scholarly journals Application of principal component analysis in assessment of relation between the parameters of technological quality of wheat grains treated with inert dusts against rice weevil (Sitophilus oryzae L.)

2011 ◽  
Vol 26 (4) ◽  
pp. 385-390 ◽  
Author(s):  
Marija Bodroza-Solarov ◽  
Petar Kljajic ◽  
Goran Andric ◽  
Bojana Filipcev ◽  
Olivera Simurina ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Sitophilus Oryzae ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Parameters ◽  
Two Dimensions ◽  
Data Set ◽  
Inert Dusts ◽  
Total Variability ◽  
Technological Quality

Quality parameters of several wheat grain lots (low vitreous and high vitreous grains, non-infested and infested with rice weevils, (Sitophilus oryzae L.) treated with inert dusts (natural zeolite, two diatomaceous earths originating from Serbia and a commercial product Protect-It?) were investigated. Principal component analysis (PCA) was used to investigate the classification of treated grain lots and to assess how attributes of technological quality contribute to this classification. This research showed that vitreousness (0.95) and test weight (0.93) contributed most to the first principal component whereas extensigraph area (-0.76) contributed to the second component. The determined accountability of the total variability by the first component was around 55%, while with the second it was 18%, which means that those two dimensions together account for around 70% of total variability of the observed set of variables. Principal component analysis (PCA) of data set was able to distinguish among the various treatments of wheat lots. It was revealed that inert dust treatments produce different effects depending on the degree of endosperm vitreousness.

scholarly journals Determination of risk factors using Nonlinear Principal Component Analysis in patients with breast tumour

2021 ◽  
Vol 8 (7) ◽  
pp. 432-436
Author(s):  
Canan Demir
Keyword(s):  
Risk Factors ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensions ◽  
Malignant Tumours ◽  
Application Process ◽  
Data Set ◽  
Breast Tumours ◽  
Nonlinear Principal Component Analysis

Objective: Breast cancer, which is the most common among women in the world and constitutes approximately 30% of all cancers, takes places near the top among the diseases that threaten women's health. The purpose of this study is to determine the risk factors in patients with breast tumours using nonlinear principal component analysis. Materials and Methods: During the application process, a data set of 569 (357 benign, 212 malign) patients with breast tumours was used. To find independent features, the data set was reduced to two dimensions via nonlinear principal component analysis. The results were evaluated by comparing the success of the method with the ROC curve. Results: The cut-off values for the radius, perimeter, area, smoothness and texture of the tumour were 14.19, 656.10, 0.09, 2.87 and 0.11, respectively. The sensitivity of the current values according to the results of ROC analysis was determined as 84% for radius, 80% for perimeter, 86% for the area and 94% for texture. It is seen that the method has an overall success of over 80% in detecting malignant tumours. Conclusions: It is hoped that this method, which is used to reveal risk factors and identify distinctive features in breast tumours, will reduce medical costs and provide a second opinion to physicians. In terms of decision making, it is predicted that the method can recognize malignant tumours and reduce the need for unnecessary biopsy for benign tumours.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

scholarly journals Assessment of Physicochemical Properties of Tequila Brands: Authentication and Quality

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Alejandra Carreon-Alvarez ◽  
Amaury Suárez-Gómez ◽  
Florentina Zurita ◽  
Sergio Gómez-Salazar ◽  
J. Felix Armando Soltero ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Refractive Index ◽  
Physicochemical Properties ◽  
Analysis Of Variance ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variability ◽  
And Cluster Analysis ◽  
Physicochemical Data

Several physicochemical properties were measured in commercial tequila brands: conductivity, density, pH, sound velocity, viscosity, and refractive index. Physicochemical data were analyzed by Principal Component Analysis (PCA), cluster analysis, and the one-way analysis of variance to identify the quality and authenticity of tequila brands. According to the Principal Component Analysis, the existence of 3 main components was identified, explaining the 87.76% of the total variability of physicochemical measurements. In general, all tequila brands appeared together in the plane of the first two principal components. In the cluster analysis, four groups showing similar characteristics were identified. In particular, one of the clusters contains some tequila brands that are not identified by the Regulatory Council of Tequila and do not meet the quality requirements established in the Mexican Official Standard 006. These tequila brands are characterized by having higher conductivity and density and lower viscosity and refractive index, determined by one-way analysis of variance. Therefore, these economical measurements, PCA, and cluster analysis can be used to determinate the authenticity of a tequila brand.

Comparisons of Environmental Ordinations with Principal Component Vegetational Ordinations for Sets of Data Having Different Degrees of Complexity

1971 ◽  
Vol 1 (2) ◽  
pp. 99-112 ◽  
Author(s):  
J. K. Jeglum ◽  
C. F. Wehrhahn ◽  
J. M. A. Swan
Keyword(s):  
Principal Component Analysis ◽  
Water Level ◽  
Principal Component ◽  
Low Complexity ◽  
Component Analysis ◽  
Two Dimensional ◽  
High Complexity ◽  
Water Conductivity ◽  
Data Set ◽  
Total Data

Data from a survey of lowland, mainly peatland, vegetation were subjected to environmental ordination based on measurements of water level and water conductivity, and to vegetational ordination derived from principal component analysis (P.C.A.). Analyzed were the total set of the data ("all types"), half sets ("nonwoody" and "woody types") and quarter sets (stands of "marshes", "meadows", "shrub fens", and "other woody types"); the number of distinct physiognomic groups in a set of data, and presumably the amount of contained heterogeneity, decreased at each segmentation.The effectiveness of the ordination models was tested by correlating measured distances in two-dimensional ordination models with 2W/(A + B) indices of vegetational similarity for randomly selected pairs of types or stands. As the physiognomic complexity decreased, the effectiveness of the P.C.A. vegetational ordination increased whereas that of the environmental ordination decreased. The environmental ordination seemed most appropriate to the data encompassing high complexity (total data set), while the P.C.A. vegetational ordination seemed most appropriate to data with low complexity (quarter sets of the data).

Characteristics and discrimination of five types of wood-plastic composites by FTIR spectroscopy combined with principal component analysis

Holzforschung ◽  
2010 ◽  
Vol 64 (6) ◽  
Author(s):  
Chia-Huang Lee ◽  
Tung-Lin Wu ◽  
Yong-Long Chen ◽  
Jyh-Horng Wu
Keyword(s):  
Principal Component Analysis ◽  
Ftir Spectroscopy ◽  
Principal Component ◽  
Core Layer ◽  
Component Analysis ◽  
Attenuated Total Reflection ◽  
Distribution Profile ◽  
Data Set ◽  
Wood Plastic Composites ◽  
Plastic Composites

Abstract The analytical potential of attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy has been tested on the following wood-plastic composites (WPCs): high and low density polyethylene (HDPE and LDPE), polypropylene (PP), polystyrene (PS), and a recycled plastic (rHDPE). The data set of ATR-FTIR spectra has been analyzed by principal component analysis (PCA) and the studied samples could be grouped according to their polymeric matrixes. Additionally, ATR-FTIR spectroscopy proved to be a useful tool for determining the distribution profile of wood and plastic materials within different types of WPCs. Accordingly, the plastic content of the surface layers of HDPE, rHDPE, and PP composites was significantly higher than that of the core layer, whereas homogenous dispersion was observed in the LDPE composite. Among all WPCs, the PS composite displayed the worst dispersion.

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