An Empirical Analysis of Nussbaum’s Central Capabilities: Insights from a Survey Using Principal Component and Cluster Analysis

This research aims to obtain information about the qualitative and quantitative characteristics of the genetic diversity of 20 chili genotypes and to analyze their similarity as the basis for selecting parental crossbreeds in peatland assembly chili varieties. This research was conducted in the peatlands of Tuah Karya Village, Tampan District, Pekanbaru City, Riau Province and analyzed at the Plant Breeding Laboratory of the Faculty of Agriculture, Riau University from November 2020-April 2021. The study used a randomized block design consisting of 20 genotypes (Agronomy and Horticulture IPB University’s collection) with three replications, so there were 60 experimental units. There are 22 qualitative characters and 13 quantitative characters identified. Qualitative data was transformed into quantitative data based on International Plant Genetic Resource Institute (IPGRI) and quantitative data was the average data. Principal component and cluster analyses were conducted using SPSS Statistics 20 software. Results show that large chili plants based on principal component analysis and cluster analysis were grouped into four clusters at level of similarity 82.5% and a proportion of diversity of 57.53%. The closest similarity of large chili is CG4 and CG6. Based on the principal component analysis and cluster analysis cayenne pepper plants are grouped into two clusters at a 90% similarity level and 72.80% diversity proportion. The closest similarity of cayenne pepper is CG16 and CG19.

Statistical methods like principal component analysis and cluster analysis are not new in identification and classification for biological features. However, the success of utilizing these two methods in discriminating late blight infected tomatoes (caused by Phytophthora infestans ) from healthy ones has not yet been reported. This paper demonstrates the capability of using principal component analysis and cluster analysis for identification and discrimination of spectral characteristics of late blight infections on tomatoes. Our results show that the first principal component is related to the spectral properties of healthy tomatoes, and the second principal component is related to the spectral properties of infected tomatoes. Cluster analysis shows that a reasonable discrimination is obtained when the centroid distance of clusters is above 0.5. The consistent results from both principal components analysis and cluster analysis indicate that late blight infection on tomatoes can be successfully detected with remote sensing when the infection severity reaches middle to late stages. Moreover, spectral ratio analysis provides us with the way to identify the sensitive spectral wavelengths where distinguishable reflectance values can be observed for unique biological features. Understanding the light responses to unique biological features may increase discrimination accuracy by reducing the impact of soil background on spectral measurements, and utilizing the most sensitive wavelengths for discriminating between healthy and diseased tomatoes.

The grouping of soils with similar properties is one of the primary objectives of soil surveyors. We compared the groupings of 89 soil sites as classified by the U.S. system of soil taxonomy and by principal component and cluster analysis. The sample sites, examined at four depth intervals, were equally spaced on a 2,700‐m transect in southern New Mexico. Ten laboratory‐determined variables: clay, calcium carbonate, soil pH, coarse fragments, organic carbon (OC) and five sand fractions, were used to characterize each sample. The principal component analysis produced two components that accounted for 60, 53, 60, and 60% of total variation for the 0 to 30, 30 to 60, 60 to 90, and 90 to 120 cm depths, respectively. It was found that clay and sand contents were the major contributors to the first two principal components. Four cluster sorting strategies were used: (i) centroid, (ii) median, (iii) group average, and (iv) flexible. These sorting strategies all gave the same results by grouping the soil sites into two groups. We identified eight soil mapping units along the transect (one Vertisol, one Mollisol, and six Aridisols) using the traditional field methods of the Soil Survey Staff. Both the cluster analysis and the principal component analysis identified only two groups. The numerical methods separated the soils classified as Vertisols as one group, but grouped the soil classified as Mollisols and Aridisols together. Our sampling scheme was not designed to recognize the diagnostic horizons and other properties used as differentiating characteristics in the U.S. system of soil taxonomy. The results of numerical classification depend more on the strategy for data analysis and variables selected for analysis than on the particular numerical method.

BackgroundModern instrumental analysis technology can provide various chemical data and information on tea samples. Unfortunately, it remains difficult to extract the useful information. We describe the use of chemical fingerprint similarities, combined with principal component and cluster analyses, to distinguish and recognize Pu-erh green teas, which from two tea mountains, Wuliang and Jingmai, in the Pu-erh district of Yunnan province. The volatile components of all 20 Pu-erh green teas (10 Wuliang and 10 Jingmai teas) were extracted and identified by headspace solid-phase micro extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS).ResultsSixty-three volatiles (including alcohols, hydrocarbons, ketones, and aldehydes) were identified in the 20 Pu-erh green teas, and differences in compound compositions between them were also observed. Through fingerprint similarity, combined with principal component and cluster analyses, the 20 Pu-erh green teas were differentiated successfully based on their volatile characteristics.ConclusionsThis study demonstrates that the GC-MS combined with chemical fingerprint and unsupervised pattern recognition method is suitable for the investigation of the volatile profiling and evaluating the quality and authenticity of teas related to the different origins.Graphical abstractDifferentiate Pu-erh green teas from different tea mountains by using chemical fingerprint similarity and multivariate statistical methodsElectronic supplementary materialThe online version of this article (doi:10.1186/s13065-016-0159-y) contains supplementary material, which is available to authorized users.

OBJECTIVESThis study was conducted to establish profiles of socioeconomic characteristics, dietary intake, and health status among Korean older adults by employing 3 multivariate analysis techniques.METHODSData were obtained from 1,352 adults aged 65 years and older who participated in the 2019 Korea National Health and Nutrition Examination Survey. Principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA) were utilized for profiling, with data preprocessing undertaken to facilitate these approaches.RESULTSPCA, FA, and CA yielded similar results, reflecting the high common variance among the variables. PCA identified 4 components, accounting for 71.6% of the accumulated variance. FA revealed 5 factors, displaying a Kaiser-Meyer-Olkin value of 0.51 and explaining 74.3% of the total variance. Finally, CA grouped the participants into 4 clusters (R2=0.465). Both PCA and FA identified dietary intake (energy, protein, carbohydrate, etc.), social support from family (incorporating family structure, number of family numbers, and engagement in social eating), and health status (encompassing oral, physical, and subjective health) as key factors. CA classified Korean older adults into 4 distinct typologies, with significant differences observed in dietary intake, health status, and household income (p<0.01).CONCLUSIONSThe study utilized PCA, FA, and CA to analyze profiling domains and derive characteristics of older adults in Korea, followed by a comparison of the results. The variables defining the clusters in CA were consistent with those identified by PCA and FA.

Multivariate analysis can help to select superior genotypes through the simultaneous analysis of original information containing the characters of interest. The present study objectives were to select soybean genotypes for good agronomic attributes with focus on yield from amonggenotypes carrying genes resistantto Asian soybean rustusing multivariate analyses and as well as to define the main plant characters which influence the selection decision. Ninety five soybean genotypes of the F6 endogamous generation were evaluated in a random block experiment with two replications for agronomic characters. The data were submitted to principal component and cluster analyses, besides the use of selection index. In the principal components analysis, four eigenvalues explained 71.6% of the variance in the original information, allowing the identification of seventeen superior genotypes, focusing on yield. In the dendrogram obtained from cluster analysis, the genotypes selected in the principal components were grouped in the same cluster, and a second selection was made in which seven genotypes were selected. Concerning the selection by index, most superior genotypes were coincident with the results of the multivariate analysis. In conclusion, multivariate analyses permitted the selection of superior genotypes for important agronomic characteristics in soybeans, principally for components linked to grain production. Key words: Glycine max, yield, principal components, cluster analysis.

The spatial variability of soil and plant properties exerts great influence on the yeld of agricultural crops. This study analyzed the spatial variability of the fertility of a Humic Rhodic Hapludox with Arabic coffee, using principal component analysis, cluster analysis and geostatistics in combination. The experiment was carried out in an area under Coffea arabica L., variety Catucai 20/15 - 479. The soil was sampled at a depth 0.20 m, at 50 points of a sampling grid. The following chemical properties were determined: P, K+, Ca2+, Mg2+, Na+, S, Al3+, pH, H + Al, SB, t, T, V, m, OM, Na saturation index (SSI), remaining phosphorus (P-rem), and micronutrients (Zn, Fe, Mn, Cu and B). The data were analyzed with descriptive statistics, followed by principal component and cluster analyses. Geostatistics were used to check and quantify the degree of spatial dependence of properties, represented by principal components. The principal component analysis allowed a dimensional reduction of the problem, providing interpretable components, with little information loss. Despite the characteristic information loss of principal component analysis, the combination of this technique with geostatistical analysis was efficient for the quantification and determination of the structure of spatial dependence of soil fertility. In general, the availability of soil mineral nutrients was low and the levels of acidity and exchangeable Al were high.

This study integrated conventional statistical tools with neighbourhood linkage to propose the statistical diagnosis approach. Fourteen monitoring wells in Kaohsiung Science Park were selected as study case, and lab data of routine groundwater analysis including pH, EC, hardness, TDS, TOC, ammonia, nitrate, nitrite, chloride, sulphate, fluoride, phenols, Fe, Mn, As, and temperature were subjected to principal component and cluster analysis. Principal component analysis (PCA) was utilized to reflect those chemical data with the greatest correlation, and PCA results identified five major principal components (PCs) representing 74.6% of cumulative variance. Based on the monitoring data between 2005 and 2008, the extracted information from the PCA mirrored the potential sources of groundwater contamination as acid leakage, arsenic dissolution, salinization, mineralization, and fluoride release. Cluster analysis (CA) was used to evaluate the similarities of water quality in groundwater samples, and five clusters were assigned in two-step clustering for corresponding with the number of PCs, i.e. the potential sources of groundwater contamination. The interpreted facts from CA illustrated that the classified monitoring wells in each cluster properly match up with the identified processes. With the aid of neighbourhood linkage, the domain of groundwater contamination can be spatially outlined by mapping the neighbouring wells within the identical cluster. Therefore, the nature of underlying processes affecting groundwater quality was explored by statistical diagnosis.

Principal component and cluster analysis of functional parameters in rheumatic patients: Identifying the most efficient assessment tool.

Asphalt ages easily under the natural environment of light, heat, and oxygen. A good amount of pavement damage, such as cracks, chaps, and trenches, is caused by asphalt aging, which shortens the service life of asphalt pavement. The aging rules of asphalt differ from oil source to oil source. Here, 18 typical asphalt samples were subjected to short-term aging experiments using a thin-film oven test. The principal component analysis (PCA) and cluster analysis were carried out on eight aging-resistance indexes. The results classified the 18 types of asphalts into 2 groups according to the oil source, which indicated that the aging resistance of asphalt primarily depends on its oil source. Furthermore, based on the PCA and cluster analysis results, a discriminant model of different aging performance asphalts with a very high accuracy rate was established. These results provide a scientific basis for reasonable selection, quality monitoring, and guarantee of the origin of asphalts.

This study presents a methodology using multivariate analysis: Principal Component Analysis (PCA) and Cluster Analysis (CA) to analyze data of hourly averaged speed in hours from 28 stations distributed in four states of Northeastern Brazil: Ceará with 10 stations, Paraíba with 5 stations, Pernambuco with 8 stations and Rio Grande do Norte with 5 stations. All stations are well distributed spatially and period of data between 1977 to 1981. The results of the Principal Component Analysis (PCA) showed that the coastal and mountainous regions have the greatest potential for energy generation results, in particularly at the stations of Acaraú-CE and Macaú-RN, while Barbalha-CE had the lowest potential, possibly due to its location. The Cluster Analysis (CA), using the Ward method, allowed the distribution of the stations into six homogeneous groups.

Near infrared spectroscopy discrimination and pattern recognition of Chinese Pogostemon cablin and Agastache rugosa

Needs for and Barriers to Correctional Mental Health Services: Inmate Perceptions

Spine grape ( Vitis davidii Foëx), an important wild grape species in South China, has gained attention because of its health-promoting effects and use in the wine industry. Fruit quality plays an important role in determining the quality of wine; however, a suitable evaluation system to monitor its fruit quality has not been established. The fruit quality characteristics (phenolics and aromas) of 15 spine grapes grown in China were evaluated using a combination of principal component and cluster analyses. The total sugar, organic acid, and phenolic content ranged from 81.80 to 154.89 mg·g −1 , 8.02 to 15.48 mg·g −1 , and 5.58 to 20.12 mg·g −1 , respectively. The comprehensive assessment by principal component analysis revealed that ‘Red xiangzhenzhu’ had the highest quality and ‘Hongjiangci10’ and ‘Ziluolan’ the lowest quality. Cluster analysis using k-means grouped the cultivars into three clusters based on their quality: Cluster 1 grouped those with inferior quality (‘Hongjiangci09’, ‘Hongjiangci10’, ‘Hongjiangci11’, and ‘Hongjiangci07’, etc.), Cluster2 grouped those with average quality (‘Ciputao3#,’ ‘Ziluolan’, and ‘Xiangci4#’), and Cluster3 grouped those with superior quality (‘Red xiangzhenzhu’ and ‘Green xiangzhenzhu’). A combination of principal component analysis and cluster analysis provides a comprehensive and objective evaluation system for determining the quality of grape cultivars. This study is important for the systematic evaluation and utilization of spine grape resources.

In this paper, twelve indicators related to green innovation capability of China's manufacturing enterprises are selected and green innovation capability of twenty-five industries in China's manufacturing industry in 2014 is evaluated and sorted by principal component analysis method. Then the green innovation ability of the industry and enterprise is divided into three categories according to the similarity by using cluster analysis. Finally, the results of principal component analysis and cluster analysis are compared and analyzed. The research shows: The transportation equipment manufacturing industry and electrical machinery and equipment manufacturing industry belong to the high green innovation ccapability of the industry as the first category; chemical fiber manufacturing, general equipment manufacturing industry, ferrous metal smelting and rolling processing industry, equipment manufacturing, non-ferrous metal smelting and rolling processing industry and the agricultural food processing industry belong to middle green innovation capability of the industry as the second categories; the rest of the industry belong to the low green innovation capability of the industry as the third categories.