Water pollution has become a growing threat to human society and natural ecosystem in recent decades, increasing the need to better understand the variabilities of pollutants within aquatic systems. This study presents the application of two chemometric techniques, namely, cluster analysis (CA) and principal component analysis (PCA). This is to classify and identify the water quality variables into groups of similarities or dissimilarities and to determine their significance. Six stations along Kinta River, Perak, were monitored for 30 physical and chemical parameters during the period of 1997-2006. Using CA, the 30 physical and chemical parameters were classified into 4 clusters; PCA was applied to the datasets and resulted in 10 varifactors with a total variance of 78.06%. The varifactors obtained indicated the significance of each of the variables to the pollution of Kinta River.
Acetaminophen, an analgesic drug was evaluated as potential chemical marker for wastewater contamination. Water samples of various sources were analysed using online solid phase extraction liquid chromatography with diode array detector. Acetaminophen was detected in the range of 0.17-1.29 ng/mL in surface water samples contaminated with wastewater. Relatively high concentrations (16.7-74.61 ng/mL) of acetaminophen were observed in water samples from Universiti Teknologi MARA (UiTM) treatment plant monitored from March to August 2014. Positive correlation was obtained between the concentrations of acetaminophen with the students’ population based on UiTM academic calendar.
This study examined the presence and sources of 10 pharmaceuticals in Klang River were studied. The most common pharmaceuticals were caffeine and acetaminophen, 0.57-20.62 ng/mL and “not detected”-1.45 ng/mL. Water samples were clustered based on pharmaceutical concentrations. Source apportionment analysis showed that treated wastewater discharged from treatment plants contributed 18.43% of pharmaceuticals in Klang River. An environmental risk assessment by means of the risk quotient (RQ) was done whereby the latter was more than one for salicylic acid and diclofenac in surface water posing threats to the aquatic environment. Salicylic acid showed high risk for acute toxicity, while diclofenac showed high risk for chronic toxicity. The results indicated a need for regular monitoring on pharmaceutical levels in Klang River and increasing the efficiency of wastewater treatment here.
Headspace solid phase microextraction (HS-SPME) was used to isolate volatile compounds (VOCs) from mangoes (Harumanis cv.). Among the four SPME fibres investigated, the mixed phase coating, 65 μm polydimethyl siloxane–divinylbenzene (DVB/PDMS) showed the highest efficiency in extracting VOCs as 26 compounds were detected with the total area of 9.6 x 109. The optimization of SPME factors was conducted in 2 stages using multivariate design. The first stage involved screening of the significant factors using the Plackett–Burman (P–B) design, followed by the optimization of the significant factors utilizing Central Composite Design (CCD). The experimental design for both P-B and CCD design was generated using Design-Expert version 6.0.4 (Stat Ease Software). Extraction time and temperature appeared to be the most significant factors in extracting VOCs in mangoes, with the optimum conditions prevailing at 55°C and 34 minutes respectively.
Tongkat Ali (Eurycoma longifolia) is one of the most popular tropical herbal plants as it is believed to enhance virility and sexual prowess. This study looked examined chromatographic fingerprint of Tongkat Ali roots and its products generated using online solid phase-extraction liquid chromatography (SPE-LC) combined with chemometric approaches. The aim was to determine its quality. Pressurised liquid extraction (PLE) technique was used prior to online SPE-LC using polystyrene divinyl benzene (PSDVB) and C18 columns. Seventeen Tongkat Ali roots and 10 products (capsules) were analysed. Chromatographic dataset was subjected to chemometric techniques, namely cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) using 37 selected peaks. The samples were grouped into three clusters based on their quality. The PCA resulted in 11 latent factors describing 90.8% of the whole variance. Pattern matching analysis showed no significant difference (p>0.05) between the roots and products within the same CA grouping. The findings showed the combination of chromatographic fingerprint and chemometric techniques provided comprehensive evaluation for efficient quality control of Tongkat Ali formulation.
Pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) has been recognised as an effective technique to analyse car paint. This study was conducted to assess the combination of Py-GC-MS and chemometric techniques to classify car paint primer, the inner layer of car paint system. Fifty car paint primer samples from various manufacturers were analysed using Py-GC-MS, and data set of identified pyrolysis products was subjected to principal component analysis (PCA) and discriminant analysis (DA). The PCA rendered 16 principal components with 86.33% of the total variance. The DA was useful to classify the car paint primer samples according to their types (1k and 2k primer) with 100% correct classification in the test set for all three modes (standard, stepwise forward and stepwise backward). Three compounds, indolizine, 1,3-benzenedicarbonitrile and p-terphenyl, were the most significant compounds in discriminating the car paint primer samples.
Headspace solid phase microextraction (HS-SPME) was employed for the extraction of volatile organic compounds (VOCs) in MD2 pineapple (Ananas comosus L. var. comosus cv. MD2). Optimisation of HS-SPME operating parameters was conducted using three-factor, three-level Box–Behnken response surface experimental design to evaluate the interactive effects of temperature (30 – 50 ºC), extraction time (10 – 30 min) and salting effect (1 – 3 g of salt addition) on the amount of selected VOCs. Determination of VOCs was done using gas chromatography with spectrometry detector (GC-MSD). Extraction temperature was found to be significant (p < 0.05) in increasing the amount of selected VOCs (ethyl acetate, methyl isobutyrate and butanoic acid methyl ester). Based on the maximum amount of these VOCs, the optimum operating extraction conditions for HS-SPME were set up at temperature of 30 °C, time of 29 min and salt addition of 1 g. The optimized HS-SPME conditions were employed for the extraction of VOCs from pineapple of different varieties.