Displaying publications 61 - 80 of 104 in total

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  1. The Heritability of Palatal Rugae Morphology Among Siblings*, †
    Chong JA, Syed Mohamed AMF, Marizan Nor M, Pau A
    J Forensic Sci, 2020 Nov;65(6):2000-2007.
    PMID: 32692413 DOI: 10.1111/1556-4029.14507
    Although there is clinical applicability of the palatal rugae as an identification tool in forensic odontology, controversy exists whether the palatal rugae patterns are stable or variable. The greater the genetic component, the higher the probability that palatal rugae patterns are stable. The aim of this study was to compare the palatal rugae morphology between full siblings and the proportion of variability due to genetic component. This cross-sectional study was conducted on digital models of 162 siblings aged 15-30 years old. The palatal rugae patterns were assessed with Thomas and Kotze (1983) classification using Geomagic Studio software (3D Systems, Rock Hill, SC). The palatal rugae morphology between siblings showed significantly similar characteristics for total number of left rugae (p = 0.001), left primary rugae (p = 0.017), secondary rugae for right (p = 0.024) and left sides (p = 0.001), right straight rugae (p = 0.010), and right convergent rugae (p = 0.005) accounting for at least 6.25%-12.8% of the variability due to heredity. Despite the similarities found, the palatal rugae patterns showed significant differences between siblings of at least 46.9% (p = 0.001). Zero heritability was found in 9 of the 14 rugae patterns. Meanwhile, total number of rugae, primary, backward, and convergent rugae showed moderate heritability (h2  > 0.3) and total number of secondary rugae showed high heritability (h2  > 0.6). In conclusion, despite the individuality characteristics, an appreciable hereditary component is observed with significant similarities found between sibling pairs and the palatal rugae patterns were both environmentally and genetically influenced.
    Matched MeSH terms: Discriminant Analysis
  2. Spatial aspects of surface water quality in the Jakara Basin, Nigeria using chemometric analysis
    Mustapha A, Aris AZ
    J Environ Sci Health A Tox Hazard Subst Environ Eng, 2012;47(10):1455-65.
    PMID: 22571534 DOI: 10.1080/10934529.2012.673305
    Multivariate statistical techniques such as hierarchical Agglomerated cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) were applied to identify the spatial variation and pollution sources of Jakara River, Kano, Nigeria. Thirty surface water samples were collected: 23 along Getsi River and 7 along the main channel of River Jakara. Twenty-three water quality parameters, namely pH, temperature, turbidity, electrical conductivity (EC), dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), Faecal coliform, total solids (TS), nitrates (NO(3)(-)), phosphates (PO(4)(3-)), cobalt (Co), iron (Fe), nickel (Ni), manganese (Mn), copper (Cu), sodium (Na), potassium (K), mercury (Hg), chromium (Cr), cadmium (Cd), lead (Pb), magnesium (Mg), and calcium(Ca) were analysed. HACA grouped the sampling points into three clusters based on the similarities of river water quality characteristics: industrial, domestic, and agricultural water pollution sources. Forward and backward DA effectively discriminated 5 and 15 water quality variables, respectively, each assigned with 100% correctness from the original 23 variables. PCA and FA were used to investigate the origin of each water quality parameter due to various land use activities, 7 principal components were obtained with 77.5% total variance, and in addition PCA identified 3 latent pollution sources to support HACA. From this study, one can conclude that the application of multivariate techniques derives meaningful information from water quality data.
    Matched MeSH terms: Discriminant Analysis
  3. Spatial assessment of Langat River water quality using chemometrics
    Juahir H, Zain SM, Aris AZ, Yusoff MK, Mokhtar MB
    J Environ Monit, 2010 Jan;12(1):287-95.
    PMID: 20082024 DOI: 10.1039/b907306j
    The present study deals with the assessment of Langat River water quality with some chemometrics approaches such as cluster and discriminant analysis coupled with an artificial neural network (ANN). The data used in this study were collected from seven monitoring stations under the river water quality monitoring program by the Department of Environment (DOE) from 1995 to 2002. Twenty three physico-chemical parameters were involved in this analysis. Cluster analysis successfully clustered the Langat River into three major clusters, namely high, moderate and less pollution regions. Discriminant analysis identified seven of the most significant parameters which contribute to the high variation of Langat River water quality, namely dissolved oxygen, biological oxygen demand, pH, ammoniacal nitrogen, chlorine, E. coli, and coliform. Discriminant analysis also plays an important role as an input selection parameter for an ANN of spatial prediction (pollution regions). The ANN showed better prediction performance in discriminating the regional area with an excellent percentage of correct classification compared to discriminant analysis. Multivariate analysis, coupled with ANN, is proposed, which could help in decision making and problem solving in the local environment.
    Matched MeSH terms: Discriminant Analysis
  4. The use of the FACT-H&N (v4) in clinical settings within a developing country: a mixed method study
    Bilal S, Doss JG, Rogers SN
    J Craniomaxillofac Surg, 2014 Dec;42(8):1590-7.
    PMID: 25224886 DOI: 10.1016/j.jcms.2014.04.015
    In the last decade there has been an increasing awareness about 'quality of life' (QOL) of cancer survivors in developing countries. The study aimed to cross-culturally adapt and validate the FACT-H&N (v4) in Urdu language for Pakistani head and neck cancer patients. In this study the 'same language adaptation method' was used. Cognitive debriefing through in-depth interviews of 25 patients to assess semantic, operational and conceptual equivalence was done. The validation phase included 50 patients to evaluate the psychometric properties. The translated FACT-H&N was easily comprehended (100%). Cronbach's alpha for FACT-G subscales ranged from 0.726 - 0.969. The head and neck subscale and Pakistani questions subscale showed low internal consistency (0.426 and 0.541 respectively). Instrument demonstrated known-group validity in differentiating patients of different clinical stages, treatment status and tumor sites (p < 0.05). Most FACT summary scales correlated strongly with each other (r > 0.75) and showed convergent validity (r > 0.90), with little discriminant validity. Factor analysis revealed 6 factors explaining 85.1% of the total variance with very good (>0.8) Kaiser-Meyer-Olkin and highly significant Bartlett's Test of Sphericity (p < 0.001). The cross-culturally adapted FACT-H&N into Urdu language showed adequate reliability and validity to be incorporated in Pakistani clinical settings for head and neck cancer patients.
    Matched MeSH terms: Discriminant Analysis
  5. Quantitative Tetraplex Real-Time Polymerase Chain Reaction Assay with TaqMan Probes Discriminates Cattle, Buffalo, and Porcine Materials in Food Chain
    Hossain MAM, Ali ME, Sultana S, Asing, Bonny SQ, Kader MA, et al.
    J Agric Food Chem, 2017 May 17;65(19):3975-3985.
    PMID: 28481513 DOI: 10.1021/acs.jafc.7b00730
    Cattle, buffalo, and porcine materials are widely adulterated, and their quantification might safeguard health, religious, economic, and social sanctity. Recently, conventional polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (RFLP) assays have been documented but they are just suitable for identification, cannot quantify adulterations. We described here a quantitative tetraplex real-time PCR assay with TaqMan Probes to quantify contributions from cattle, buffalo, and porcine materials simultaneously. Amplicon-sizes were very short (106-, 90-, and 146-bp for cattle, buffalo, and porcine) because longer targets could be broken down, bringing serious ambiguity in molecular diagnostics. False negative detection was eliminated through an endogenous control (141-bp site of eukaryotic 18S rRNA). Analysis of 27 frankfurters and 27 meatballs reflected 84-115% target recovery at 0.1-10% adulterations. Finally, a test of 36 commercial products revealed 71% beef frankfurters, 100% meatballs, and 85% burgers contained buffalo adulteration, but no porcine was found in beef products.
    Matched MeSH terms: Discriminant Analysis
  6. Double Gene Targeting Multiplex Polymerase Chain Reaction-Restriction Fragment Length Polymorphism Assay Discriminates Beef, Buffalo, and Pork Substitution in Frankfurter Products
    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
  7. Fulltext Changes in the urinary metabolites in losartan-treated spontaneously hypertensive rats
    Norasikin Ab Azis, Mohd Saleh Ahmad Kamal, Zurain Radjeni, Ahmed Mediani, Renu Agarwal
    Journal of Clinical and Health Sciences, 2021;6(1):32-45.
    MyJurnal
    Introduction: This study examined the association of losartan induced changes in urinary
    metabolomic profile with the changes in blood pressure (BP) and renin-angiotensinaldosterone system (RAAS) in spontaneously hypertensive rats (SHR). Methods: Male SHR
    were administered with either 0.5 mL of distilled water (control group, n=6) or 10 mg.kg-1 of
    losartan (group 2, n=6) daily by oral gavage for 4 weeks. Body weight, BP, food and water
    intake were measured weekly. At week 4, urine was collected for urinary electrolyte analysis
    and metabolite profiling, after which the animals were euthanised by decapitation and blood
    was collected for analysis of components of RAAS and electrolyte concentrations. Urine
    metabolite profile of SHR was determined using proton nuclear magnetic resonance (
    1H-NMR)
    spectrometry combined with multivariate data analysis. Results: At week 4, losartan-treated
    SHR had significantly lower BP than non-treated SHR. There were no differences in water
    and food intake, body weight, serum and urinary electrolyte concentrations or in their urinary
    excretions between the two groups. No differences were evident in the components of RAAS
    except that the angiotensinogen level was significantly higher in losartan-treated SHR
    compared to non-treated SHR. Orthogonal partial least squares discriminant analysis (OPLSDA) showed clear separation of urinary metabolites between control and losartan-treated
    SHR. Losartan-treated SHR group was separated from the control group by changes in the
    intermediates involved in glycine, serine and threonine metabolism. Conclusion:
    Antihypertensive effect of losartan in SHR seems to be associated with changes in urinary
    metabolite profile, particularly involving the metabolism of glycine, serine and threonine.
    Matched MeSH terms: Discriminant Analysis
  8. Fulltext Assessment of physical properties of ripe banana flour prepared from two varieties: Cavendish and Dream banana
    Abbas, F.M.A., Saifullah, R., Azhar, M.E.
    International Food Research Journal, 2009;16(2):183-189.
    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
  9. Fulltext Multivariate statistical analysis of antioxidants in dates (Phoenix dactylifera)
    Abbas, F.M.A., Foroogh, B., Liong, M.T., Azhar, M.E.
    International Food Research Journal, 2008;15(2):193-200.
    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
  10. The influence of ball-swing on the timing and coordination of a natural interceptive task
    Sarpeshkar V, Mann DL, Spratford W, Abernethy B
    Hum Mov Sci, 2017 Aug;54:82-100.
    PMID: 28410536 DOI: 10.1016/j.humov.2017.04.003
    Successful interception relies on the use of perceptual information to accurately guide an efficient movement strategy that allows performers to be placed at the right place at the right time. Although previous studies have highlighted the differences in the timing and coordination of movement that underpin interceptive expertise, very little is known about how these movement patterns are adapted when intercepting targets that follow a curvilinear flight-path. The aim of this study was to examine how curvilinear ball-trajectories influence movement patterns when intercepting a fast-moving target. Movement timing and coordination was examined when four groups of cricket batters, who differed in their skill level and/or age, hit targets that followed straight or curvilinear flight-paths. The results revealed that when compared to hitting straight trials, (i) mixing straight with curvilinear trials altered movement coordination and when the ball was hit, (ii) curvilinear trajectories reduced interceptive performance and significantly delayed the timing of all kinematic moments, but there were (iii) larger decrease in performance when the ball swung away from (rather than in towards) the performer. Movement coordination differed between skill but not age groups, suggesting that skill-appropriate movement patterns that are apparent in adults may have fully emerged by late adolescence.
    Matched MeSH terms: Discriminant Analysis
  11. Fulltext Exposure to environmental microbiota explains persistent abdominal pain and irritable bowel syndrome after a major flood
    Yusof N, Hamid N, Ma ZF, Lawenko RM, Wan Mohammad WMZ, Collins DA, et al.
    Gut Pathog, 2017;9:75.
    PMID: 29255490 DOI: 10.1186/s13099-017-0224-7
    Background: After an environmental disaster, the affected community is at increased risk for persistent abdominal pain but mechanisms are unclear. Therefore, our study aimed to determine association between abdominal pain and poor water, sanitation and hygiene (WaSH) practices, and if small intestinal bacterial overgrowth (SIBO) and/or gut dysbiosis explain IBS, impaired quality of life (QOL), anxiety and/or depression after a major flood.

    Results: New onset abdominal pain, IBS based on the Rome III criteria, WaSH practices, QOL, anxiety and/or depression, SIBO (hydrogen breath testing) and stools for metagenomic sequencing were assessed in flood victims. Of 211 participants, 37.9% (n = 80) had abdominal pain and 17% (n = 36) with IBS subtyped diarrhea and/or mixed type (n = 27 or 12.8%) being the most common. Poor WaSH practices and impaired quality of life during flood were significantly associated with IBS. Using linear discriminant analysis effect size method, gut dysbiosis was observed in those with anxiety (Bacteroidetes and Proteobacteria, effect size 4.8), abdominal pain (Fusobacteria, Staphylococcus, Megamonas and Plesiomonas, effect size 4.0) and IBS (Plesiomonas and Trabulsiella, effect size 3.0).

    Conclusion: Disturbed gut microbiota because of environmentally-derived organisms may explain persistent abdominal pain and IBS after a major environmental disaster in the presence of poor WaSH practices.

    Matched MeSH terms: Discriminant Analysis
  12. Fulltext Fischer Linear Discrimination and Quadratic Discrimination Analysis-Based Data Mining Technique for Internet of Things Framework for Healthcare
    Hasan MK, Ghazal TM, Alkhalifah A, Abu Bakar KA, Omidvar A, Nafi NS, et al.
    Front Public Health, 2021;9:737149.
    PMID: 34712639 DOI: 10.3389/fpubh.2021.737149
    The internet of reality or augmented reality has been considered a breakthrough and an outstanding critical mutation with an emphasis on data mining leading to dismantling of some of its assumptions among several of its stakeholders. In this work, we study the pillars of these technologies connected to web usage as the Internet of things (IoT) system's healthcare infrastructure. We used several data mining techniques to evaluate the online advertisement data set, which can be categorized as high dimensional with 1,553 attributes, and the imbalanced data set, which automatically simulates an IoT discrimination problem. The proposed methodology applies Fischer linear discrimination analysis (FLDA) and quadratic discrimination analysis (QDA) within random projection (RP) filters to compare our runtime and accuracy with support vector machine (SVM), K-nearest neighbor (KNN), and Multilayer perceptron (MLP) in IoT-based systems. Finally, the impact on number of projections was practically experimented, and the sensitivity of both FLDA and QDA with regard to precision and runtime was found to be challenging. The modeling results show not only improved accuracy, but also runtime improvements. When compared with SVM, KNN, and MLP in QDA and FLDA, runtime shortens by 20 times in our chosen data set simulated for a healthcare framework. The RP filtering in the preprocessing stage of the attribute selection, fulfilling the model's runtime, is a standpoint in the IoT industry. Index Terms: Data Mining, Random Projection, Fischer Linear Discriminant Analysis, Online Advertisement Dataset, Quadratic Discriminant Analysis, Feature Selection, Internet of Things.
    Matched MeSH terms: Discriminant Analysis
  13. Comparison on three classification techniques for sex estimation from the bone length of Asian children below 19 years old: an analysis using different group of ages
    Darmawan MF, Yusuf SM, Kadir MR, Haron H
    Forensic Sci Int, 2015 Feb;247:130.e1-11.
    PMID: 25540897 DOI: 10.1016/j.forsciint.2014.11.007
    Sex estimation is used in forensic anthropology to assist the identification of individual remains. However, the estimation techniques tend to be unique and applicable only to a certain population. This paper analyzed sex estimation on living individual child below 19 years old using the length of 19 bones of left hand applied for three classification techniques, which were Discriminant Function Analysis (DFA), Support Vector Machine (SVM) and Artificial Neural Network (ANN) multilayer perceptron. These techniques were carried out on X-ray images of the left hand taken from an Asian population data set. All the 19 bones of the left hand were measured using Free Image software, and all the techniques were performed using MATLAB. The group of age "16-19" years old and "7-9" years old were the groups that could be used for sex estimation with as their average of accuracy percentage was above 80%. ANN model was the best classification technique with the highest average of accuracy percentage in the two groups of age compared to other classification techniques. The results show that each classification technique has the best accuracy percentage on each different group of age.
    Matched MeSH terms: Discriminant Analysis
  14. Odontometric sex variation in Malaysians with application to sex prediction
    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
  15. Analysis of geometric moments as features for firearm identification
    Md Ghani NA, Liong CY, Jemain AA
    Forensic Sci Int, 2010 May 20;198(1-3):143-9.
    PMID: 20211535 DOI: 10.1016/j.forsciint.2010.02.011
    The task of identifying firearms from forensic ballistics specimens is exacting in crime investigation since the last two decades. Every firearm, regardless of its size, make and model, has its own unique 'fingerprint'. These fingerprints transfer when a firearm is fired to the fired bullet and cartridge case. The components that are involved in producing these unique characteristics are the firing chamber, breech face, firing pin, ejector, extractor and the rifling of the barrel. These unique characteristics are the critical features in identifying firearms. It allows investigators to decide on which particular firearm that has fired the bullet. Traditionally the comparison of ballistic evidence has been a tedious and time-consuming process requiring highly skilled examiners. Therefore, the main objective of this study is the extraction and identification of suitable features from firing pin impression of cartridge case images for firearm recognition. Some previous studies have shown that firing pin impression of cartridge case is one of the most important characteristics used for identifying an individual firearm. In this study, data are gathered using 747 cartridge case images captured from five different pistols of type 9mm Parabellum Vektor SP1, made in South Africa. All the images of the cartridge cases are then segmented into three regions, forming three different set of images, i.e. firing pin impression image, centre of firing pin impression image and ring of firing pin impression image. Then geometric moments up to the sixth order were generated from each part of the images to form a set of numerical features. These 48 features were found to be significantly different using the MANOVA test. This high dimension of features is then reduced into only 11 significant features using correlation analysis. Classification results using cross-validation under discriminant analysis show that 96.7% of the images were classified correctly. These results demonstrate the value of geometric moments technique for producing a set of numerical features, based on which the identification of firearms are made.
    Matched MeSH terms: Discriminant Analysis
  16. STR data for the AmpFlSTR Profiler loci from the three main ethnic population groups (Malay, Chinese and Indian) in Malaysia
    Lim KB, Jeevan NH, Jaya P, Othman MI, Lee YH
    Forensic Sci Int, 2001 Jun 01;119(1):109-12.
    PMID: 11348801
    Allele frequencies for the nine STRs genetic loci included in the AmpFlSTR Profiler kit were obtained from samples of unrelated individuals comprising 139-156 Malays, 149-153 Chinese and 132-135 Indians, residing in Malaysia.
    Matched MeSH terms: Discriminant Analysis
  17. The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil
    Rohman A, Che Man YB
    Food Chem, 2011 Nov 15;129(2):583-588.
    PMID: 30634271 DOI: 10.1016/j.foodchem.2011.04.070
    Currently, the authentication of virgin coconut oil (VCO) has become very important due to the possible adulteration of VCO with cheaper plant oils such as corn (CO) and sunflower (SFO) oils. Methods involving Fourier transform mid infrared (FT-MIR) spectroscopy combined with chemometrics techniques (partial least square (PLS) and discriminant analysis (DA)) were developed for quantification and classification of CO and SFO in VCO. MIR spectra of oil samples were recorded at frequency regions of 4000-650cm-1 on horizontal attenuated total reflectance (HATR) attachment of FTIR. DA can successfully classify VCO and that adulterated with CO and SFO using 10 principal components. Furthermore, PLS model correlates the actual and FTIR estimated values of oil adulterants (CO and SFO) with coefficient of determination (R2) of 0.999.
    Matched MeSH terms: Discriminant Analysis
  18. Discrimination of Malaysian stingless bee honey from different entomological origins based on physicochemical properties and volatile compound profiles using chemometrics and machine learning
    Sharin SN, Sani MSA, Jaafar MA, Yuswan MH, Kassim NK, Manaf YN, et al.
    Food Chem, 2021 Jun 01;346:128654.
    PMID: 33461823 DOI: 10.1016/j.foodchem.2020.128654
    Identification of honey origin based on specific chemical markers is important for honey authentication. This study is aimed to differentiate Malaysian stingless bee honey from different entomological origins (Heterotrigona bakeri, Geniotrigona thoracica and Tetrigona binghami) based on physicochemical properties (pH, moisture content, ash, total soluble solid and electrical conductivity) and volatile compound profiles. The discrimination pattern of 75 honey samples was observed using Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), Partial Least Square-Discriminant Analysis (PLS-DA), and Support Vector Machine (SVM). The profiles of H. bakeri and G. thoracica honey were close to each other, but clearly separated from T. binghami honey, consistent with their phylogenetic relationship. T. binghami honey is marked by significantly higher electrical conductivity, moisture and ash content, and high abundance of 2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl-1-cyclohexene-1-acetaldehyde and ethyl 2-(5-methyl-5-vinyltetrahydrofuran-2-yl)propan-2-yl carbonate. Copaene was proposed as chemical marker for G. thoracica honey. The potential of different parameters that aid in honey authentication was highlighted.
    Matched MeSH terms: Discriminant Analysis
  19. Tracing the geographical origin of rice by stable isotopic analyses combined with chemometrics
    Wang J, Chen T, Zhang W, Zhao Y, Yang S, Chen A
    Food Chem, 2020 May 30;313:126093.
    PMID: 31927205 DOI: 10.1016/j.foodchem.2019.126093
    Multivariate stable isotope analysis combined with chemometrics was used to investigate and discriminate rice samples from six rice producing provinces in China (Heilongjiang, Jilin, Jiangsu, Zhejiang, Hunan and Guizhou) and four other Asian rice producing countries (Thailand, Malaysia, Philippines, and Pakistan). The stable isotope characteristics were analyzed for rice of different species cultivated with varied farming methods at different altitudes and latitudes/longitudes. The index groups of δ13C, δ15N, δ18O, 207/206Pb and 208/207Pb were screened and established for the selected samples with different geographical features by means of principal component analysis (PCA) and discriminant analysis (DA), which would provide a sound technical solution for rice traceability and serve as a template for further research on the traceability of other agricultural products, especially plant-derived products.
    Matched MeSH terms: Discriminant Analysis
  20. Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia
    Syed Abdul Mutalib SN, Juahir H, Azid A, Mohd Sharif S, Latif MT, Aris AZ, et al.
    Environ Sci Process Impacts, 2013 Sep;15(9):1717-28.
    PMID: 23831918 DOI: 10.1039/c3em00161j
    The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.
    Matched MeSH terms: Discriminant Analysis
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