Drugs-facilitated crimes (DFCs) involve the incapacitation of victims under the influence of drugs. Conventionally, a drug administration act is often determined through the examination of biological samples; however, dry residues from any surface, such as drinking glass if related to a DFC could be a potential source of evidence. This study was aimed to establish an attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometrics for the determination of spiked sedative-hypnotics from dry residues of a drug-spiked beverage. In this study, four sedative-hypnotics, namely diazepam, ketamine, nimetazepam, and xylazine were examined using ATR-FTIR spectroscopy. Subsequently, the ATR-FTIR profiles were compared and decomposed by principal component analysis (PCA) followed by linear discriminant analysis (LDA) for their detection and discrimination. Visual comparison of ATR-FTIR profiles revealed distinct spectra among the tested drugs. An initial unsupervised exploratory PCA model indicated the separation of four main sedative-hypnotics clusters, and the proposed PCA score-LDA model had allowed for a 100% accurate classification. Discrimination of sedative-hypnotics from a dry beverage previously spiked with these drugs was also possible upon an additional extraction procedure. In conclusion, ATR-FTIR coupled with PCA score-LDA model was useful in detecting and discriminating sedative-hypnotics, including those that had been previously spiked into a beverage.
Falls are common and often lead to serious physical and psychological consequences for older persons. The occurrence of falls are usually attributed to the interaction between multiple risk factors. The clinical evaluation of falls risks is time-consuming as a result, hence limiting its availability. The purpose of this study was, therefore, to develop a clustering-based algorithm to determine falls risk. Data from the Malaysian Elders Longitudinal Research (MELoR), comprising 1411 subjects aged ≥55 years, were utilized. The proposed algorithm was developed through the stages of: data pre-processing, feature identification and extraction with either t-Distributed Stochastic Neighbour Embedding (t-SNE) or principal component analysis (PCA)), clustering (K-means clustering, Hierarchical clustering, and Fuzzy C-means clustering) and characteristics interpretation with statistical analysis. A total of 1279 subjects and 9 variables were selected for clustering after the data pre-possessing stage. Using feature extraction with the t-SNE and the K-means clustering algorithm, subjects were clustered into low, intermediate A, intermediate B and high fall risk groups which corresponded with fall occurrence of 13%, 19%, 21% and 31% respectively. Slower gait, poorer balance, weaker muscle strength, presence of cardiovascular disorder, poorer cognitive performance, and advancing age were the key variables identified. The proposed fall risk clustering algorithm grouped the subjects according to features. Such a tool could serve as a case identification or clinical decision support tool for clinical practice to enhance access to falls prevention efforts.
Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools. Image splicing is one of the most prevalent techniques. Digital images had lost their trustability, and researches have exerted considerable effort to regain such trustability by focusing mostly on algorithms. However, most of the proposed algorithms are incapable of handling high dimensionality and redundancy in the extracted features. Moreover, existing algorithms are limited by high computational time. This study focuses on improving one of the image splicing detection algorithms, that is, the run length run number algorithm (RLRN), by applying two dimension reduction methods, namely, principal component analysis (PCA) and kernel PCA. Support vector machine is used to distinguish between authentic and spliced images. Results show that kernel PCA is a nonlinear dimension reduction method that has the best effect on R, G, B, and Y channels and gray-scale images.
Matched MeSH terms: Principal Component Analysis/methods*
This paper describes an image analysis technique that objectively measures skin repigmentation for the assessment of therapeutic response in vitiligo treatments. Skin pigment disorders due to the abnormality of melanin production, such as vitiligo, cause irregular pale patches of skin. The therapeutic response to treatment is repigmentation of the skin. However the repigmentation process is very slow and is only observable after a few months of treatment. Currently, there is no objective method to assess the therapeutic response of skin pigment disorder treatment, particularly for vitiligo treatment. In this work, we apply principal component analysis followed by independent component analysis to represent digital skin images in terms of melanin and haemoglobin composition respectively. Vitiligo skin areas are identified as skin areas that lack melanin (non-melanin areas). Results obtained using the technique have been verified by dermatologists. Based on 20 patients, the proposed technique effectively monitored the progression of repigmentation over a shorter time period of six weeks and can thus be used to evaluate treatment efficacy objectively and more effectively.
Matched MeSH terms: Principal Component Analysis/methods*
Boosting accuracy in automatically discriminating patients with Alzheimer's disease (AD) and normal controls (NC), based on multidimensional classification of longitudinal whole brain atrophy rates and their intermediate counterparts in analyzing magnetic resonance images (MRI).
Matched MeSH terms: Principal Component Analysis/methods*
Food intake biomarkers (FIBs) can reflect the intake of specific foods or dietary patterns (DP). DP for successful aging (SA) has been widely studied. However, the relationship between SA and DP characterized by FIBs still needs further exploration as the candidate markers are scarce. Thus, 1H-nuclear magnetic resonance (1H-NMR)-based urine metabolomics profiling was conducted to identify potential metabolites which can act as specific markers representing DP for SA. Urine sample of nine subjects from each three aging groups, SA, usual aging (UA), and mild cognitive impairment (MCI), were analyzed using the 1H-NMR metabolomic approach. Principal components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were applied. The association between SA urinary metabolites and its DP was assessed using the Pearson's correlation analysis. The urine of SA subjects was characterized by the greater excretion of citrate, taurine, hypotaurine, serotonin, and melatonin as compared to UA and MCI. These urinary metabolites were associated with alteration in "taurine and hypotaurine metabolism" and "tryptophan metabolism" in SA elderly. Urinary serotonin (r = 0.48, p < 0.05) and melatonin (r = 0.47, p < 0.05) were associated with oat intake. These findings demonstrate that a metabolomic approach may be useful for correlating DP with SA urinary metabolites and for further understanding of SA development.
Matched MeSH terms: Principal Component Analysis/methods
The stability and high yielding of Vigna subterranea L. Verdc. genotype is an important factor for long-term development and food security. The effects of G × E interaction on yield stability in 30 Bambara groundnut genotypes in four different Malaysian environments were investigated in this research. The experiment used a randomized complete block design with three replications in each environment. Over multiple harvests, yield component traits such as the total number of pods per plant, fresh pods weight (g), hundred seeds weight (g), and yield per hectare were evaluated in the main and off-season in 2020 and 2021. Stability tests for multivariate stability parameters were performed based on analyses of variance. For all the traits, the pooled analysis of variance revealed highly significant (p
Matched MeSH terms: Principal Component Analysis/methods*
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.
The present study was undertaken to determine the content of six minerals, five trace elements, and ten pesticide residues in honeys originating from different regions of Malaysia. Calcium (Ca), magnesium (Mg), iron (Fe), and zinc (Zn) were analyzed by flame atomic absorption spectrometry (FAAS), while sodium (Na) and potassium (K) were analyzed by flame emission spectrometry (FAES). Trace elements such as arsenic (As), lead (Pb), cadmium (Cd), copper (Cu), and cobalt (Co) were analyzed by graphite furnace atomic absorption spectrometry (GFAAS) following the microwave digestion of honey. High mineral contents were observed in the investigated honeys with K, Na, Ca, and Fe being the most abundant elements (mean concentrations of 1349.34, 236.80, 183.67, and 162.31 mg/kg, resp.). The concentrations of the trace elements were within the recommended limits, indicating that the honeys were of good quality. Principal component analysis reveals good discrimination between the different honey samples. The pesticide analysis for the presence of organophosphorus and carbamates was performed by high performance liquid chromatography (HPLC). No pesticide residues were detected in any of the investigated honey samples, indicating that the honeys were pure. Our study reveals that Malaysian honeys are rich sources of minerals with trace elements present within permissible limits and that they are free from pesticide contamination.
Two subspecies of cynomolgus macaques (Macaca fascicularis) are alleged to co-exist in the Philippines, M. f. philippensis in the north and M. f. fascicularis in the south. However, genetic differences between the cynomolgus macaques in the two regions have never been studied to document the propriety of their subspecies status. We genotyped samples of cynomolgus macaques from Batangas in southwestern Luzon and Zamboanga in southwestern Mindanao for 15 short tandem repeat (STR) loci and sequenced an 835 bp fragment of the mtDNA of these animals. The STR genotypes were compared with those of cynomolgus macaques from southern Sumatra, Singapore, Mauritius and Cambodia, and the mtDNA sequences of both Philippine populations were compared with those of cynomolgus macaques from southern Sumatra, Indonesia and Sarawak, Malaysia. We conducted STRUCTURE and PCA analyses based on the STRs and constructed a median joining network based on the mtDNA sequences. The Philippine population from Batangas exhibited much less genetic diversity and greater genetic divergence from all other populations, including the Philippine population from Zamboanga. Sequences from both Batangas and Zamboanga were most closely related to two different mtDNA haplotypes from Sarawak from which they are apparently derived. Those from Zamboanga were more recently derived than those from Batangas, consistent with their later arrival in the Philippines. However, clustering analyses do not support a sufficient genetic distinction of cynomolgus macaques from Batangas from other regional populations assigned to subspecies M. f. fascicularis to warrant the subspecies distinction M. f. philippensis.
In recent years, groundwater quality has become a global concern due to its effect on human life and natural ecosystems. To assess the groundwater quality in the Amol-Babol Plain, a total of 308 water samples were collected during wet and dry seasons in 2009. The samples were analysed for their physico-chemical and biological constituents. Multivariate statistical analysis and geostatistical techniques were applied to assess the spatial and temporal variabilities of groundwater quality and to identify the main factors and sources of contamination. Principal component analysis (PCA) revealed that seven factors explained around 75% of the total variance, which highlighted salinity, hardness and biological pollution as the dominant factors affecting the groundwater quality in the Plain. Two-way analysis of variance (ANOVA) was conducted on the dataset to evaluate the spatio-temporal variation. The results showed that there were no significant temporal variations between the two seasons, which explained the similarity between six component factors in dry and wet seasons based on the PCA results. There are also significant spatial differences (p > 0.05) of the parameters under study, including salinity, potassium, sulphate and dissolved oxygen in the plain. The least significant difference (LSD) test revealed that groundwater salinity in the eastern region is significantly different to the central and western side of the study area. Finally, multivariate analysis and geostatistical techniques were combined as an effective method for demonstrating the spatial structure of multivariate spatial data. It was concluded that multiple natural processes and anthropogenic activities were the main sources of groundwater salinization, hardness and microbiological contamination of the study area.
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.
The study was aimed to differentiate between porcine and bovine gelatines in adulterated samples by utilising sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) combined with principal component analysis (PCA). The distinct polypeptide patterns of 6 porcine type A and 6 bovine type B gelatines at molecular weight ranged from 50 to 220 kDa were studied. Experimental samples of raw gelatine were prepared by adding porcine gelatine in a proportion ranging from 5% to 50% (v/v) to bovine gelatine and vice versa. The method used was able to detect 5% porcine gelatine added to the bovine gelatine. There were no differences in the electrophoretic profiles of the jelly samples when the proteins were extracted with an acetone precipitation method. The simple approach employing SDS-PAGE and PCA reported in this paper may provide a useful tool for food authenticity issues concerning gelatine.
A sum of 48 accessions of physic nut, Jatropha curcas L. were analyzed to determine the genetic diversity and association between geographical origin using RAPD-PCR markers. Eight primers generated a total of 92 fragments with an average of 11.5 amplicons per primer. Polymorphism percentages of J. curcas accessions for Selangor, Kelantan, and Terengganu states were 80.4, 50.0, and 58.7%, respectively, with an average of 63.04%. Jaccard's genetic similarity co-efficient indicated the high level of genetic variation among the accessions which ranged between 0.06 and 0.81. According to UPGMA dendrogram, 48 J. curcas accessions were grouped into four major clusters at coefficient level 0.3 and accessions from same and near states or regions were found to be grouped together according to their geographical origin. Coefficient of genetic differentiation (G(st)) value of J. curcas revealed that it is an outcrossing species.
The genus Curcuma is a member of the ginger family (Zingiberaceae) that has recently become popular for use as flowering pot plants, both indoors and as patio and landscape plants. We used PCR-based molecular markers (ISSRs) to assess genetic variation and relationships between five varieties of curcuma (Curcuma alismatifolia) cultivated in Malaysia. Sixteen ISSR primers generated 139 amplified fragments, of which 77% had high polymorphism among these varieties. These markers were used to estimate genetic similarity among the varieties using Jaccard's similarity coefficient. The similarity matrix was used to construct a dendrogram, and a principal component plot was developed to examine genetic relationships among varieties. Similarity coefficient values ranged from 0.40 to 0.58 (with a mean of 0.5) among the five varieties. The mean value of number of observed alleles, number of effective alleles, mean Nei's gene diversity, and Shannon's information index were 8.69, 1.48, 0.29, and 0.43, respectively.
The pollution status of the downstream section of the Jakara River was investigated. Dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), chemical oxygen demand (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in the form of ammonia (NH(3)), turbidity, dissolved solids (DS), total solids (TS), nitrates (NO(3)), chloride (Cl) and phosphates (PO(3-)(4)) were evaluated, using both dry and wet season samples, as a measure of variation in surface water quality in the area. The results obtained from the analyses were correlated using Pearson's correlation matrix, principal component analysis (PCA) and paired sample t-tests. Positive correlations were observed for BOD(5), NH(3), COD, and SS, turbidity, conductivity, salinity, DS, TS for dry and wet seasons, respectively. PCA was used to investigate the origin of each water quality parameter, and yielded 5 varimax factors for each of dry and wet seasons, with 70.7 % and 83.1 % total variance, respectively. A paired sample t-test confirmed that the surface water quality varies significantly between dry and wet season samples (P < 0.01). The source of pollution in the area was concluded to be of anthropogenic origin in the dry season and natural origins in the wet season.
The genotypes of black pepper are morphologically and genotypically highly diverse and carry all the cumulative variations inherited and maintained through generations. The present study describes the Simple Sequence Repeat (SSR) or microsatellite based assessment of genetic diversity among forty popular genotypes and four different species of black pepper in Southern region of India. For isolation of SSR primers, our earlier attempts with enrichment strategies like 'Triplex affinity capture' did not extract a single SSR primer due to close proximity of restriction sites to the SSR motif. Hence we developed a 'Sequential Reverse Genome Walking (SRGW)' strategy with better enrichment efficiency of 72% that generated seven new SSR primers. Genotyping precisely discriminated majority of genotypes which indicated that the SSR primers are very informative. A total of 62 alleles with an average of 15.5 alleles over 4 loci were identified. All the SSR primers showed an average Polymorphism Information Content (PIC) value of 0.85. The estimated average Shared Allele Frequency ranged between 1.57 and 20.12%. The PCA plot revealed four closely related individual groups and identified Karimunda, Wild pepper and a local landrace 'local b' as the most divergent genotypes. Cluster analysis exposed the genetic relatedness between hybrids and selections with other known cultivars. The introduction of black pepper from South India to Malaysia was emphasized from the observation of genetic similarity of Malaysian cultivar 'Kuching' with other indigenous popular cultivars. The study was first to portray the precise genetic relatedness among the major indigenous genotypes of black pepper.
The Langat River in Malaysia has been experiencing anthropogenic input from urban, rural and industrial activities for many years. Sewage contamination, possibly originating from the greater than three million inhabitants of the Langat River Basin, were examined. Sediment samples from 22 stations (SL01-SL22) along the Langat River were collected, extracted and analysed by GC-MS. Six different sterols were identified and quantified. The highest sterol concentration was found at station SL02 (618.29 ng/g dry weight), which situated in the Balak River whereas the other sediment samples ranged between 11.60 and 446.52 ng/g dry weight. Sterol ratios were used to identify sources, occurrence and partitioning of faecal matter in sediments and majority of the ratios clearly demonstrated that sewage contamination was occurring at most stations in the Langat River. A multivariate statistical analysis was used in conjunction with a combination of biomarkers to better understand the data that clearly separated the compounds. Most sediments of the Langat River were found to contain low to mid-range sewage contamination with some containing 'significant' levels of contamination. This is the first report on sewage pollution in the Langat River based on a combination of biomarker and multivariate statistical approaches that will establish a new standard for sewage detection using faecal sterols.
This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.
Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.