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The effect of nitrogen supplementation on the efficiency of colour and COD removal by Malaysian white-rot fungi in textile dyeing effluent

Lee KK, Kassim AM, Lee HK

Water Sci Technol, 2004;50(5):73-7.
PMID: 15497832

White-rot fungi, namely Coriolus versicolor and Schizophyllum commune, were studied for the biodecolorization of textile dyeing effluent in shaker-flask experiments. The results showed that C. versicolor was able to achieve 68% color removal after 5 days of treatment while that of S. commune was 88% in 9 days. Both fungi achieved the above results in non-sterile condition with diammonium hydrogen phosphate as the nutrient supplement. On the other hand, the best COD removal of 80% was obtained with C. versicolor in 9 days in sterile effluent with yeast extract as nutrient supplement, while S. commune was able to remove 85% COD within 8 days in non-sterile textile effluent supplemented with diammonium hydrogen phosphate.
Fulltext Concerns of Thalassemia Patients, Carriers, and their Caregivers in Malaysia: Text Mining Information Shared on Social Media

Phang YC, Kassim AM, Mangantig E

Healthc Inform Res, 2021 Jul;27(3):200-213.
PMID: 34384202 DOI: 10.4258/hir.2021.27.3.200

Objective: The main aim of this study was to use text mining on social media to analyze information and gain insight into the health-related concerns of thalassemia patients, thalassemia carriers, and their caregivers.
Methods: Posts from two Facebook groups whose members consisted of thalassemia patients, thalassemia carriers, and caregivers in Malaysia were extracted using the Data Miner tool. In this study, a new framework known as Malay-English social media text pre-processing was proposed for performing the steps of pre-processing the noisy mixed language (Malay-English language) of social media posts. Topic modeling was used to identify hidden topics within posts shared among members. Three different topic models-latent Dirichlet allocation (LDA) in GenSim, LDA in MALLET, and latent semantic analysis-were applied to the dataset with and without stemming using Python.
RESULTS: LDA in MALLET without stemming was found to be the best topic model for this dataset. Eight topics were identified within the posts shared by members. Of those eight topics, four were newly discovered by this study, and four others corresponded to the findings of previous studies that used an interview approach.
Conclusions: Topic 2 (the challenges faced by thalassemia patients) was found to be the topic with the highest attention and engagement. Healthcare practitioners and other concerned parties should make an effort to build a stronger support system related to this issue for those affected by thalassemia.
Support vector machines for oil classification link with polyaromatic hydrocarbon contamination in the environment

Ismail A, Juahir H, Mohamed SB, Toriman ME, Kassim AM, Zain SM, et al.

Water Sci Technol, 2021 Mar;83(5):1039-1054.
PMID: 33724935 DOI: 10.2166/wst.2021.038

The main focus of this study is exploring the spatial distribution of polyaromatics hydrocarbon links between oil spills in the environment via Support Vector Machines based on Kernel-Radial Basis Function (RBF) approach for high precision classification of oil spill type from its sample fingerprinting in Peninsular Malaysia. The results show the highest concentrations of Σ Alkylated PAHs and Σ EPA PAHs in ΣTAH concentration in diesel from the oil samples PP3_liquid and GP6_Jetty achieving 100% classification output, corresponding to coherent decision boundary and projective subspace estimation. The high dimensional nature of this approach has led to the existence of a perfect separability of the oil type classification from four clustered oil type components; i.e diesel, bunker C, Mixture Oil (MO), lube oil and Waste Oil (WO) with the slack variables of ξ ≠ 0. Of the four clusters, only the SVs of two are correctly predicted, namely diesel and MO. The kernel-RBF approach provides efficient and reliable oil sample classification, enabling the oil classification to be optimally performed within a relatively short period of execution and a faster dataset classification where the slack variables ξ are non-zero.
Chemometric techniques in oil classification from oil spill fingerprinting

Ismail A, Toriman ME, Juahir H, Kassim AM, Zain SM, Ahmad WKW, et al.

Mar Pollut Bull, 2016 Oct 15;111(1-2):339-346.
PMID: 27397593 DOI: 10.1016/j.marpolbul.2016.06.089

Extended use of GC-FID and GC-MS in oil spill fingerprinting and matching is significantly important for oil classification from the oil spill sources collected from various areas of Peninsular Malaysia and Sabah (East Malaysia). Oil spill fingerprinting from GC-FID and GC-MS coupled with chemometric techniques (discriminant analysis and principal component analysis) is used as a diagnostic tool to classify the types of oil polluting the water. Clustering and discrimination of oil spill compounds in the water from the actual site of oil spill events are divided into four groups viz. diesel, Heavy Fuel Oil (HFO), Mixture Oil containing Light Fuel Oil (MOLFO) and Waste Oil (WO) according to the similarity of their intrinsic chemical properties. Principal component analysis (PCA) demonstrates that diesel, HFO, MOLFO and WO are types of oil or oil products from complex oil mixtures with a total variance of 85.34% and are identified with various anthropogenic activities related to either intentional releasing of oil or accidental discharge of oil into the environment. Our results show that the use of chemometric techniques is significant in providing independent validation for classifying the types of spilled oil in the investigation of oil spill pollution in Malaysia. This, in consequence would result in cost and time saving in identification of the oil spill sources.
Improving oil classification quality from oil spill fingerprint beyond six sigma approach

Juahir H, Ismail A, Mohamed SB, Toriman ME, Kassim AM, Zain SM, et al.

Mar Pollut Bull, 2017 Jul 15;120(1-2):322-332.
PMID: 28535957 DOI: 10.1016/j.marpolbul.2017.04.032

This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC-MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving Fstat>Fcritical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited.