Displaying publications 21 - 40 of 104 in total

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  1. Braley C, Hondrogiannis EM
    J Forensic Sci, 2020 Mar;65(2):428-437.
    PMID: 31560807 DOI: 10.1111/1556-4029.14201
    Kratom is a plant material exhibiting both analgesic and stimulant effects and is also forensically relevant since it is abused as a "legal high." It is regulated in several countries but not scheduled in the United States at the federal level. This study used inductively coupled plasma-mass spectrometry (ICP-MS) to measure the concentrations of 13 elements in 19 kratom samples obtained from an online distributor selling kratom, from Borneo, Malaysia, Indonesia, Thailand, and Vietnam, for the purpose of using the elements to discriminate among purported country of origin, "suborigin," and strain. Analysis of variance revealed statistical differences in concentrations of elements from each group, while discriminant function analysis (using leave-one-out classification) successfully classified kratom samples by their purported country of origin (100%), "suborigin," (100%), and strain (86%). Our method illustrates the possibility of utilizing ICP-MS for determination of commercially available kratom samples by purported origin, "subororign," or by product line.
    Matched MeSH terms: Discriminant Analysis
  2. Farah Izza Jais, Sharifah Mastura, Naji Arafat Mahat, Dzulkiflee Ismail, Muhammad Naeim Mohamad Asri
    MyJurnal
    Introduction: Accelerants and fabrics are commonly used to spread fire attributable to their highly flammable prop- erties. Hence, having the ability to discriminate the different types of accelerants on various types of fabrics after fire and/or arson using the non-destructive Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spec- troscopy coupled with chemometric techniques appears forensically relevant. Methods: Six types of fabrics viz. cotton, wool, silk, rayon, satin, and polyester, were burnt completely with RON95 and RON97 gasoline as well as diesel. Subsequently, the samples were analyzed by ATR-FTIR spectroscopy followed by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for discriminating the different types of accelerants on such burned fabrics. Results: RON95 showed the fastest rate of burning on all fabric types. Results also revealed that while wool had the slowest burning rate for all the three different accelerants, polyester, cotton, and satin demon- strated the highest rate of burning in RON95, RON97, and diesel, respectively. FTIR spectra revealed the presence of alkane, alcohol, alkene, alkyne, aromatic, and amine compounds for all fabrics. The two dimensional PCA (PC1 versus PC2) demonstrated 71% of variance and it was improved by cross-validation through the three dimensional LDA technique with correct classification of 77.8%. Conclusion: ATR-FTIR spectroscopy coupled with chemometric techniques had enabled identification of the functional groups, as well as statistically supported discrimination of the different accelerants, a matter of relevance in forensic fire and arson investigations.
    Matched MeSH terms: Discriminant Analysis
  3. 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
  4. 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
  5. Shamsudin S, Selamat J, Sanny M, A R SB, Jambari NN, Khatib A
    Molecules, 2019 Oct 29;24(21).
    PMID: 31671885 DOI: 10.3390/molecules24213898
    Stingless bee honey produced by Heterotrigona itama from different botanical origins was characterised and discriminated. Three types of stingless bee honey collected from acacia, gelam, and starfruit nectars were analyzed and compared with Apis mellifera honey. The results showed that stingless bee honey samples from the three different botanical origins were significantly different in terms of their moisture content, pH, free acidity, total soluble solids, colour characteristics, sugar content, amino acid content and antioxidant properties. Stingless bee honey was significantly different from Apis mellifera honey in terms of physicochemical and antioxidant properties. The amino acid content was further used in the chemometrics analysis to evaluate the role of amino acid in discriminating honey according to botanical origin. Partial least squares-discriminant analysis (PLS-DA) revealed that the stingless bee honey was completely distinguishable from Apis mellifera honey. Notably, a clear distinction between the stingless bee honey types was also observed. The specific amino acids involved in the distinction of honey were cysteine for acacia and gelam, phenylalanine and 3-hydroxyproline for starfruit, and proline for Apis mellifera honey. The results showed that all honey samples were successfully classified based on amino acid content.
    Matched MeSH terms: Discriminant Analysis
  6. 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
  7. Lee LC, Jemain AA
    Analyst, 2019 Apr 08;144(8):2670-2678.
    PMID: 30849143 DOI: 10.1039/c8an02074d
    In response to our review paper [L. C. Lee et al., Analyst, 2018, 143, 3526-3539], we present a study that compares empirical differences between PLS1-DA and PLS2-DA algorithms in modelling a colossal ATR-FTIR spectral dataset. Over the past two decades, partial least squares-discriminant analysis (PLS-DA) has gained wide acceptance and huge popularity in the field of applied research, partly due to its dimensionality reduction capability and ability to handle multicollinear and correlated variables. To solve a K-class problem (K > 2) using PLS-DA and high-dimensional data like infrared spectra, one can construct either K one-versus-all PLS1-DA models or only one PLS2-DA model. The aim of this work is to explore empirical differences between the two PLS-DA algorithms in modeling a colossal ATR-FTIR spectral dataset. The practical task is to build a prediction model using the imbalanced, high dimensional, colossal and multi-class ATR-FTIR spectra of blue gel pen inks. Four different sub-datasets were prepared from the principal dataset by considering the raw and asymmetric least squares (AsLS) preprocessed forms: (a) Raw-global region; (b) Raw-local region; (c) AsLS-global region; and (d) AsLS-local region. A series of 50 models which includes the first 50 PLS components incrementally was constructed repeatedly using the four sub-datasets. Each model was evaluated using six different variants of v-fold cross validation, autoprediction and external testing methods. As a result, each PLS-DA algorithm was represented by a number of figures of merit. The differences between PLS1-DA and PLS2-DA algorithms were assessed using hypothesis tests with respect to model accuracy, stability and fitting. On the other hand, confusion matrices of the two PLS-DA algorithms were inspected carefully for assessment of model parsimony. Overall, both the algorithms presented satisfactory model accuracy and stability. Nonetheless, PLS1-DA models showed significantly higher accuracy rates than PLS2-DA models, whereas PLS2-DA models seem to be much more stable compared to PLS1-DA models. Eventually, PLS2-DA also proved to be less prone to overfitting and is more parsimonious than PLS1-DA. In conclusion, the relatively high accuracy of the PLS1-DA algorithm is achieved at the cost of rather low parsimony and stability, and with an increased risk of overfitting.
    Matched MeSH terms: Discriminant Analysis
  8. Samsudin MS, Azid A, Khalit SI, Sani MSA, Lananan F
    Mar Pollut Bull, 2019 Apr;141:472-481.
    PMID: 30955758 DOI: 10.1016/j.marpolbul.2019.02.045
    The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011-2015 data employed in this study entailed 13 parameters from six monitoring stations in West Malaysia. Spatial discriminant analysis (SDA) had recommended seven significant parameters to develop the MWQI which were DO, TSS, O&G, PO4, Cd, Cr and Zn. These selected parameters were then used to develop prediction models for the MWQI using artificial neural network (ANN) and multiple linear regressions (MLR). The SDA-ANN model had higher R2 value for training (0.9044) and validation (0.7113) results than SDA-MLR model and was chosen as the best model in mangrove estuarine zone. The SDA-ANN model had also demonstrated lower RMSE (5.224) than the SDA-MLR (12.7755). In summary, this work suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods.
    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. Liyana Daud, Mohamad Razali Abdullah, Siti Musliha Mat-Rasid, Ahmad Bisyri Husin Musawi Maliki, Amr Alnaimat, Muhammad Rabani Hashim, et al.
    MyJurnal
    The study attempts to use multivariate analysis to evaluate the profile of male player for developments of Long-Term Talent in Sports (LT-TiS) model based on anthropometric and motor fitness components. Data of anthropometric and motor fitness included power, flexibility, coordination and speed were obtained from 2019 respondents aged 7.32±0.52 year. Data interpretations were carried out using multivariate analysis of Principle Components Analysis (PCA) and Discriminant analysis (DA). The adequacy of sampling has been measured using Bartletts tests on sphericity and Kaiser-Meyer-Olkin (KMO) has been used, with this conformance of running the Principal Component Analysis (PCA). Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS model. Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS. As a result, two factors with eigenvalues greater than 1 were extracted which accounted for 55.00% of the variations present in the original variables was found. The two factors were used to obtain the factor score coefficients explained by 27.86% and 27.21% of the variations in player performance respectively. Factor 1 revealed high factor loading on motor fitness compared to factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing; LT−TiS≥65%, 40%≤LT−TiS
    Matched MeSH terms: Discriminant Analysis
  11. 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
  12. Muazu Musa R, P P Abdul Majeed A, Abdullah MR, Ab Nasir AF, Arif Hassan MH, Mohd Razman MA
    PLoS One, 2019;14(6):e0219138.
    PMID: 31247012 DOI: 10.1371/journal.pone.0219138
    The present study aims to identify the essential technical and tactical performance indicators that could differentiate winning and losing performance in the Asian elite beach soccer competition. A set of 20 technical and tactical performance indicators namely; shot back-third, shot mid-third, shot front-third, pass back-third, pass mid-third, pass front-third, shot in box, shot outbox, chances created, interception, turnover, goals scored 1st period, goals scored 2nd period, goals scored 3rd period, goals scored extra time, tackling, fouls committed, complete save, incomplete save and passing error were observed during the beach soccer Asian Football Confederation tournament 2017 held in Malaysia. A total of 23 matches from 12 teams were notated using StatWatch application in real-time. Discriminant analysis (DA) of standard, backward as well stepwise modes were used to develop a model for the winning (WT) and losing team (LT) whilst Mann-Whitney U test was utilized to ascertain the differences between the WT and LT with respect to the performance indicators evaluated. The standard backward, forward and stepwise discriminates the WT and the LT with an excellent accuracy of 95.65%, 91.30% and 89.13%, respectively. The standard DA model discriminated the teams from seven performance indicators whilst both the backward and forward stepwise identified two performance indicators. The Mann-Whitney U test analysis indicated that the WT is statistically significant from the LT based on the performance indicators determined from the standard mode model of the DA. It was demonstrated that seven performance indicators namely; shot front-third, pass front-third, chances created, goals scores at the 1st period, goals scored at the 2nd period, goals scored at 3rd period were directly linked to a successful performance whilst the incomplete save by the keeper attribute towards the poor performance of the team. The present finding could serve useful to the coaches as well as performance analysts as a measure of profiling successful performance and enables team improvement with respect to the associated performance indicators.
    Matched MeSH terms: Discriminant Analysis
  13. 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
  14. Zakaria SR, Saim N, Osman R, Abdul Haiyee Z, Juahir H
    Molecules, 2018 Sep 16;23(9).
    PMID: 30223605 DOI: 10.3390/molecules23092365
    This study analyzed the volatile organic compounds (VOCs) of three mango varieties (Harumanis, Tong Dam and Susu) for the discrimination of authentic Harumanis from other mangoes. The VOCs of these mangoes were extracted and analysed nondestructively using Head Space-Solid Phase Micro Extraction (HS-SPME) coupled to Gas Chromatography-Mass Spectrometry (GC-MS). Prior to the analytical method, two simple sensory analyses were carried out to assess the ability of the consumers to differentiate between the Harumanis and Tong Dam mangoes as well as their preferences towards these mangoes. On the other hand, chemometrics techniques, such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and discriminant analysis (DA), were used to visualise grouping tendencies of the volatile compounds detected. These techniques were successful in identifying the grouping tendencies of the mango samples according to the presence of their respective volatile compounds, thus enabling the identification of the groups of substances responsible for the discrimination between the authentic and unauthentic Harumanis mangoes. In addition, three ocimene compounds, namely beta-ocimene, trans beta-ocimene, and allo-ocimene, can be considered as chemical markers of the Harumanis mango, as these compounds exist in all Harumanis mango, regardless the different sources of the mangoes obtained.
    Matched MeSH terms: Discriminant Analysis
  15. Jamil SZMR, Rohani ER, Baharum SN, Noor NM
    3 Biotech, 2018 Aug;8(8):322.
    PMID: 30034986 DOI: 10.1007/s13205-018-1336-6
    Callus was induced from mangosteen (Garcinia mangostana L.) young purple-red leaves on Murashige and Skoog basal medium with various combinations of plant growth regulators. Murashige and Skoog medium with 4.44 µM 6-benzylaminopurine and 4.52 µM 2,4-dichlorophenoxyacetic acid was the best for friable callus induction. This friable callus was used for the initiation of cell suspension culture. The effects of different combinations of 6-benzylaminopurine and 2,4-dichlorophenoxyacetic acid, carbon sources and inoculum sizes were tested. It was found that combination of 2.22 µM 6-benzylaminopurine + 2.26 µM 2,4-dichlorophenoxyacetic acid, glucose (30 g/l) and 1.5 g/50 ml inoculum size was the best for cell growth. Callus and cell suspension cultures were then treated either with 100 µM methyl jasmonate as an elicitor for 5 days, or 0.5 g/l casein hydrolysate as an organic supplement for 7 days. Metabolites were then extracted and profiled using liquid chromatography-time of flight mass spectrometry. Multivariate discriminant analyses revealed significant metabolite differences (P ≤ 0.05) for callus and suspension cells treated either with methyl jasmonate or casein hydrolysate. Based on MS/MS data, methyl jasmonate stimulated the production of an alkaloid (thalsimine) and fatty acid (phosphatidyl ethanolamine) in suspension cells while in callus, an alkaloid (thiacremonone) and glucosinolate (7-methylthioheptanaldoxime) was produced. Meanwhile casein hydrolysate stimulated the production of alkaloids such as 3ß,6ß-dihydroxynortropane and cis-hinokiresinol and triterpenoids such as schidigerasaponin and talinumoside in suspension cells. This study provides evidence on the potential of secondary metabolite production from in vitro culture of mangosteen.
    Matched MeSH terms: Discriminant Analysis
  16. Lee LC, Liong CY, Jemain AA
    Analyst, 2018 Jul 23;143(15):3526-3539.
    PMID: 29947623 DOI: 10.1039/c8an00599k
    Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.
    Matched MeSH terms: Discriminant Analysis
  17. Mediani A, Abas F, Maulidiani M, Abu Bakar Sajak A, Khatib A, Tan CP, et al.
    J Physiol Biochem, 2018 May 15.
    PMID: 29766441 DOI: 10.1007/s13105-018-0631-3
    Diabetes mellitus (DM) is a chronic disease that can affect metabolism of glucose and other metabolites. In this study, the normal- and obese-diabetic rats were compared to understand the diabetes disorders of type 1 and 2 diabetes mellitus. This was done by evaluating their urine metabolites using proton nuclear magnetic resonance (1H NMR)-based metabolomics and comparing with controls at different time points, considering the induction periods of obesity and diabetes. The biochemical parameters of the serum were also investigated. The obese-diabetic model was developed by feeding the rats a high-fat diet and inducing diabetic conditions with a low dose of streptozotocin (STZ) (25 mg/kg bw). However, the normal rats were induced by a high dose of STZ (55 mg/kg bw). A partial least squares discriminant analysis (PLS-DA) model showed the biomarkers of both DM types compared to control. The synthesis and degradation of ketone bodies, tricarboxylic (TCA) cycles, and amino acid pathways were the ones most involved in the variation with the highest impact. The diabetic groups also exhibited a noticeable increase in the plasma glucose level and lipid profile disorders compared to the control. There was also an increase in the plasma cholesterol and low-density lipoprotein (LDL) levels and a decline in the high-density lipoprotein (HDL) of diabetic rats. The normal-diabetic rats exhibited the highest effect of all parameters compared to the obese-diabetic rats in the advancement of the DM period. This finding can build a platform to understand the metabolic and biochemical complications of both types of DM and can generate ideas for finding targeted drugs.
    Matched MeSH terms: Discriminant Analysis
  18. Muhammad SA, Seow EK, Mohd Omar AK, Rodhi AM, Mat Hassan H, Lalung J, et al.
    Sci Justice, 2018 Jan;58(1):59-66.
    PMID: 29332695 DOI: 10.1016/j.scijus.2017.05.008
    A total of 33 crude palm oil samples were randomly collected from different regions in Malaysia. Stable carbon isotopic composition (δ13C) was determined using Flash 2000 elemental analyzer while hydrogen and oxygen isotopic compositions (δ2H and δ18O) were analyzed by Thermo Finnigan TC/EA, wherein both instruments were coupled to an isotope ratio mass spectrometer. The bulk δ2H, δ18O and δ13C of the samples were analyzed by Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA). Unsupervised HCA and PCA methods have demonstrated that crude palm oil samples were grouped into clusters according to respective state. A predictive model was constructed by supervised OPLS-DA with good predictive power of 52.60%. Robustness of the predictive model was validated with overall accuracy of 71.43%. Blind test samples were correctly assigned to their respective cluster except for samples from southern region. δ18O was proposed as the promising discriminatory marker for discerning crude palm oil samples obtained from different regions. Stable isotopes profile was proven to be useful for origin traceability of crude palm oil samples at a narrower geographical area, i.e. based on regions in Malaysia. Predictive power and accuracy of the predictive model was expected to improve with the increase in sample size. Conclusively, the results in this study has fulfilled the main objective of this work where the simple approach of combining stable isotope analysis with chemometrics can be used to discriminate crude palm oil samples obtained from different regions in Malaysia. Overall, this study shows the feasibility of this approach to be used as a traceability assessment of crude palm oils.
    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. Muhammad Zul Fayyadh Azizo Rahman, Chong, Hui Wen, Lim, Vuanghao
    MyJurnal
    Adulterated premixed coffees have turned into an issue in Malaysia lately and have caught the eye of the authorities due to death reports linked to these products. The major cause of this issue is reported that these premixed coffees have passed food inspection test and eventually released to the market for public consumption. These coffees were claimed to be spiked with several sexual enhancers like sildenafil, tadalafil, and vardenafill, which are common drugs used to treat erectile dysfunction. Methods: Chemometrics approach using UV-Vis spectroscopy was developed to detect the selected sexual enhancer drugs found in commercial coffees by employing SIMCA-P software for the multivariate statistical analysis. Seven brands of coffee samples were purchased from local stores, and 30 sachets each were tested, hence totalling to 210 samples. Each sample was named H, J, G, W, N, T, and K, respectively. Results: Three multivariate models were generated, namely principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and partial least squares discriminant analysis (PLS-DA). OPLS-DA was selected as the best model for the overall results as it displayed minimal discriminate. Sildenafil, tadalafil, and vardenafil were detected in sample H, while vardenafil in brand J, and none in samples G, W, N, T, and K. Conclusion: OPLS-DA analysis showed discrimination for the sexual enhancer drugs in two brands of premixed coffee. The UV-Vis spectroscopy-based chemometrics method proved to be reliable and efficient in determining the selected drugs, as well as in saving time and cost.
    Matched MeSH terms: Discriminant Analysis
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