Pattern Recognition of Melaleuca cajuputi Powell Essential Oils Fingerprint in Terengganu, Malaysia Using Chemometric  Approach

This study attempts to determine youth profile in sports talent identification program. Data of anthropometric and physical fitness included power, agility, speeds, flexibility, strength and endurance were obtained from 600 participants in a sports talent identification program aged 13-15 years. Data analyses were carried out using multivariate analysis of principal component analysis (PCA). PCA revealed three principal components with 71.5% total variation for this studied group. The first component consists of high factor loading in speed variables (10 meter run, 20 meter run, 40 meter run) and endurance (predicted VO₂ max). The second component was constituted by anthropometric variables (weight, height, sitting height and armspan). While third component contains flexibility variable (sit and reach). These selected variables of anthropometric and fitness are, therefore, revealed as the essential attributes those must be prioritized for a talent scouting in sports. Present results had demonstrated youth profile that capable of providing an information that could help coaches in decision making during athlete selection in sports talent identification.

Section: Discussionmentioning

confidence: 99%

Determining Youth Profile using Principle Component Analysis for Identifying Talent in Sports

2019

“…This method examines the distance between each point and its nearest point [14]. The nearest neighboring method is the simplest method, where the endpoint of the gap is used as an estimate of all missing values [15,16].…”

Section: Discussionmentioning

confidence: 99%

Applied Multidimensional Analysis for Assessing Youth Performance in Sports Talent Identification Program

2019

This study attempts to apply multidimensional analysis for assessing the profile of male youth in sports talent identification program. Data of anthropometric and physical fitness included power, agility, speeds, flexibility, strength and endurance were obtained from 600 youth in a sports talent identification program aged 13-15 years. Data analyses were carried out using multivariate analysis cluster analysis (CA) and discriminant analysis (DA). Cluster analysis assigned three groups with different profile. While standard mode of DA demonstrated 90.0% accuracy of classification matrix for the assigned groups with nine discriminated variables. Forward and backward stepwise DA discriminated six variables from nine variables with 90.3% level of accuracy. The variables are weight, sitting height, armspan, 20 meter run, 40 meter run and VO2 max. These selected variables of anthropometric and fitness are, therefore, revealed as the essential attributes those must be prioritized for a talent scouting in sports. Present results had demonstrated multidimensional analysis as comprehensive approach capable of providing an information that could help coaches in decision making during youth selection in sports talent identification

“…This method examines the distance between each point and the closest point to it. The nearest neighbor method is the simplest method to be applied, where the end points of the gaps are used as estimation of all missing values [9,10].…”

Section: Discussionmentioning

confidence: 99%

The Development of Malaysian Body Somatotype Model using Comprehensive Multivariate Techniques

2019

This study was designed to develop Malaysian body somatotype model among U21 athletes using multivariate analysis. A total of 85 athletes (aged 20.5 ± 5.5 year) were participated in the assessment of anthropometrics measurements. They were from three types of sports which are weightlifter, futsal and Sepak Takraw. These selected sports were represented three categories of body somatotypes namely endomorph, mesomorph and ectomorph respectively. Discriminant Analysis (DA) was used to determine the model and validate the accuracy of these groups classification. As the result, DA obtained 98.8% variability with two factors. First factor (F1) revealed 85.9% discrimination while second factor (F2) revealed 12.9% discrimination as it is related to anthropometrics and the variation of three groups significantly. A body somatotype model was generated using the standardized coefficient of F1. Standard DA showed three discriminated variables (height, sitting height and weight) with 67.06% percentage of correctness. While forward and backward stepwise mode assigned two discriminated variables (weight and height) with 69.41% percentage of correctness. They were established as essential indicators for differentiating body physical category.