BACKGROUND/OBJECTIVES: Little is known about the dietary patterns (DPs) of women during pregnancy. The present study aimed to identify the DPs of pregnant Malaysian women and their associations with socio-demographic, obstetric, and anthropometric characteristics.
SUBJECTS AND METHODS: This prospective cohort study included 737 participants enrolled in Seremban Cohort Study between 2013 and 2015. Food consumption was assessed using a validated 126-food item semi-quantitative food frequency questionnaire (SFFQ) at four time-points, namely, pre-pregnancy and at each trimester (first, second, and third). Principal component analysis (PCA) was used to identify DPs.
RESULTS: Three DPs were identified at each time point and designated DP 1-3 (pre-pregnancy), DP 4-6 (first trimester), DP 7-9 (second trimester) and DP 10-12 (third trimester). DP 1, 4, and 7 appeared to be more prudent diets, characterized by higher intakes of nuts, seeds & legumes, green leafy vegetables, other vegetables, eggs, fruits, and milk & dairy products. DP 2, 5, 8, and 11 had greater loadings of condiments & spices, sugar, spreads & creamer, though DP 2 had additional sweet foods, DP 5 and 8 had additional oils & fats, and DP 11 had additional tea & coffee, respectively. DP 3 and 6 were characterized by high protein (poultry, meat, processed, dairy, eggs, and fish), sugars (mainly as beverages and sweet foods), and energy (bread, cereal & cereal products, rice, noodles & pasta) intakes. DP 9 had additional fruits. However, DP 12 had greater loadings of energy foods (bread, cereal & cereal products, rice, noodles & pasta), sugars (mainly as beverages, and sweet foods), and good protein sources (eggs, nuts, seeds & legumes). Malays were more likely to have lower adherence (LA) for DP 1 and 10 than non-Malays. DP 2, 8, and 11 were more prevalent among Malays than non-Malays. Women with a higher education were more likely to have LA for DP 10, and women with a greater waist circumference at first prenatal visit were more likely to show LA for DP 11.
CONCLUSIONS: DPs observed in the present study were substantially different from those reported in Western populations. Information concerning associations between ethnicity, waist circumference and education with specific DPs before and throughout pregnancy could facilitate efforts to promote healthy dietary behavior and the overall health and well-being of pregnant women.
Study name: Seremban Cohort Study (SECOST)
The influx of medicines from different sources into healthcare systems of developing countries presents a challenge to monitor their origin and quality. The absence of a repository of reference samples or spectra prevents the analysis of tablets by direct comparison. A set of paracetamol tablets purchased in Malaysian pharmacies were compared to a similar set of sample purchased in the UK using near-infrared spectroscopy (NIRS). Additional samples of products containing ibuprofen or paracetamol in combination with other actives were added to the study as negative controls. NIR spectra of the samples were acquired and compared by using multivariate modeling and classification algorithms (PCA/SIMCA) and stored in a spectral database. All analysed paracetamol samples contained the purported active ingredient with only 1 out of 20 batches excluded from the 95% confidence interval, while the negative controls were clearly classified as outliers of the set. Although the substandard products were not detected in the purchased sample set, our results indicated variability in the quality of the Malaysian tablets. A database of spectra was created and search methods were evaluated for correct identification of tablets. The approach presented here can be further developed as a method for identifying substandard pharmaceutical products.
Ancestry-informative markers (AIMs) can be used to infer the ancestry of an individual to minimize the inaccuracy of self-reported ethnicity in biomedical research. In this study, we describe three methods for selecting AIM SNPs for the Malay population (Malay AIM panel) using different approaches based on pairwise FST, informativeness for assignment (In), and PCA-correlated SNPs (PCAIMs). These Malay AIM panels were extracted from genotype data stored in SNP arrays hosted by the Malaysian node of the Human Variome Project (MyHVP) and the Singapore Genome Variation Project (SGVP). In particular, genotype data from a total of 165 Malay individuals were analyzed, comprising data on 117 individual genotypes from the Affymetrix SNP-6 SNP array platform and data on 48 individual genotypes from the OMNI 2.5 Illumina SNP array platform. The HapMap phase 3 database (1397 individuals from 11 populations) was used as a reference for comparison with the Malay genotype data. The accuracy of each resulting Malay AIM panel was evaluated using a machine learning "ancestry-predictive model" constructed by using WEKA, a comprehensive machine learning platform written in Java. A total of 1250 SNPs were finally selected, which successfully identified Malay individuals from other world populations with an accuracy of 90%, but the accuracy decreased to 80% using 157 SNPs according to the pairwise FST method, while a panel of 200 SNPs selected using In and PCAIMs could be used to identify Malay individuals with an accuracy of approximately 80%.
Malay, the main ethnic group in Peninsular Malaysia, is represented by various sub-ethnic groups such as Melayu Banjar, Melayu Bugis, Melayu Champa, Melayu Java, Melayu Kedah Melayu Kelantan, Melayu Minang and Melayu Patani. Using data retrieved from the MyHVP (Malaysian Human Variome Project) database, a total of 135 individuals from these sub-ethnic groups were profiled using the Affymetrix GeneChip Mapping Xba 50-K single nucleotide polymorphism (SNP) array to identify SNPs that were ancestry-informative markers (AIMs) for Malays of Peninsular Malaysia. Prior to selecting the AIMs, the genetic structure of Malays was explored with reference to 11 other populations obtained from the Pan-Asian SNP Consortium database using principal component analysis (PCA) and ADMIXTURE. Iterative pruning principal component analysis (ipPCA) was further used to identify sub-groups of Malays. Subsequently, we constructed an AIMs panel for Malays using the informativeness for assignment (In) of genetic markers, and the K-nearest neighbor classifier (KNN) was used to teach the classification models. A model of 250 SNPs ranked by In, correctly classified Malay individuals with an accuracy of up to 90%. The identified panel of SNPs could be utilized as a panel of AIMs to ascertain the specific ancestry of Malays, which may be useful in disease association studies, biomedical research or forensic investigation purposes.
Nitrate-nitrogen leaching from agricultural areas is a major cause for groundwater pollution. Polluted groundwater with high levels of nitrate is hazardous and cause adverse health effects. Human consumption of water with elevated levels of NO3-N has been linked to the infant disorder methemoglobinemia and also to non-Hodgkin's disease lymphoma in adults. This research aims to study the temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy soil at Ladang Merdeka Ismail Mulong in Kelantan, Malaysia. The complex data matrix (128 x 16) of nitrate-nitrogen parameters was subjected to multivariate analysis mainly Principal Component Analysis (PCA) and Discriminant Analysis (DA). PCA extracted four principal components from this data set which explained 86.4% of the total variance. The most important contributors were soil physical properties confirmed using Alyuda Forecaster software (R2 = 0.98). Discriminant analysis was used to evaluate the temporal variation in soil nitrate-nitrogen on leaching process. Discriminant analysis gave four parameters (hydraulic head, evapotranspiration, rainfall and temperature) contributing more than 98% correct assignments in temporal analysis. DA allowed reduction in dimensionality of the large data set which defines the four operating parameters most efficient and economical to be monitored for temporal variations. This knowledge is important so as to protect the precious groundwater from contamination with nitrate.
This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.
The issue of food authenticity has become a concern among religious adherents, particularly Muslims, due to the possible presence of nonhalal ingredients in foods as well as other commercial products. One of the nonhalal ingredients that commonly found in food and pharmaceutical products is gelatin which extracted from porcine source. Bovine and fish gelatin are also becoming the main commercial sources of gelatin. However, unclear information and labeling regarding the actual sources of gelatin in food and pharmaceutical products have become the main concern in halal authenticity issue since porcine consumption is prohibited for Muslims. Hence, numerous analytical methods involving chemical and chemometric analysis have been developed to identify the sources of gelatin. Chemical analysis techniques such as biochemical, chromatography, electrophoretic, and spectroscopic are usually combined with chemometric and mathematical methods such as principal component analysis, cluster, discriminant, and Fourier transform analysis for the gelatin classification. A sample result from Fourier transform infrared spectroscopy analysis, which combines Fourier transform and spectroscopic technique, is included in this paper. This paper presents an overview of chemical and chemometric methods involved in identification of different types of gelatin, which is important for halal authentication purposes.
BACKGROUND: Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input system.
OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.
RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.
CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.
The KIR system shows variation at both gene content and allelic level across individual genome and populations. This variation reflects its role in immunity and has become a significant tool for population comparisons. In this study, we investigate KIR gene content in 120 unrelated individuals from the four Malay subethnic groups (Kelantan, Jawa, Banjar and Pattani Malays). Genotyping using commercial polymerase chain reaction-sequence-specific primer (PCR-SSP) kits revealed a total of 34 different KIR genotypes; 17 for Kelantan, 15 for Banjar, 14 for Jawa and 13 for Pattani Malays. Two new variants observed in Banjar Malays have not previously been reported. Genotype AA and haplotype A were the most common in Jawa (0.47 and 0.65, respectively), Banjar (0.37 and 0.52, respectively) and Pattani (0.40 and 0.60, respectively) Malays. In contrast, Kelantan Malays were observed to have slightly higher frequency (0.43) of genotype BB as compared with the others. Based on the KIR genes distribution, Jawa, Pattani and Banjar subethnic groups showed greater similarity and are discrete from Kelantan Malays. A principal component plot carried out using KIR gene carrier frequency shows that the four Malay subethnic groups are clustered together with other South-East Asian populations. Overall, our observation on prevalence of KIR gene content demonstrates genetic affinities between the four Malay subethnic groups and supports the common origins of the Austronesian-speaking people.
Combined Support Vector Machine (SVM) and Principal Component Analysis (PCA) was used to recognize the infant cries with asphyxia. SVM classifier based on features selected by the PCA was trained to differentiate between pathological and healthy cries. The PCA was applied to reduce dimensionality of the vectors that serve as inputs to the SVM. The performance of the SVM utilizing linear and RBF kernel was examined. Experimental results showed that SVM with RBF kernel yields good performance. The classification accuracy in classifying infant cry with asphyxia using the SVM-PCA is 95.86%.
Matched MeSH terms: Principal Component Analysis/methods*
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.
The characteristics of urban stormwater pollution in the tropics are still poorly understood. This issue is crucial to the tropical environment because its rainfall and runoff generation processes are so different from temperate regions. In this regard, a stormwater monitoring program was carried out at three urban catchments (e.g. residential, commercial and industrial) in the southern part of Peninsular Malaysia. A total of 51 storm events were collected at these three catchments. Samples were analyzed for total suspended solids, 5-day biochemical oxygen demand, chemical oxygen demand (COD), oil and grease, nitrate nitrogen, nitrite nitrogen, ammonia nitrogen (NH3-N), soluble reactive phosphorus and total phosphorus. Principal component analysis (PCA) and hierarchical cluster analysis were used to interpret the stormwater quality data for pattern recognition and identification of possible sources. The most likely sources of stormwater pollutants at the residential catchment were from surface soil and leachate of fertilizer from domestic lawns and gardens, whereas the most likely sources for the commercial catchment were from discharges of food waste and washing detergent. In the industrial catchment, the major sources of pollutants were discharges from workshops and factories. The PCA factors further revealed that COD and NH3-N were the major pollutants influencing the runoff quality in all three catchments.
This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.
Freeze-thaw cycles (FTC) pretreatment was employed before the vacuum freeze-drying of garlic slices, aimed at improving the drying process and the quality of the end product. Cell viability, water status, internal structure, flavor, chemical composition and thermogravimetric of garlic samples were evaluated. The results indicated that FTC pretreatment reduced the drying time (22.22%-33.33%) and the energy consumption (14.25%-15.50%), owing to the water loss, the increase in free water, and the formation of porous structures. The FTC pretreatment improved thermal stability, flavor and chemical composition content of dried products. The antioxidant activity of polysaccharides extracted from FTC pretreated dried products was higher than that of the unpretreated dried product due to the reduction in polysaccharide molecular weight. This research could pave a route for future production of dried garlic slices having good quality by using the FTC pretreatment, with lower energy consumption and shorter drying time.
Actinobacteria from underexplored and unusual environments have gained significant attention for their capability in producing novel bioactive molecules of diverse chemical entities. Streptomyces is the most prolific Actinobacteria in producing useful molecules. Rapid decline effectiveness of existing antibiotics in the treatment of infections are caused by the emergence of multidrug-resistant pathogens. Intensive efforts are urgently required in isolating non-Streptomyces or rare Actinobacteria and understanding of their distribution in the harsh environment for new drug discovery. In this study, pretreatment of soil samples with 1.5% phenol was used for the selective isolation of Actinobacteria from Dee Island and Greenwich Island. A high number of non-Streptomyces (69.4%) or rare Actinobacteria was significantly recovered despite the Streptomyces (30.6%), including the genera Micromonospora, Micrococcus, Kocuria, Dermacoccus, Brachybacterium, Brevibacterium, Rhodococcus, Microbacterium and Rothia. Reduced diversity and shift of distribution were observed at the elevated level of soil pH. The members of genera Streptomyces, Micromonospora and Micrococcus were found to distribute and tolerate to a relatively high pH level of soil (pH 9.4-9.5), and could potentially be alkaliphilic Actinobacteria. The phylogenetic analysis had revealed some potentially new taxa members of the genera Micromonospora, Micrococcus and Rhodococcus. Principal Component Analysis of soil samples was used to uncover the factors that underlie the diversity of culturable Actinobacteria. Water availability in soil was examined as the principal factor that shaped the diversity of the Actinobacteria, by providing a dynamic source for microbial interactions and elevated diversity of Actinobacteria.
The geographical variation of the gray-bellied squirrel (Callosciurus caniceps) was examined using osteometry of skull in Southeast Asia. From the principal component analysis (PCA), the plots of the northern localities from Nan to Kanchanaburi and those of the southern localities from Narathiwat to Kuala Lumpur in male were completely separated. In female, the plots of the locality from Uttradit to Kanchanaburi and those of the locality from Pattani to Negri Sembilan were completely separated. We called these northern localities and southern localities which are distinguished by the PCA as N group and S group. The size and shape of the skulls of these squirrels indicated the differences between N group and S group from t-test and U-test. These results may be influenced by the two transitions of the phytogeography around the southernmost locality in N group and the northernmost locality in S group in the peninsular Thailand and Malay Peninsula. Localities which are located between N and S groups were called the Middle (M) group. From the PCA among N, S groups and each locality of M group, the plots of localities such as Prachuap Khiri Khan, Chumphon, Krabi, Nakhon Si Thammarat and Trang in both sexes of M group could not be separated from those of N and S groups. We suggest that the sympatric distribution of N and S groups and the hybrid of N and S populations may be seen in these localities of M group.
Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.
Statistical validation is crucial for the clustering of unknown samples. This study aims to demonstrate how statistical techniques can be optimized using simulated heroin samples containing a range of analyte concentrations that are similar to those of the case samples. Eight simulated heroin distribution links consisting of 64 postcut samples were prepared by mixing one of two mixtures of paracetamol-caffeine-dextromethorphan at different proportions with eight precut samples. Analyte contents and compositional variation of the prepared samples were investigated. A number of data pretreatments were evaluated by associating the postcut samples with the corresponding precut samples using principal component analysis and discriminant analysis. Subsequently, combinations of seven linkage methods and five distance measures were explored using hierarchical cluster analysis. In this study, Ward-Manhattan showed better distinctions between unrelated links and was able to cluster all related samples in very close distance under the known links on a dendogram. A similar discriminative outcome was also achieved by 90 unknown case samples when clustered via Ward-Manhattan.
Sixteen trace elements found in 309 street heroin samples, piped water and contaminated water were determined using inductively coupled plasma-mass spectrometry. All the street heroin samples were found to contain high levels of sodium, a reflection of the use of sodium bicarbonate during heroin synthesis. Additionally, this element was also found to be one of the potential contaminants acquired from the piped water. Calcium could be derived from lime while iron, aluminum and zinc could have come from the metallic container used in the processing/cutting stage. The levels of these elements remained low in the heroin and it could be due to the dilution effects from the addition of adulterants. Statistical validation was performed with six links of related heroin samples using principal component analysis to find the best pretreatment for sample classification. It was obtained that normalization followed by fourth root showed promising results with 8% errors in the sample clustering. The technique was then applied to the case samples. Finally, the result suggested that the case samples could have originated from at least two major groups respectively showing unique elemental profiles at the street level.
Statistical classification remains the most useful statistical tool for forensic chemists to assess the relationships between samples. Many clustering techniques such as principal component analysis and hierarchical cluster analysis have been employed to analyze chemical data for pattern recognition. Due to the feeble foundation of this statistics knowledge among novice drug chemists, a tetrahedron method was designed to simulate how advanced chemometrics operates. In this paper, the development of the graphical tetrahedron and computational matrices derived from the possible tetrahedrons are discussed. The tetrahedron method was applied to four selected parameters obtained from nine illicit heroin samples. Pattern analysis and mathematical computation of the differences in areas for assessing the dissimilarity between the nine tetrahedrons were found to be user-convenient and straightforward for novice cluster analysts.