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  1. Fulltext Postural sway and shooting accuracy of skilled recurve archers
    Muhamad Noor Mohamed, Azrul Hisham Azhar
    Movement Health & Exercise, 2012;1(1):49-60.
    MyJurnal
    Postural movements potentially affect aiming stability in archery, thus contributing to chances of inconsistent hits. According to the expertisenovice paradigm, the factor that sets winners apart from ordinary athletes is the former’s ability to control minute changes in their performance. The
    present study seeks to determine the relationship between postural sway and shooting performance amongst Malaysian skilled recurve archers. Twenty one skilled Malaysian archers participated in this study, where performance level was measured by rank tournaments International Archery Federation (FITA) score. Postural sway was assessed in terms of anterior deviation (positive value) and posterior deviation (negative value) using ZEPHYR Bio-Harness. Postural sway was analysed at the following three phases; (i) setup, (ii) aiming, and (iii) release. Participants shot 12 arrows to a 30-meter target. Data yielded a significant relationship between postural sway and shooting performance. The correlation coefficients between shooting performance and postural sway value for skilled archers ranged between (r = -0.021 to 0.248) with the highest correlation recorded at the release phase, with the lowest at the aiming phase. The setup phase showed the only anterior deviation throughout the test. During the setup and release phases, correlation between postural sway with shooting performance was significantly noted (p < 0.001). Multiple regression analysis showed that postural sway during the setup and release phases were the significant indicators for shooting performance, accounting approximately 17% and 24% of the variances respectively. In sum, the results indicate that reducing postural sway
    during the release phase can increase shooting performance of skilled archery athletes, thus establishing a significant relationship between the postural sway value with shooting performance of skilled archers.
    Matched MeSH terms: Multivariate Analysis
  2. Fulltext Dynamic similarity distance with mean average precision tool
    Nur Atikah Arbain, Mohd Sanusi Azmi, Sharifah Sakinah Syed Ahmad, Azah Kamilah Muda, Intan Ermahami A. Jalil, King Ming Tiang
    Pertanika Journal of Science & Technology, 2017;25(106):11-18.
    MyJurnal
    In recent years, many classification models have been developed and applied to increase their accuracy. The concept of distance between two samples or two variables is a fundamental concept in multivariate analysis. This paper proposed a tool that used different similarity distance approaches with ranking method based on Mean Average Precision (MAP). In this study, several similarity distance methods were used, such as Euclidean, Manhattan, Chebyshev, Sorenson and Cosine. The most suitable distance measure was based on the smallest value of distance between the samples. However, the real solution showed that the results were not accurate as and thus, MAP was considered the best approach to overcome current limitations.
    Matched MeSH terms: Multivariate Analysis
  3. Fulltext Kernel partial diagnostic robust potential to handle high-dimensional and irregular data space on near infrared spectral data
    Silalahi DD, Midi H, Arasan J, Mustafa MS, Caliman JP
    Heliyon, 2020 Jan;6(1):e03176.
    PMID: 32042959 DOI: 10.1016/j.heliyon.2020.e03176
    In practice, the collected spectra are very often composes of complex overtone and many overlapping peaks which may lead to misinterpretation because of its significant nonlinear characteristics. Using linear solution might not be appropriate. In addition, with a high-dimension of dataset due to large number of observations and data points the classical multiple regressions will neglect to fit. These complexities commonly will impact to multicollinearity problem, furthermore the risk of contamination of multiple outliers and high leverage points also increases. To address these problems, a new method called Kernel Partial Diagnostic Robust Potential (KPDRGP) is introduced. The method allows the nonlinear solution which maps nonlinearly the original input

    X

    matrix into higher dimensional feature mapping with corresponds to the Reproducing Kernel Hilbert Spaces (RKHS). In dimensional reduction, the method replaces the dot products calculation of elements in the mapped data to a nonlinear function in the original input space. To prevent the contamination of the multiple outlier and high leverage points the robust procedure using Diagnostic Robust Generalized Potentials (DRGP) algorithm was used. The results verified that using the simulation and real data, the proposed KPDRGP method was superior to the methods in the class of non-kernel and some other robust methods with kernel solution.
    Matched MeSH terms: Multivariate Analysis
  4. Effect of steam blanching on lycopene and total phenolics in pink guava puree industry by-products
    Kong, K.W., Emmy, H.K.I., Azizah, O., Amin, I., Tan, C.P.
    International Food Research Journal, 2010;17(2):461-468.
    MyJurnal
    Lycopene and total phenolics of pink guava puree industry by-products (refiner, siever and decanter)
    were evaluated after steam blanching at selected temperatures and times. Lycopene content was in the order of decanter > siever > refiner (7.3, 6.3 and 1.5 mg/100 g, respectively), and the content of total phenolics was in the order of refiner > siever > decanter (4434.1, 2881.3 and 1529.3 mg GAE/100 g, respectively). Regression coefficients for temperatures (x1) and times (x2) from multiple linear regression models of siever and decanter showed significant (p
    Matched MeSH terms: Multivariate Analysis
  5. Fulltext Optimization of soaking conditions for the production of seaweed (Kappaphycus alverazii) paste using response surface methodology
    Siah, W. M., Aminah, A., Ishak, A.
    International Food Research Journal, 2014;21(2):471-477.
    MyJurnal
    The effects of soaking conditions on the quality characteristics of seaweed paste of Kappaphycus alverazii species were studied. Response Surface Methodology (RSM) with a 2-factor, 5-level central composite design (CCD) was conducted to determine the optimum soaking conditions. The interactive effect of dry seaweed: soaking water ratio (X1 = 1: 15-50) and soaking duration (X2 = 30-120 min) on the gel strength (g), whiteness, expansion (%), moisture content (%) and protein content (g/100 g) of the paste were determined. Results showed that the experimental data could be adequately fitted into a second-order polynomial model with multiple regression coefficients (R2) of 0.8141, 0.9245, 0.9118, 0.9113 and 0.9271 for the gel strength, whiteness, expansion, moisture content and protein content, respectively. The gel strength, whiteness, expansion, moisture content and protein content of seaweed paste were dependent on the ratio of dry seaweed to soaking water and also soaking duration. The proposed optimum soaking conditions for the production of seaweed paste is at a ratio of 1:15 (dry seaweed : soaking water) and soaking duration of 117.06 min. Based on the result obtained, the RSM demonstrated a suitable approach for the processing optimization of Kappaphycus alverazii paste.
    Matched MeSH terms: Multivariate Analysis
  6. Fulltext The graphical assessment of multivariate normality using SPSS
    Wan Nor Arifin
    Education in Medicine Journal, 2015;7(2):71-75.
    MyJurnal
    Multivariate analyses depend on multivariate normality assumption. Although the analyses are available in SPSS, it is not possible to assess the assumption from the basic package. Statistical assessment of the normality is available in a specialized package, SPSS Amos, in form of Mardia's multivariate kurtosis. However, graphical assessment of the normality by chi-square versus Mahalanobis distance plot is not available in both of the packages. The aim of this article is to present the steps to construct the plot in SPSS in a point-and-click manner as expected by most SPSS users.
    Matched MeSH terms: Multivariate Analysis
  7. A monte carlo simulation study on high leverage collinearity-enhancing observation and its effect on multicollinearity pattern
    Habshah Midi, Bagheri A, Rahmatullah Imon A
    Sains Malaysiana, 2011;40:1437-1447.
    Outliers in the X-direction or high leverage points are the latest known source of multicollinearity. Multicollinearity is a nonorthogonality of two or more explanatory variables in multiple regression models, which may have important influential impacts on interpreting a fitted regression model. In this paper, we performed Monte Carlo simulation studies to achieve two main objectives. The first objective was to study the effect of certain magnitude and percentage of high leverage points, which are two important issues in tending the high leverage points to be collinearity-enhancing observations, on the multicollinarity pattern of the data. The second objective was to investigate in which situations these points do make different degrees of multicollinearity, such as moderate or severe. According to the simulation results, high leverage points should be in large magnitude for at least two explanatory variables to guarantee that they are the cause of multicollinearity problems. We also proposed some practical Lower Bound (LB) and Upper Bound (UB) for High Leverage Collinearity Influential Measure (HLCIM) which is an essential measure in detecting the degree of multicollinearity. A well-known example is used to confirm the simulation results.
    Matched MeSH terms: Multivariate Analysis
  8. Load forecasting using combination model of multiple linear regression with neural network for Malaysian city
    Nur Arina Bazilah Kamisan, Muhammad Hisyam Lee, Suhartono Suhartono, Abdul Ghapor Hussin, Yong Zulina Zubairi
    Sains Malaysiana, 2018;47:419-426.
    Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a
    data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the
    forecast a linear relationship with other factors but MLR has a disadvantage of having difficulties in modelling a nonlinear
    relationship between the variables and influencing factors. Neural network (NN) model, on the other hand, is a good
    model for modelling a nonlinear data. Therefore, in this study, a combination of MLR and NN models has proposed this
    combination to overcome the problem. This hybrid model is then compared with MLR and NN models to see the performance
    of the hybrid model. RMSE is used as a performance indicator and a proposed graphical error plot is introduce to see the
    error graphically. From the result obtained this model gives a better forecast compare to the other two models.
    Matched MeSH terms: Multivariate Analysis
  9. Fulltext Morphological characterization of the progenies of pure and reciprocal crosses of Pangasianodon hypophthalmus (Sauvage, 1878) and Clarias gariepinus (Burchell, 1822)
    Okomoda TV, Koh ICC, Hassan A, Amornsakun T, Shahreza SM
    Sci Rep, 2018 02 28;8(1):3827.
    PMID: 29491444 DOI: 10.1038/s41598-018-22149-4
    Twenty-five traditional and thirty-four geometric morphometric comparisons were carried out on pure and reciprocal crosses of Pangasianodon hypophthalmus (Sauvage, 1878) and Clarias gariepinus (Burchell, 1822). Thirty fish samples each of the C. gariepinus (CH), P. hypophthalmus (PH), Pangapinus (♀PH × ♂CG) and the two distinct morphotypes of the Clariothalmus (♀CG × ♂PH) (Clarias-like and Panga-like) between the ages of four and six months were used for this study. Phenotypically, the Clarias-like Clariothalmus and the Pangapinus progenies were indistinguishable from their maternal parents while the Panga-like Clariothalmus was a phenotypic intermediary of the putative parents but looks more closely to the paternal parent. Hence, both univariate proportion and multivariate analysis of the collected data successfully separated the different fishes into three multivariate spaces. The analysis of the dendrogram with complete linkage and Euclidean distance further showed the close relationship of the isolated Panga-like Clariothalmus progenies to the paternal parent, however, Clarias-like Clariothalmus and the Pangapinus were completely intermingled with their maternal parents. The most important index of discrimination of these fishes into different multivariate spaces was the fin characteristic which showed 100% exclusive ranges for the individual groups in many cases.
    Matched MeSH terms: Multivariate Analysis
  10. Dose-Dependent Effect of Wedelia trilobata Essential Oil (EO) on Lettuce (Lactuca sativa L.) with Multivariate Analysis
    Azlan Azizan K, Izzairy Zamani A, Azlan Nor Muhammad N, Khairudin K, Yusoff N, Firdaus Nawawi M
    Chem Biodivers, 2022 Mar;19(3):e202100833.
    PMID: 34962057 DOI: 10.1002/cbdv.202100833
    Understanding metabolite changes and underlying metabolic pathways that may be affected in target plants following essential oils (EOs) exposure is of great importance. In this study, a gas chromatography-mass spectrometry (GC/MS) based metabolomics approach was used to determine the metabolite changes in lettuce (Lactuca sativa L.) shoot and root after exposure to different concentrations of W. trilobata EO. Multivariate analyses of principal component analysis (PCA) and orthogonal partial least-discriminant analysis (OPLS-DA) corroborated that shoot and root of lettuce responded differently to W. trilobata EO. In EO-exposed shoot samples, an increase in the levels of malic acid, glutamine, serine, lactose and α-glucopyranose affected important metabolism pathways such as glycolysis, fructose and mannose metabolism and galactose metabolism. The findings suggest that lettuce may be up-regulating these metabolites to increase tolerance against W. trilobata EO. In EO-exposed root samples, changes in fatty acid biosynthesis, elongation, degradation, phenylalanine, tyrosine and tryptophan metabolism were linked to a decrease in lyxose, palmitic acid, octadecanoic acid, aspartic acid, phenylalanine and myo-inositol. These results indicate that W. trilobata EO could cause alterations in fatty acid compositions and lead to inhibition of roots growth. Together, these findings provide insight into the metabolic responses of lettuce upon W. trilobata EO exposure, as well as potential mechanisms of action of W. trilobata EO as bio-herbicides.
    Matched MeSH terms: Multivariate Analysis
  11. Fulltext Multivariate optimization in the biosynthesis of a triethanolamine (TEA)-based esterquat cationic surfactant using an artificial neural network
    Masoumi HR, Kassim A, Basri M, Abdullah DK, Haron MJ
    Molecules, 2011 Jun 29;16(7):5538-49.
    PMID: 21716175 DOI: 10.3390/molecules16075538
    An Artificial Neural Network (ANN) based on the Quick Propagation (QP) algorithm was used in conjunction with an experimental design to optimize the lipase-catalyzed reaction conditions for the preparation of a triethanolamine (TEA)-based esterquat cationic surfactant. Using the best performing ANN, the optimum conditions predicted were an enzyme amount of 4.77 w/w%, reaction time of 24 h, reaction temperature of 61.9 °C, substrate (oleic acid: triethanolamine) molar ratio of 1:1 mole and agitation speed of 480 r.p.m. The relative deviation percentage under these conditions was less than 4%. The optimized method was successfully applied to the synthesis of the TEA-based esterquat cationic surfactant at a 2,000 mL scale. This method represents a more flexible and convenient means for optimizing enzymatic reaction using ANN than has been previously reported by conventional methods.
    Matched MeSH terms: Multivariate Analysis*
  12. Fulltext Conchology Variations in Species Identification of Pachychilidae (Mollusca, Gastropoda, Cerithiodea) through Multivariate Analysis
    Hamli H, Hamed NA, Azmai SHS, Idris MH
    Trop Life Sci Res, 2020 Jul;31(2):145-158.
    PMID: 32922672 DOI: 10.21315/tlsr2020.31.2.7
    Pachychilidae is one of the freshwater gastropod family which was previously known under the Potamididae and Thiaridae families. Studies on freshwater gastropods especially on conchcology examinantions are still inadequate compared to marine gastropods. Morphological and morphometric studies of gastropods are practically used to identify and differentiate between species and necessary to complement molecular studies due to its low cost and tolerable resolving power of discrimination. The aim of the current study is to provide information on morphological and morphometric characteristics of Pachychilidae in Bintulu, Sarawak stream. A total of 20 individuals from each species of Sulcospira testudinaria, Sulcospira schmidti, Brotia siamensis, and Tylomelania sp. from Pachychilidae familiy were collected at three different sites from a small stream within the Bintulu area. Fourteen measurement of shell morphometrics were converted into proportioned ratios and analysed for univariate and multivariate analysis. Three shell morphometric (Aperture width, AW; Whorl width, WW2; and, Interior anterior length, AINL) of Pachychilidae indicated significant differences (P < 0.05) between species. However, multivariate analysis revealed that these shell morphometrics are pre-eminent factors to discriminate genus Sulcospira, Brotia and Tylomelania, as well as between Sulcospira species. This current study also suggests that these three characteristics are unique to Sulcospira species due to strong distinction among species. Findings on these three characteristics are significant for Sulcospira spp. as this study is the first shell morphometric report on the Pachychilidae species in Sarawak.
    Matched MeSH terms: Multivariate Analysis
  13. Fulltext Determination of quality parameters in Cavendish banana during ripening by NIR spectroscopy
    Liew, C.Y., Lau, C.Y.
    International Food Research Journal, 2012;19(2):751-758.
    MyJurnal
    Studies have been carried out to determine the chemical (soluble solid content) and physical (firmness) parameters of locally grown Cavendish banana by near infrared (NIR) spectroscopy. NIR spectra in the wavelength region of 680-2500 nm were obtained from a total of 408 Cavendish bananas of different ripeness indices. Chemometrics using multiple linear regression (MLR) was applied to develop calibration models for prediction of firmness and soluble solid content (SSC) of Cavendish banana. Results showed that NIR spectroscopy had the feasibility for non-destructive determination of the quality of Cavendish banana. The coefficient of determination (R2) for firmness and SSC calibration models at different ripeness indices ranged from 0.78 to 0.86 and 0.75 to 0.96, respectively. The calibration models were validated using independent sets of data and prediction models developed with the root mean square error of prediction (RMSEP) ranged from 0.01 to 0.26 kgf and 0.039 to 0.788 Brix for firmness and SSC, respectively. The multi-index models showed considerable robustness but higher prediction error with RMSEP of 0.336 kgf for firmness and 0.937% Brix for SSC compared to index specific model.
    Matched MeSH terms: Multivariate Analysis
  14. Fulltext Modeling of annual air temperature in Malaysia using multiple regression model
    Nur Hanim Mohd Salleh, Husna Hasan
    Journal of Engineering and Science Research, 2017;1(2):164-170.
    MyJurnal
    Annual air temperature data obtained from twenty-two meteorological stations across Malaysia are modeled using multiple regression. A correlation test was conducted to find statistical relationship between each of the dependent variables: annual maximum and annual average air temperature and predictor variables: longitude, latitude, elevation and wind speed. Regression models using least square estimation method were developed relating the dependent variables to independent variables and the adequacy of the models is determined by the coefficient of determination. The result shows that the longitude and wind speed factors have a significant influence on the annual air temperature in Malaysia.
    Matched MeSH terms: Multivariate Analysis
  15. Fulltext The impact of luminance contrast on attention and memory
    Yap SL, Tan XB, Tan CS
    Jurnal Psikologi Malaysia, 2017;31:90-98.
    Previous studies have found that luminance contrast may enhance attention and attention is positively correlated with memory. However, little attention has been given to understand the impact of luminance contrast on memory. The present study attempts to address this gap by examining the effect of luminance contrast on attention and memory. A total of 159 undergraduates were randomly assigned to three luminance contrast conditions (high vs. moderate vs. low) and were administered a modified d2 test and modified words memory test. Multivariate analysis of variance showed significant effect of luminance contrast on memory performance. Participants in the high and moderate luminance contrast groups recalled more words than counterparts in the low contrast group. However, the effect of luminance contrast on attention was not significant, though planned comparison found that high contrast group scored higher than low contrast group. The findings not only shed light on improvement of memory but also have implication for design and marketing and consumer behaviours study.
    Matched MeSH terms: Multivariate Analysis
  16. Genetic diversity in modern T. Aman rice varieties of bangladesh (Oryza Sativa L.)
    Biswash MR, Sharmin M, Rahman NMF, Farhat T, Siddique MA
    Sains Malaysiana, 2016;45:706-716.
    A field experiment was conducted from June to December, 2013 to study the genetic diversity of 15 modern T. Aman rice
    varieties of Bangladesh (Oryza sativa L.) with a view to assess the superior genotype in future hybridization program
    for developing new rice varieties that is suitable for the target environment. Analysis of variance for each trait showed
    significant differences among the varieties. High heritability associated with high genetic advance in percent of mean
    was observed for plant height and thousand seed weight which indicated that selection for these characters would be
    effective. Hence, thrust has to be given for these characters in future breeding program to improve the yield trait in rice.
    Multivariate analysis based on 10 agronomic characters indicated that the 15 varieties were grouped into four distant
    clusters. The inter cluster distance was maximum between cluster II and cluster IV. The highest intra-cluster distance was
    found in cluster IV. Based on positive value of vector 1 and vector 2, plant height and 1000-seed weight had maximum
    contribution towards genetic divergence. From the results, it can be concluded that the varieties BRRI dhan40, BRRI
    dhan44, BRRI dhan46, BRRI dhan49 and BINA dhan7 may be selected for future hybridization program.
    Matched MeSH terms: Multivariate Analysis
  17. Fulltext Factors influencing community pharmacists' knowledge about women's issues in epilepsy
    Jairoun AA, Al-Himyari SS, Shahwan M, Hassan N, Al-Tamimi S, Jairoun M, et al.
    Front Public Health, 2023;11:1251393.
    PMID: 37766744 DOI: 10.3389/fpubh.2023.1251393
    BACKGROUND: Previous studies have highlighted instances where pharmacists lacked knowledge regarding women's health issues related to epilepsy.

    OBJECTIVES: To assess UAE community pharmacists' knowledge, toward women's issues in epilepsy.

    METHODS: a cross-sectional research method was employed. A team of seven pharmacy students in their final year visited a randomly selected sample of community pharmacies in the UAE and face-to-face interviews were conducted with the pharmacists using a structured questionnaire. The questionnaire includes two parts; Eight questions designed to elicit data about the demographics of the study participants and 12 questions eliciting insights into the participants' knowledge of women's issues in epilepsy.

    RESULTS: A total of 412 community pharmacist were recruited in the study. The overall level of knowledge about women's issues in epilepsy was good and the average knowledge score was 81% with a 95% confidence interval (CI) [79.1, 82.7%]. The results of multivariate analysis showed higher knowledge scores in chain pharmacies (OR 1.37; 95% CI 1.12-1.67), Chief pharmacists (OR 1.44; 95% CI 1.01-2.06), Pharmacists in charge (OR 3.46; 95% CI 2.7-4.45), pharmacists with 1-5 Years of experience (OR 2.87; 95% CI 1.71-4.82), pharmacists with 6-10 Years (OR 2.63; 95% CI 1.58-4.38), pharmacists with >10 years (OR 3.13; 95% CI 2.03-4.83), graduation form regional universities (OR 1.37; 95% CI 1.12-1.67), graduation form international universities (OR 1.73; 95% CI 1.36-2.20) and receiving a training on epilepsy (OR 1.36; 95% CI 1.12-1.67).

    CONCLUSION: While the findings reveal an overall promising level of knowledge among community pharmacists regarding the issues faced by women with epilepsy, pinpointing which clinical and demographic factors have the most significant impact on this knowledge would permit the implementation of tailored educational interventions. Workshops and modules targeting the issues faced by women with epilepsy would further raise the knowledge and competence among community pharmacists in this area, ensuring better pharmaceutical care for this population.

    Matched MeSH terms: Multivariate Analysis
  18. Fulltext Development and double cross-validation of new spot urine sodium equation to predict 24-h urine sodium in the Malaysian population
    Othman F, Ambak R, Omar MA, Shahar S, Nor NSM, Ahmad MH, et al.
    J Health Popul Nutr, 2021 05 31;40(Suppl 1):10.
    PMID: 34059149 DOI: 10.1186/s41043-021-00232-3
    BACKGROUND: Monitoring sodium intake through 24-h urine collection sample is recommended, but the implementation of this method can be difficult. The objective of this study was to develop and validate an equation using spot urine concentration to predict 24-h sodium excretion in the Malaysian population.

    METHODS: This was a Malaysian Community Salt Study (MyCoSS) sub-study, which was conducted from October 2017 to March 2018. Out of 798 participants in the MyCoSS study who completed 24-h urine collection, 768 of them have collected one-time spot urine the following morning. They were randomly assigned into two groups to form separate spot urine equations. The final spot urine equation was derived from the entire data set after confirming the stability of the equation by double cross-validation in both study groups. Newly derived spot urine equation was developed using the coefficients from the multiple linear regression test. A Bland-Altman plot was used to measure the mean bias and limits of agreement between estimated and measured 24-h urine sodium. The estimation of sodium intake using the new equation was compared with other established equations, namely Tanaka and INTERSALT.

    RESULTS: The new equation showed the least mean bias between measured and predicted sodium, - 0.35 (- 72.26, 71.56) mg/day compared to Tanaka, 629.83 (532.19, 727.47) mg/day and INTERSALT, and 360.82 (284.34, 437.29) mg/day. Predicted sodium measured from the new equation showed greater correlation with measured sodium (r = 0.50) compared to Tanaka (r =0.24) and INTERSALT (r = 0.44), P < 0.05.

    CONCLUSION: Our newly developed equation from spot urine can predict least mean bias of sodium intake among the Malaysian population when 24-h urine sodium collection is not feasible.

    Matched MeSH terms: Multivariate Analysis
  19. An automated approach for analysis of Fourier Transform Infrared (FTIR) spectra of edible oils
    Sim SF, Ting W
    Talanta, 2012 Jan 15;88:537-43.
    PMID: 22265538 DOI: 10.1016/j.talanta.2011.11.030
    This paper reports a computational approach for analysis of FTIR spectra where peaks are detected, assigned and matched across samples to produce a peak table with rows corresponding to samples and columns to variables. The algorithm is applied on a dataset of 103 spectra of a broad range of edible oils for exploratory analysis and variable selection using Self Organising Maps (SOMs) and t-statistics, respectively. Analysis on the resultant peak table allows the underlying patterns and the discriminatory variables to be revealed. The algorithm is user-friendly; it involves a minimal number of tunable parameters and would be useful for analysis of a large and complicated FTIR dataset.
    Matched MeSH terms: Multivariate Analysis
  20. 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: Multivariate Analysis
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