Displaying publications 1 - 20 of 107 in total

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  1. Abbas Alkarkhi FM, Ismail N, Easa AM
    J Hazard Mater, 2008 Feb 11;150(3):783-9.
    PMID: 17590506
    Cockles (Anadara granosa) sample obtained from two rivers in the Penang State of Malaysia were analyzed for the content of arsenic (As) and heavy metals (Cr, Cd, Zn, Cu, Pb, and Hg) using a graphite flame atomic absorption spectrometer (GF-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometer (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data. MANOVA showed a strong significant difference between the two rivers in term of As and heavy metals contents in cockles. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used only two parameters (Zn and Cd) affording more than 72% correct assignations. Results indicated that the two rivers were different in terms of As and heavy metal contents in cockle, and the major difference was due to the contribution of Zn and Cd. A positive correlation was found between discriminate functions (DF) and Zn, Cd and Cr, whereas negative correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metals and arsenic content. Taking into account of these results, it can be suggested that a continuous monitoring of As and heavy metals in cockles be performed in these two rivers.
    Matched MeSH terms: Discriminant Analysis
  2. Abbas, F.M.A., Saifullah, R., Azhar, M.E.
    MyJurnal
    Physical properties of ripe banana flour were studied in Cavendish and Dream banana, in order to distinguish the two varieties. Flour was analyzed for pH, total soluble solids (TSS), water holding capacity
    (WHC) and oil holding capacity (OHC) at 40, 60 and 80 °C, color values L*, a* and b*, back extrusion force
    and viscosity. Physical properties data were analyzed by cluster analysis (CA) and discriminant analysis (DA). CA showed that the two types of flour were different in terms of selected physical properties. DA indicated that WHC at 60 °C was the main contributor in discriminating the two types of flour.
    Matched MeSH terms: Discriminant Analysis
  3. Abbas, F.M.A., Foroogh, B., Liong, M.T., Azhar, M.E.
    MyJurnal
    Four types of soft dates (SD), three types of semi-dried dates (SDD) and one type of dried dates (DD) were used in this study. The antioxidant activities were assessed using TEAC method (ABTS assay) and the ferric reducing/antioxidant power method (FRAP assay), while total phenolic content (TPC) and total flavonoid content (TFC) were measured using Folin-Ciocalteau and aluminum chloride colorimetric methods. Multivariate analysis of variance (MANOVA), discriminant analysis (DA) and principal component analysis (PCA) were used to analyze the data. MANOVA showed a strong significant difference between the eight types of dates. DA identified the relative contribution of each parameter in distinguishing the dates. DA also identified two functions responsible for discriminating the dates and showed the difference between different types of dates. The first function distinguished DD from other types of dates, whilst the second function discriminated SD and SDD, affording 100% correct assignation. PCA identified only one component responsible for explaining 98.85% of the total variance in antioxidant data. It is suggested that the TEAC method and the quantitative determination of TPC and TFC was suitable for differentiation of dates and quality control.
    Matched MeSH terms: Discriminant Analysis
  4. Abdullah P, Abdullah SMS, Jaafar O, Mahmud M, Khalik WMAWM
    Mar Pollut Bull, 2015 Dec 15;101(1):378-385.
    PMID: 26476861 DOI: 10.1016/j.marpolbul.2015.10.014
    Characterization of hydrochemistry changes in Johor Straits within 5 years of monitoring works was successfully carried out. Water quality data sets (27 stations and 19 parameters) collected in this area were interpreted subject to multivariate statistical analysis. Cluster analysis grouped all the stations into four clusters ((Dlink/Dmax) × 100<90) and two clusters ((Dlink/Dmax) × 100<80) for site and period similarities. Principal component analysis rendered six significant components (eigenvalue>1) that explained 82.6% of the total variance of the data set. Classification matrix of discriminant analysis assigned 88.9-92.6% and 83.3-100% correctness in spatial and temporal variability, respectively. Times series analysis then confirmed that only four parameters were not significant over time change. Therefore, it is imperative that the environmental impact of reclamation and dredging works, municipal or industrial discharge, marine aquaculture and shipping activities in this area be effectively controlled and managed.
    Matched MeSH terms: Discriminant Analysis
  5. Acharya UR, Mookiah MR, Koh JE, Tan JH, Noronha K, Bhandary SV, et al.
    Comput Biol Med, 2016 06 01;73:131-40.
    PMID: 27107676 DOI: 10.1016/j.compbiomed.2016.04.009
    Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.
    Matched MeSH terms: Discriminant Analysis
  6. Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Bhandary SV, Rao AK, et al.
    Comput Biol Med, 2017 05 01;84:59-68.
    PMID: 28343061 DOI: 10.1016/j.compbiomed.2017.03.016
    The cause of diabetic macular edema (DME) is due to prolonged and uncontrolled diabetes mellitus (DM) which affects the vision of diabetic subjects. DME is graded based on the exudate location from the macula. It is clinically diagnosed using fundus images which is tedious and time-consuming. Regular eye screening and subsequent treatment may prevent the vision loss. Hence, in this work, a hybrid system based on Radon transform (RT), discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed for an automated detection of DME. The fundus images are subjected to RT to obtain sinograms and DWT is applied on these sinograms to extract wavelet coefficients (approximate, horizontal, vertical and diagonal). DCT is applied on approximate coefficients to obtain 2D-DCT coefficients. Further, these coefficients are converted into 1D vector by arranging the coefficients in zig-zag manner. This 1D signal is subjected to locality sensitive discriminant analysis (LSDA). Finally, various supervised classifiers are used to classify the three classes using significant features. Our proposed technique yielded a classification accuracy of 100% and 97.01% using two and seven significant features for private and public (MESSIDOR) databases respectively. Also, a maculopathy index is formulated with two significant parameters to discriminate the three groups distinctly using a single integer. Hence, our obtained results suggest that this system can be used as an eye screening tool for diabetic subjects for DME.
    Matched MeSH terms: Discriminant Analysis
  7. Acharya UR, Raghavendra U, Fujita H, Hagiwara Y, Koh JE, Jen Hong T, et al.
    Comput Biol Med, 2016 12 01;79:250-258.
    PMID: 27825038 DOI: 10.1016/j.compbiomed.2016.10.022
    Fatty liver disease (FLD) is reversible disease and can be treated, if it is identified at an early stage. However, if diagnosed at the later stage, it can progress to an advanced liver disease such as cirrhosis which may ultimately lead to death. Therefore, it is essential to detect it at an early stage before the disease progresses to an irreversible stage. Several non-invasive computer-aided techniques are proposed to assist in the early detection of FLD and cirrhosis using ultrasound images. In this work, we are proposing an algorithm to discriminate automatically the normal, FLD and cirrhosis ultrasound images using curvelet transform (CT) method. Higher order spectra (HOS) bispectrum, HOS phase, fuzzy, Kapoor, max, Renyi, Shannon, Vajda and Yager entropies are extracted from CT coefficients. These extracted features are subjected to locality sensitive discriminant analysis (LSDA) feature reduction method. Then these LSDA coefficients ranked based on F-value are fed to different classifiers to choose the best performing classifier using minimum number of features. Our proposed technique can characterize normal, FLD and cirrhosis using probabilistic neural network (PNN) classifier with an accuracy of 97.33%, specificity of 100.00% and sensitivity of 96.00% using only six features. In addition, these chosen features are used to develop a liver disease index (LDI) to differentiate the normal, FLD and cirrhosis classes using a single number. This can significantly help the radiologists to discriminate FLD and cirrhosis in their routine liver screening.
    Matched MeSH terms: Discriminant Analysis
  8. Agbolade O, Nazri A, Yaakob R, Ghani AA, Cheah YK
    BMC Bioinformatics, 2019 Dec 02;20(1):619.
    PMID: 31791234 DOI: 10.1186/s12859-019-3153-2
    BACKGROUND: Expression in H-sapiens plays a remarkable role when it comes to social communication. The identification of this expression by human beings is relatively easy and accurate. However, achieving the same result in 3D by machine remains a challenge in computer vision. This is due to the current challenges facing facial data acquisition in 3D; such as lack of homology and complex mathematical analysis for facial point digitization. This study proposes facial expression recognition in human with the application of Multi-points Warping for 3D facial landmark by building a template mesh as a reference object. This template mesh is thereby applied to each of the target mesh on Stirling/ESRC and Bosphorus datasets. The semi-landmarks are allowed to slide along tangents to the curves and surfaces until the bending energy between a template and a target form is minimal and localization error is assessed using Procrustes ANOVA. By using Principal Component Analysis (PCA) for feature selection, classification is done using Linear Discriminant Analysis (LDA).

    RESULT: The localization error is validated on the two datasets with superior performance over the state-of-the-art methods and variation in the expression is visualized using Principal Components (PCs). The deformations show various expression regions in the faces. The results indicate that Sad expression has the lowest recognition accuracy on both datasets. The classifier achieved a recognition accuracy of 99.58 and 99.32% on Stirling/ESRC and Bosphorus, respectively.

    CONCLUSION: The results demonstrate that the method is robust and in agreement with the state-of-the-art results.

    Matched MeSH terms: Discriminant Analysis
  9. Agbolade O, Nazri A, Yaakob R, Ghani AA, Cheah YK
    PLoS One, 2020;15(4):e0228402.
    PMID: 32271782 DOI: 10.1371/journal.pone.0228402
    BACKGROUND: The application of three-dimensional scan models offers a useful resource for studying craniofacial variation. The complex mathematical analysis for facial point acquisition in three-dimensional models has made many craniofacial assessments laborious.

    METHOD: This study investigates three-dimensional (3D) soft-tissue craniofacial variation, with relation to ethnicity, sex and age variables in British and Irish white Europeans. This utilizes a geometric morphometric approach on a subsampled dataset comprising 292 scans, taken from a Liverpool-York Head Model database. Shape variation and analysis of each variable are tested using 20 anchor anatomical landmarks and 480 sliding semi-landmarks.

    RESULTS: Significant ethnicity, sex, and age differences are observed for measurement covering major aspects of the craniofacial shape. The ethnicity shows subtle significant differences compared to sex and age; even though it presents the lowest classification accuracy. The magnitude of dimorphism in sex is revealed in the facial, nasal and crania measurement. Significant shape differences are also seen at each age group, with some distinct dimorphic features present in the age groups.

    CONCLUSIONS: The patterns of shape variation show that white British individuals have a more rounded head shape, whereas white Irish individuals have a narrower head shape. White British persons also demonstrate higher classification accuracy. Regarding sex patterns, males are relatively larger than females, especially in the mouth and nasal regions. Females presented with higher classification accuracy than males. The differences in the chin, mouth, nose, crania, and forehead emerge from different growth rates between the groups. Classification accuracy is best for children and senior adult age groups.

    Matched MeSH terms: Discriminant Analysis
  10. Ahmad R, Lim CK, Marzuki NF, Goh YK, Azizan KA, Goh YK, et al.
    Molecules, 2020 Dec 16;25(24).
    PMID: 33339375 DOI: 10.3390/molecules25245965
    In solving the issue of basal stem rot diseases caused by Ganoderma, an investigation of Scytalidium parasiticum as a biological control agent that suppresses Ganoderma infection has gained our interest, as it is more environmentally friendly. Recently, the fungal co-cultivation has emerged as a promising method to discover novel antimicrobial metabolites. In this study, an established technique of co-culturing Scytalidium parasiticum and Ganoderma boninense was applied to produce and induce metabolites that have antifungal activity against G. boninense. The crude extract from the co-culture media was applied to a High Performance Liquid Chromatography (HPLC) preparative column to isolate the bioactive compounds, which were tested against G. boninense. The fractions that showed inhibition against G. boninense were sent for a Liquid Chromatography-Time of Flight-Mass Spectrometry (LC-TOF-MS) analysis to further identify the compounds that were responsible for the microbicidal activity. Interestingly, we found that eudistomin I, naringenin 7-O-beta-D-glucoside and penipanoid A, which were present in different abundances in all the active fractions, except in the control, could be the antimicrobial metabolites. In addition, the abundance of fatty acids, such as oleic acid and stearamide in the active fraction, also enhanced the antimicrobial activity. This comprehensive metabolomics study could be used as the basis for isolating biocontrol compounds to be applied in oil palm fields to combat a Ganoderma infection.
    Matched MeSH terms: Discriminant Analysis
  11. Ahmad SJ, Mohamad Zin N, Mazlan NW, Baharum SN, Baba MS, Lau YL
    PeerJ, 2021;9:e10816.
    PMID: 33777509 DOI: 10.7717/peerj.10816
    Background: Antiplasmodial drug discovery is significant especially from natural sources such as plant bacteria. This research aimed to determine antiplasmodial metabolites of Streptomyces spp. against Plasmodium falciparum 3D7 by using a metabolomics approach.

    Methods: Streptomyces strains' growth curves, namely SUK 12 and SUK 48, were measured and P. falciparum 3D7 IC50 values were calculated. Metabolomics analysis was conducted on both strains' mid-exponential and stationary phase extracts.

    Results: The most successful antiplasmodial activity of SUK 12 and SUK 48 extracts shown to be at the stationary phase with IC50 values of 0.8168 ng/mL and 0.1963 ng/mL, respectively. In contrast, the IC50 value of chloroquine diphosphate (CQ) for antiplasmodial activity was 0.2812 ng/mL. The univariate analysis revealed that 854 metabolites and 14, 44 and three metabolites showed significant differences in terms of strain, fermentation phase, and their interactions. Orthogonal partial least square-discriminant analysis and S-loading plot putatively identified pavettine, aurantioclavine, and 4-butyldiphenylmethane as significant outliers from the stationary phase of SUK 48. For potential isolation, metabolomics approach may be used as a preliminary approach to rapidly track and identify the presence of antimalarial metabolites before any isolation and purification can be done.

    Matched MeSH terms: Discriminant Analysis
  12. Akbar R, Jusoh SA, Amaro RE, Helms V
    Chem Biol Drug Des, 2017 May;89(5):762-771.
    PMID: 27995760 DOI: 10.1111/cbdd.12900
    Finding pharmaceutically relevant target conformations from an arbitrary set of protein conformations remains a challenge in structure-based virtual screening (SBVS). The growth in the number of available conformations, either experimentally determined or computationally derived, obscures the situation further. While the inflated conformation space potentially contains viable druggable targets, the increase of conformational complexity, as a consequence, poses a selection problem. To address this challenge, we took advantage of machine learning methods, namely an over-sampling and a binary classification procedure, and present a novel method to select druggable receptor conformations. Specifically, we trained a binary classifier on a set of nuclear receptor conformations, wherein each conformation was labeled with an enrichment measure for a corresponding SBVS. The classifier enabled us to formulate suggestions and identify enriching SBVS targets for six of seven nuclear receptors. Further, the classifier can be extended to other proteins of interest simply by feeding new training data sets to the classifier. Our work, thus, provides a methodology to identify pharmaceutically interesting receptor conformations for nuclear receptors and other drug targets.
    Matched MeSH terms: Discriminant Analysis
  13. Al-Quraishi MS, Ishak AJ, Ahmad SA, Hasan MK, Al-Qurishi M, Ghapanchizadeh H, et al.
    Med Biol Eng Comput, 2017 May;55(5):747-758.
    PMID: 27484411 DOI: 10.1007/s11517-016-1551-4
    Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.
    Matched MeSH terms: Discriminant Analysis
  14. Alias A, Ibrahim A, Abu Bakar SN, Swarhib Shafie M, Das S, Abdullah N, et al.
    Clin Ter, 2018 11 6;169(5):e217-e223.
    PMID: 30393808 DOI: 10.7417/CT.2018.2082
    INTRODUCTION: The first step in the forensic identification is sex determination followed by age and stature estimation, as both are sex-dependent. The mandible is the largest, strongest and most durable bone in the face. Mandible is important for sex confirmation in absence of a complete pelvis and skull.

    AIM: The aim of the present study was to determine sex of human mandible from morphology, morphometric measurements as well as discriminant function analysis from the CT scan.

    MATERIALS AND METHODS: The present retrospective study comprised 79 subjects (48 males, 31 females), with age group between 18 and 74 years, and were obtained from the post mortem computed tomography data in the Hospital Kuala Lumpur. The parameters were divided into three morphologic and nine morphometric parameters, which were measured by using Osirix MD Software 3D Volume Rendering.

    RESULTS: The Chi-square test showed that men were significantly association with square-shaped chin (92%), prominent muscle marking (85%) and everted gonial glare, whereas women had pointed chin (84%), less prominent muscle marking (90%) and inverted gonial glare (80%). All parameter measurements showed significantly greater values in males than in females by independent t-test (p< 0.01). By discriminant analysis, the classification accuracy was 78.5%, the sensitivity was 79.2% and the specificity was 77.4%. The discriminant function equation was formulated based on bigonial breath and condylar height, which were the best predictors.

    CONCLUSION: In conclusion, the mandible could be distinguished according to the sex. The results of the study can be used for identification of damaged and/or unknown mandible in the Malaysian population.

    Matched MeSH terms: Discriminant Analysis
  15. Alkarkhi AFM, Alqaraghuli WAA, Mohamed Zam NR, Manan DMA, Mahmud MN, Huda N
    Data Brief, 2020 Jun;30:105414.
    PMID: 32258278 DOI: 10.1016/j.dib.2020.105414
    Data on the mineral composition and content of one heavy metal measured in three different fruit flours prepared from ripe and unripe fruits (pulp and peel) are presented. The mineral composition (sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), zinc (Zn), copper (Cu), iron (Fe) and manganese (Mn)) and content of one heavy metal (lead (Pb)) of the flours were analyzed by atomic absorption spectrophotometry. The analysis showed that the data can be used for differentiation between different fruits and stages of ripeness, as revealed by discriminant analysis and cluster analysis. The data provided can be used by researchers and scientists in the differentiation of fruits based on major and minor mineral elements.
    Matched MeSH terms: Discriminant Analysis
  16. Amin AM, Mostafa H, Arif NH, Abdul Kader MAS, Kah Hay Y
    Clin Chim Acta, 2019 Jun;493:112-122.
    PMID: 30826371 DOI: 10.1016/j.cca.2019.02.030
    BACKGROUND: Coronary artery disease (CAD) claims lives yearly. Nuclear magnetic resonance (1H NMR) metabolomics analysis is efficient in identifying metabolic biomarkers which lend credence to diagnosis. We aimed to identify CAD metabotypes and its implicated pathways using 1H NMR analysis.

    METHODS: We analysed plasma and urine samples of 50 stable CAD patients and 50 healthy controls using 1H NMR. Orthogonal partial least square discriminant analysis (OPLS-DA) followed by multivariate logistic regression (MVLR) models were developed to indicate the discriminating metabotypes. Metabolic pathway analysis was performed to identify the implicated pathways.

    RESULTS: Both plasma and urine OPLS-DA models had specificity, sensitivity and accuracy of 100%, 96% and 98%, respectively. Plasma MVLR model had specificity, sensitivity, accuracy and AUROC of 92%, 86%, 89% and 0.96, respectively. The MVLR model of urine had specificity, sensitivity, accuracy and AUROC of 90%, 80%, 85% and 0.92, respectively. 35 and 12 metabolites were identified in plasma and urine metabotypes, respectively. Metabolic pathway analysis revealed that urea cycle, aminoacyl-tRNA biosynthesis and synthesis and degradation of ketone bodies pathways were significantly disturbed in plasma, while methylhistidine metabolism and galactose metabolism pathways were significantly disturbed in urine. The enrichment over representation analysis against SNPs-associated-metabolite sets library revealed that 85 SNPs were significantly enriched in plasma metabotype.

    CONCLUSIONS: Cardiometabolic diseases, dysbiotic gut-microbiota and genetic variabilities are largely implicated in the pathogenesis of CAD.

    Matched MeSH terms: Discriminant Analysis
  17. 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
  18. Aminu M, Ahmad NA
    ACS Omega, 2020 Oct 20;5(41):26601-26610.
    PMID: 33110988 DOI: 10.1021/acsomega.0c03362
    Partial least squares discriminant analysis (PLS-DA) is a well-known technique for feature extraction and discriminant analysis in chemometrics. Despite its popularity, it has been observed that PLS-DA does not automatically lead to extraction of relevant features. Feature learning and extraction depends on how well the discriminant subspace is captured. In this paper, discriminant subspace learning of chemical data is discussed from the perspective of PLS-DA and a recent extension of PLS-DA, which is known as the locality preserving partial least squares discriminant analysis (LPPLS-DA). The objective is twofold: (a) to introduce the LPPLS-DA algorithm to the chemometrics community and (b) to demonstrate the superior discrimination capabilities of LPPLS-DA and how it can be a powerful alternative to PLS-DA. Four chemical data sets are used: three spectroscopic data sets and one that contains compositional data. Comparative performances are measured based on discrimination and classification of these data sets. To compare the classification performances, the data samples are projected onto the PLS-DA and LPPLS-DA subspaces, and classification of the projected samples into one of the different groups (classes) is done using the nearest-neighbor classifier. We also compare the two techniques in data visualization (discrimination) task. The ability of LPPLS-DA to group samples from the same class while at the same time maximizing the between-class separation is clearly shown in our results. In comparison with PLS-DA, separation of data in the projected LPPLS-DA subspace is more well defined.
    Matched MeSH terms: Discriminant Analysis
  19. Ang KH
    Sains Malaysiana, 2018;47:471-479.
    In recent years, Malaysia has experienced quite a few number of chronic air pollution problems and it has become a
    major contributor to the deterioration of human health and ecosystems. This study aimed to assess the air quality data
    and identify the pattern of air pollution sources using chemometric analysis through hierarchical cluster analysis (HCA),
    discriminant analysis (DA), principal component analysis (PCA) and multiple linear regression analysis (MLR). The air
    quality data from January 2016 until December 2016 was obtained from the Department of Environment Malaysia. Air
    quality data from eight sampling stations in Selangor include the selected variables of nitrogen dioxide (NO2
    ), ozone (O3
    ),
    sulfur dioxide (SO2
    ), carbon monoxide (CO) and particulate matter (PM10). The HCA resulted in three clusters, namely low
    pollution source (LPS), moderate pollution source (MPS) and slightly high pollution source (SHPS). Meanwhile, DA resulted
    in two and four variables for the forward stepwise mode and the backward stepwise mode, respectively. Through PCA,
    it was identified that the main pollutants of LPS, MPS and SHPS came from industrial and vehicle emissions, agricultural
    systems, residential factors and natural emission sources. Among the three models yielded from the MLR analysis, it was
    found that SHPS is the most suitable model to be used for the prediction of Air Pollution Index. This study concluded that
    a clearer review and practical design of air quality monitoring network would be beneficial for better management of
    air pollution. The study also suggested that chemometric techniques have the ability to show significant information on
    spatial variability for large and complex air quality data.
    Matched MeSH terms: Discriminant Analysis
  20. Azizan KA, Baharum SN, Mohd Noor N
    Molecules, 2012 Jul 03;17(7):8022-36.
    PMID: 22759915 DOI: 10.3390/molecules17078022
    Gas chromatography mass spectrometry (GC-MS) and headspace gas chromatography mass spectrometry (HS/GC-MS) were used to study metabolites produced by Lactococcus lactis subsp. cremoris MG1363 grown at a temperature of 30 °C with and without agitation at 150 rpm, and at 37 °C without agitation. It was observed that L. lactis produced more organic acids under agitation. Primary alcohols, aldehydes, ketones and polyols were identified as the corresponding trimethylsilyl (TMS) derivatives, whereas amino acids and organic acids, including fatty acids, were detected through methyl chloroformate derivatization. HS analysis indicated that branched-chain methyl aldehydes, including 2-methylbutanal, 3-methylbutanal, and 2-methylpropanal are degdradation products of isoleucine, leucine or valine. Multivariate analysis (MVA) using partial least squares discriminant analysis (PLS-DA) revealed the major differences between treatments were due to changes of amino acids and fermentation products.
    Matched MeSH terms: Discriminant Analysis
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