 1 to 50 ppm of copper, the limit of detection for copper was 0.3 ppm and a relative standard deviation was 6.1 %. The method was successfully applied to the determination of copper in GSR. The Cu determination on SPCE was compared and validated by ICP-OES method with 94 % accuracy.
2015 International Conference on Computer, Communications, and Control Technology (I4CT), 2015
In the development of drugs compounds suitable for human being, many experiments have to be condu... more In the development of drugs compounds suitable for human being, many experiments have to be conducted to ensure drugs safe consumption and generally takes almost 10 to 12 years for a particular drugs to enter the market from laboratory. Therefore, the pattern recognition in QSAR is significant for analyzing the data and developing several necessary models, so that only novel drugs candidate will be synthesized. There are three important aspects for the classification of BBB activity in this work, (1) variable reduction by PCA (2) variable selection and class separation with comparison of three methods such as T-Statistics, Partial Least Squares Regression Coefficient (PLSRC) and newly invented Self Organising Maps Discriminatory Index (SOMDI). and (3) classification, a comparison of linear (PLSDA) and non linear (SuSOMs) methods. The number of PCA component determined by LOO cross-validations is seven. Based on PCA score, the variables selected by T-Statistics and SOMDI are more selective and can provide better separation for BBB activity than PLSRC. Models performances and validations, built through PLSDA and SOMs show that the consensually selected 7 descriptors in this work by using SOMDI, T-statistics and PLSRC were able to classify BBB penetration and non-penetration compounds.
2014 IEEE Conference on Systems, Process and Control (ICSPC 2014), 2014
ABSTRACT
In this study, Artemisinin compounds that were classified according to their antimalarial activit... more In this study, Artemisinin compounds that were classified according to their antimalarial activity values were used as a data set to develop predictive classification models namely Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Linear Vector Quantization (LVQ) and Quadratic Discriminant Analysis (QDA). The influence of Duplex and Kennard-Stone method of splitting data into a training and test set were also investigated. Their performance were evaluated based on the percent correctly classified (%CC) of both training and test set. Standardization was used as data pre-processing technique. The generated classification models have shown that Kennard-Stone data splitting technique produced higher percent correctly classified of test set in all models. Meanwhile, LDA approach was found to be superior with lower risk of over-fitting for artemisinin data set.
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Over the past two decades there has been a growth in pattern recognition methods, especially cata... more Over the past two decades there has been a growth in pattern recognition methods, especially catalysed by machine learning community and the rapid growth in computing power. Methods developed two or three decades ago such as principal components analysis, cross-validation and partial least squares, required limited computing power and are now embedded into modern software packages. Moore's law as variously described suggests a doubling of computer speeds every 2 years, or over 30,000 times increase in 30 years, yet modern prepackaged chemometric software has not kept pace. Many problems are non-linear especially outside mainstream analytical chemistry and as such are require approaches often more usual in areas such as economics or biology. In addition proper validation and optimisation usually requires significant iterations, for example using a bootstrap and test / training set splits might require a model to be reformed 20,000 times. In addition, self organising maps are a powerful alternative to principal components for the visualisation of relationships between samples. These methods are illustrated on a dataset of ancient Italian pottery coming from different sites.
2014 IEEE 10th International Colloquium on Signal Processing and its Applications, 2014
Jurnal Teknologi, 2014
This paper shows the determination of iodine value (IV) of pure and frying palm oils using Partia... more This paper shows the determination of iodine value (IV) of pure and frying palm oils using Partial Least Squares (PLS) regression with application of variable selection. A total of 28 samples consisting of pure and frying palm oils which acquired from markets. Seven of them were considered as high-priced palm oils while the remaining was low-priced. PLS regression models were developed for the determination of IV using Fourier Transform Infrared (FTIR) spectra data in absorbance mode in the range from 650 cm-1 to 4000 cm-1. Savitzky Golay derivative was applied before developing the prediction models. The models were constructed using wavelength selected in the FTIR region by adopting selectivity ratio (SR) plot and correlation coefficient to the IV parameter. Each model was validated through Root Mean Square Error Cross Validation, RMSECV and cross validation correlation coefficient, R2cv. The best model using SR plot was the model with mean centring for pure sample and model with ...
This recent work describes the data pre-processing method of FT-NIR spectroscopy datasets of cook... more This recent work describes the data pre-processing method of FT-NIR spectroscopy datasets of cooking oil and its quality parameters with chemometrics method. Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modelling. Hence, this work is dedicated to investigate the utility and effectiveness of pre-processing algorithms namely row scaling, column scaling and single scaling process with Standard Normal Variate (SNV). The combinations of these scaling methods have impact on exploratory analysis and classification via Principle Component Analysis plot (PCA). The samples were divided into palm oil and non-palm cooking oil. The classification model was build using FT-NIR cooking oil spectra datasets in absorbance mode at the range of 4000cm−1-14000cm−1. Savitzky Golay derivative was applied before developing the classification model. Then, the data was separated into two sets which were training set and test set by using Duplex method. The number of each class was...
Procedia - Social and Behavioral Sciences, 2014
The purpose of this paper is to construct a new modified reporting index for shariah-compliant co... more The purpose of this paper is to construct a new modified reporting index for shariah-compliant companies. This new reporting index adheres to the Islamic Advisory Council's guidelines and is validated by the principal component analysis, which establishes a link between internal reporting of shariah-compliant companies and external reporting needs of the community and the public. Overall reporting index quality for shariah-compliant companies is found to be more of standard when variable selection method of t-statistic algorithm under the principal component analysis suggests that the strongest themes for the items of information in the index comprise two themes, which include Finance and Investment, and the Society.
Water Research, 2009
Faecal sterols detection is a promising method for identifying sources of faecal pollution. In th... more Faecal sterols detection is a promising method for identifying sources of faecal pollution. In this study, faecal contamination in water samples from point source (sewage treatment plants, chicken farms, quail farms and horse stables) was extracted using the solid phase extraction (SPE) technique. Faecal sterols (coprostanol, cholesterol, stigmasterol, b-sitosterol and stigmastanol) were selected as parameters to differentiate the source of faecal pollution. The results indicated that coprostanol, cholesterol and b-sitosterol were the most significant parameters that can be used as source tracers for faecal contamination. Chemometric techniques, such as cluster analysis, principal component analysis and discriminant analysis were applied to the data set on faecal contamination in water from various pollution sources in order to validate the faecal sterols' profiles. Cluster analysis generated three clusters: coprostanol was in cluster 1, cholesterol and b-sitosterol formed cluster 2, while cluster 3 contained stigmasterol and stigmastanol. Discriminant analysis suggested that coprostanol, cholesterol and b-sitosterol were the most significant parameters to discriminate between the faecal pollution source. The use of chemometric techniques provides useful and promising indicators in tracing the source of faecal contamination.
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Papers by DR. MOHD. ZULI JAAFAR