Displaying publications 1 - 20 of 107 in total

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  1. Hussain H, Yusoff MK, Ramli MF, Abd Latif P, Juahir H, Zawawi MA
    Pak J Biol Sci, 2013 Nov 15;16(22):1524-30.
    PMID: 24511695
    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.
    Matched MeSH terms: Discriminant Analysis
  2. Subari N, Mohamad Saleh J, Md Shakaff AY, Zakaria A
    Sensors (Basel), 2012;12(10):14022-40.
    PMID: 23202033 DOI: 10.3390/s121014022
    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.
    Matched MeSH terms: Discriminant Analysis
  3. Hossain MA, Ali ME, Abd Hamid SB, Asing, Mustafa S, Mohd Desa MN, et al.
    J Agric Food Chem, 2016 Aug 17;64(32):6343-54.
    PMID: 27501408 DOI: 10.1021/acs.jafc.6b02224
    Beef, buffalo, and pork adulteration in the food chain is an emerging and sensitive issue. Current molecular techniques to authenticate these species depend on polymerase chain reaction (PCR) assays involving long and single targets which break down under natural decomposition and/or processing treatments. This novel multiplex polymerase chain reaction-restriction fragment length polymorphism assay targeted two different gene sites for each of the bovine, buffalo, and porcine materials. This authentication ensured better security, first through a complementation approach because it is highly unlikely that both sites will be missing under compromised states, and second through molecular fingerprints. Mitochondrial cytochrome b and ND5 genes were targeted, and all targets (73, 90, 106, 120, 138, and 146 bp) were stable under extreme boiling and autoclaving treatments. Target specificity and authenticity were ensured through cross-amplification reaction and restriction digestion of PCR products with AluI, EciI, FatI, and CviKI-1 enzymes. A survey of Malaysian frankfurter products revealed rampant substitution of beef with buffalo but purity in porcine materials.
    Matched MeSH terms: Discriminant Analysis
  4. Murat M, Chang SW, Abu A, Yap HJ, Yong KT
    PeerJ, 2017;5:e3792.
    PMID: 28924506 DOI: 10.7717/peerj.3792
    Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.
    Matched MeSH terms: Discriminant Analysis
  5. Yusuf N, Zakaria A, Omar MI, Shakaff AY, Masnan MJ, Kamarudin LM, et al.
    BMC Bioinformatics, 2015;16:158.
    PMID: 25971258 DOI: 10.1186/s12859-015-0601-5
    Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.
    Matched MeSH terms: Discriminant Analysis
  6. Jingying C, Baocai L, Ying C, Wujun Z, Yunqing Z, Yingzhen H, et al.
    PMID: 37625275 DOI: 10.1016/j.saa.2023.123229
    Dioscorea oppositifolia is an important crop and functional food. D. oppositifolia tuber is often adulterated with D. persimilis, D. alata, and D. fordii tuber in the commercial market. This study proposed an integrated Fourier transform infrared spectroscopy (FT-IR) with chemometric approach to differentiate these four Dioscorea species. A total of 107 Dioscorea spp. tuber samples were collected from different locations in China. Principal Component Analysis (PCA), PCA-Class, and Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) were utilised to classify the FT-IR spectra. In this PCA is unable to differentiate the Dioscorea spp. tuber effectively. However, PCA-Class and OPLS-DA can distinguish spp. these 4 species Dioscorea tuber with high accuracy, sensitivity, and specificity. Additionally, the RMSEE, RMSEP and RMSECV values for OPLS-DA model were low, showing that it is a good model. The combination of FT-IR with the PCA-Class and OPLS-DA is practical in discriminating Dioscorea spp. tubers.
    Matched MeSH terms: Discriminant Analysis
  7. Chan KW, Tan GH, Wong RC
    J Forensic Sci, 2013 Jan;58 Suppl 1:S199-207.
    PMID: 23013257 DOI: 10.1111/j.1556-4029.2012.02285.x
    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.
    Matched MeSH terms: Discriminant Analysis
  8. Khamis MF, Taylor JA, Malik SN, Townsend GC
    Forensic Sci Int, 2014 Jan;234:183.e1-7.
    PMID: 24128748 DOI: 10.1016/j.forsciint.2013.09.019
    Information about the sex of individuals is important for human identification. This study was conducted to quantify classification rates of sex prediction models for Malaysians using odontometric profiles. Mesiodistal (MD) and buccolingual (BL) crown dimensions of the permanent dentition were studied in 400 young adult Malaysians, giving a total of 28 tooth size variables. The sample consisted of three major ethnic groups, the Malays, Chinese and Tamils, since the aim was to assess sex dimorphism in Malaysians as a whole. Results showed that the mesiodistal diameter of the lower canine was the most sexually dimorphic dimension in Malaysian Malays and Tamils. Univariate analyses showed that the magnitude and pattern of sex dimorphism varies between these three ethnic groups, with Malaysian Chinese and Tamils being more dimorphic than the Malaysian Malays. Stepwise discriminant functions were generated bearing in mind their application in practical forensic situations. The range of classification rates was from 70.2% to 78.5% for the composite Malaysian group, and 83.8%, 77.9%, 72.4% for Malaysian Chinese, Malays and Tamils, respectively. The 'Area Under the Receiver Operating Characteristic Curve statistics' indicated good classification rates for three prediction models obtained using a combination of all tooth size variables, mandibular teeth, and mesiodistal dimensions in the composite Malaysian group, and for all tooth size variables in each ethnic group. The present study provides strong support for the value of odontometry as an adjunct scientific method for sex prediction in human identification.
    Matched MeSH terms: Discriminant Analysis
  9. Tan M, Mariapun S, Yip CH, Ng KH, Teo SH
    Phys Med Biol, 2019 01 31;64(3):035016.
    PMID: 30577031 DOI: 10.1088/1361-6560/aafabd
    Historically, breast cancer risk prediction models are based on mammographic density measures, which are dichotomous in nature and generally categorize each voxel or area of the breast parenchyma as 'dense' or 'not dense'. Using these conventional methods, the structural patterns or textural components of the breast tissue elements are not considered or ignored entirely. This study presents a novel method to predict breast cancer risk that combines new texture and mammographic density based image features. We performed a comprehensive study of the correlation of 944 new and conventional texture and mammographic density features with breast cancer risk on a cohort of Asian women. We studied 250 breast cancer cases and 250 controls matched at full-field digital mammography (FFDM) status for age, BMI and ethnicity. Stepwise regression analysis identified relevant features to be included in a linear discriminant analysis (LDA) classifier model, trained and tested using a leave-one-out based cross-validation method. The area under the receiver operating characteristic (AUC) and adjusted odds ratios (ORs) were used as the two performance assessment indices in our study. For the LDA trained classifier, the adjusted OR was 6.15 (95% confidence interval: 3.55-10.64) and for Volpara volumetric breast density, 1.10 (0.67-1.81). The AUC for the LDA trained classifier was 0.68 (0.64-0.73), compared to 0.52 (0.47-0.57) for Volpara volumetric breast density (p   
    Matched MeSH terms: Discriminant Analysis
  10. Goh CH, Ng SC, Kamaruzzaman SB, Chin AV, Poi PJ, Chee KH, et al.
    Medicine (Baltimore), 2016 May;95(19):e3614.
    PMID: 27175670 DOI: 10.1097/MD.0000000000003614
    To evaluate the utility of blood pressure variability (BPV) calculated using previously published and newly introduced indices using the variables falls and age as comparators.While postural hypotension has long been considered a risk factor for falls, there is currently no documented evidence on the relationship between BPV and falls.A case-controlled study involving 25 fallers and 25 nonfallers was conducted. Systolic (SBPV) and diastolic blood pressure variability (DBPV) were assessed using 5 indices: standard deviation (SD), standard deviation of most stable continuous 120 beats (staSD), average real variability (ARV), root mean square of real variability (RMSRV), and standard deviation of real variability (SDRV). Continuous beat-to-beat blood pressure was recorded during 10 minutes' supine rest and 3 minutes' standing.Standing SBPV was significantly higher than supine SBPV using 4 indices in both groups. The standing-to-supine-BPV ratio (SSR) was then computed for each subject (staSD, ARV, RMSRV, and SDRV). Standing-to-supine ratio for SBPV was significantly higher among fallers compared to nonfallers using RMSRV and SDRV (P = 0.034 and P = 0.025). Using linear discriminant analysis (LDA), 3 indices (ARV, RMSRV, and SDRV) of SSR SBPV provided accuracies of 61.6%, 61.2%, and 60.0% for the prediction of falls which is comparable with timed-up and go (TUG), 64.4%.This study suggests that SSR SBPV using RMSRV and SDRV is a potential predictor for falls among older patients, and deserves further evaluation in larger prospective studies.
    Matched MeSH terms: Discriminant Analysis
  11. Dominic JA, Aris AZ, Sulaiman WN, Tahir WZ
    Environ Monit Assess, 2016 Mar;188(3):191.
    PMID: 26914327 DOI: 10.1007/s10661-016-5192-8
    The approach of this paper is to predict the sand mass distribution in an urban stormwater holding pond at the Stormwater Management And Road Tunnel (SMART) Control Centre, Malaysia, using simulated depth average floodwater velocity diverted into the holding during storm events. Discriminant analysis (DA) was applied to derive the classification function to spatially distinguish areas of relatively high and low sand mass compositions based on the simulated water velocity variations at corresponding locations of gravimetrically measured sand mass composition of surface sediment samples. Three inflow parameter values, 16, 40 and 80 m(3) s(-1), representing diverted floodwater discharge for three storm event conditions were fixed as input parameters of the hydrodynamic model. The sand (grain size > 0.063 mm) mass composition of the surface sediment measured at 29 sampling locations ranges from 3.7 to 45.5%. The sampling locations of the surface sediment were spatially clustered into two groups based on the sand mass composition. The sand mass composition of group 1 is relatively lower (3.69 to 12.20%) compared to group 2 (16.90 to 45.55%). Two Fisher's linear discriminant functions, F 1 and F 2, were generated to predict areas; both consist of relatively higher and lower sand mass compositions based on the relationship between the simulated flow velocity and the measured surface sand composition at corresponding sampling locations. F 1 = -9.405 + 4232.119 × A - 1795.805 × B + 281.224 × C, and F 2 = -2.842 + 2725.137 × A - 1307.688 × B + 231.353 × C. A, B and C represent the simulated flow velocity generated by inflow parameter values of 16, 40 and 80 m(3) s(-1), respectively. The model correctly predicts 88.9 and 100.0% of sampling locations consisting of relatively high and low sand mass percentages, respectively, with the cross-validated classification showing that, overall, 82.8% are correctly classified. The model predicts that 31.4% of the model domain areas consist of high-sand mass composition areas and the remaining 68.6% comprise low-sand mass composition areas.
    Matched MeSH terms: Discriminant Analysis*
  12. Aminan AW, Kit LLW, Hui CH, Sulaiman B
    Trop Life Sci Res, 2020 Jul;31(2):33-49.
    PMID: 32922668 DOI: 10.21315/tlsr2020.31.2.3
    The genus Rasbora is one of the most species-rich genus among the freshwater fishes and cryptic diversity has been a major hindrance in species identification in the past four decades due to their high similarities in terms of morphology. This study aimed to investigate this issue both morphologically and molecularly. In this study, a total of 23 morphometric parameters were used to differentiate the 103 Rasbora fish samples harvested from different regions of Sarawak state of Malaysia via Multivariate Stepwise Discriminant Function Analysis (SDFA). Then, cytochrome oxidase subunit I (COI) gene was utilised to further distinguish 33 of these fishes, followed by sequence and phylogenetic analysis. Our results unravelled pre-anal length as strongest morphometric discriminant (100%) and that all eight Rasbora species tested are monophyletic except for R. sumatrana and R. caudimaculata, revealing possible cryptic Rasbora species. Further investigations are vital to enrich the data from this study for Rasbora cryptic diversity and conservation studies in future.
    Matched MeSH terms: Discriminant Analysis
  13. Endo H, Fukuta K, Kimura J, Sasaki M, Stafford BJ
    J Vet Med Sci, 2004 Oct;66(10):1229-35.
    PMID: 15528854
    We examined the geographical variation of the skull size and shape of the lesser mouse deer (Tragulus javanicus) from Laos, Thailand, Peninsular Malaysia, Sumatra, Java, Borneo, Langkawi and some Islands of Tenasserim in Myanmar. Although the influence of the climatic condition on skull size was not confirmed in the mainland populations, the skull became rostro-caudally longer in the populations of Tenasserim and Sumatra because of island isolation effect. The skull size was classified into the following three clusters of localities from the matrix of Q-mode correlation coefficients: 1) Langkawi and Tenasserim, 2) Laos and Thailand, 3) Sumatra and Borneo. The skulls in the population of Java belong to the cluster of Langkawi and Tenasserim in male, however were morphologically similar to those in the cluster of Borneo and Sumatra. The canonical discriminant analysis pointed out that the Laos and Tenasserim populations were separated from the other ones and that the populations of Sumatra, Java and Borneo were intermingled each other.
    Matched MeSH terms: Discriminant Analysis
  14. Hariharan M, Polat K, Sindhu R
    Comput Methods Programs Biomed, 2014 Mar;113(3):904-13.
    PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004
    Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
    Matched MeSH terms: Discriminant Analysis
  15. Dhiman Gain, Mahfuj M, Islam S, Minar M, Goutham-Bharathi M, Simon Kumar Das
    Sains Malaysiana, 2017;46:695-702.
    Wild stocks of endangered mrigal carp, Cirrhinus cirrhosus (Bloch 1795), continues to decline rapidly in the Indo-Ganges river basin. With an objective to evaluate its population status, landmark-based morphometric and meristic variations among three different stocks viz., hatchery (Jessore), baor (Gopalganj) and river (Faridpur) in Bangladesh were studied. Significant differences were observed in 10 of the 15 morphometric measurements viz., head length, standard length, fork length, length of base of spinous, pre-orbital length, eye length, post-orbital length, length of upper jaw, height of pelvic fin and barbel length, two of the 8 meristic counts viz., scales above the lateral line and pectoral fin rays and 10 of the 22 truss network measurements viz., 1 to 10, 2 to 3, 2 to 8, 2 to 9, 2 to 10, 3 to 4, 3 to 8, 4 to 5, 4 to 7 and 9 to 10 among the stocks. For morphometric and landmark measurements, the 1st discriminant function (DF) accounted for 58.1% and the 2nd DF accounted for 41.9% of the among-group variability. In discriminant space, the river stock was isolated from the other two stocks. On the other hand, baor and hatchery stocks formed a very compact cluster. A dendrogram based on the hierarchical cluster analysis using morphometric and truss distance data placed the hatchery and baor in one cluster and the river in another cluster and the distance between the river and hatchery populations was the highest. Morphological differences among stocks are expected, because of their geographical isolation and their origin from different ancestors. The baseline information derived from the present study would be useful for genetic studies and in the assessment of environmental impacts on C. cirrhosus populations in Bangladesh.
    Matched MeSH terms: Discriminant Analysis
  16. Sweeti, Joshi D, Panigrahi BK, Anand S, Santhosh J
    J Healthc Eng, 2018;2018:9213707.
    PMID: 29808111 DOI: 10.1155/2018/9213707
    This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.
    Matched MeSH terms: Discriminant Analysis
  17. Contreras-Jodar A, Nayan NH, Hamzaoui S, Caja G, Salama AAK
    PLoS One, 2019;14(2):e0202457.
    PMID: 30735497 DOI: 10.1371/journal.pone.0202457
    The aim of the study is to identify the candidate biomarkers of heat stress (HS) in the urine of lactating dairy goats through the application of proton Nuclear Magnetic Resonance (1H NMR)-based metabolomic analysis. Dairy does (n = 16) in mid-lactation were submitted to thermal neutral (TN; indoors; 15 to 20°C; 40 to 45% humidity) or HS (climatic chamber; 37°C day, 30°C night; 40% humidity) conditions according to a crossover design (2 periods of 21 days). Thermophysiological traits and lactational performances were recorded and milk composition analyzed during each period. Urine samples were collected at day 15 of each period for 1H NMR spectroscopy analysis. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) assessment with cross validation were used to identify the goat urinary metabolome from the Human Metabolome Data Base. HS increased rectal temperature (1.2°C), respiratory rate (3.5-fold) and water intake (74%), but decreased feed intake (35%) and body weight (5%) of the lactating does. No differences were detected in milk yield, but HS decreased the milk contents of fat (9%), protein (16%) and lactose (5%). Metabolomics allowed separating TN and HS urinary clusters by PLS-DA. Most discriminating metabolites were hippurate and other phenylalanine (Phe) derivative compounds, which increased in HS vs. TN does. The greater excretion of these gut-derived toxic compounds indicated that HS induced a harmful gastrointestinal microbiota overgrowth, which should have sequestered aromatic amino acids for their metabolism and decreased the synthesis of neurotransmitters and thyroid hormones, with a negative impact on milk yield and composition. In conclusion, HS markedly changed the thermophysiological traits and lactational performances of dairy goats, which were translated into their urinary metabolomic profile through the presence of gut-derived toxic compounds. Hippurate and other Phe-derivative compounds are suggested as urinary biomarkers to detect heat-stressed dairy animals in practice.
    Matched MeSH terms: Discriminant Analysis
  18. Darvish Ghanbar K, Yousefi Rezaii T, Farzamnia A, Saad I
    PLoS One, 2021;16(3):e0248511.
    PMID: 33788862 DOI: 10.1371/journal.pone.0248511
    Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.
    Matched MeSH terms: Discriminant Analysis
  19. Yusof NA, Isha A, Ismail IS, Khatib A, Shaari K, Abas F, et al.
    J Sci Food Agric, 2015 Sep;95(12):2533-43.
    PMID: 25371390 DOI: 10.1002/jsfa.6987
    The metabolite changes in three germplasm accessions of Malaysia Andrographis paniculata (Burm. F.) Nees, viz. 11265 (H), 11341 (P) and 11248 (T), due to their different harvesting ages and times were successfully evaluated by attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy and translated through multivariate data analysis of principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). This present study revealed the feasibility of ATR-FTIR in detecting the trend changes of the major metabolites - andrographolide and neoandrographolide - functional groups in A. paniculata leaves of different accessions. The harvesting parameter was set at three different ages of 120, 150 and 180 days after transplanting (DAT) and at two different time sessions of morning (7:30-10:30 am) and evening (2:30-5.30 pm).
    Matched MeSH terms: Discriminant Analysis
  20. Nor Nasriah Zaini, Mardiana Saaid, Hafizan Juahir, Rozita Osman
    MyJurnal
    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.
    Matched MeSH terms: Discriminant Analysis
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