Proximate Analysis and Nutritional Evaluation of Twenty Canadian Lentils by Principal Component and Cluster Analyses

Proximate composition and starch nutritional properties of twenty cooked lentils were assessed to identify unique varieties that could be used in value added foods. Significant variations exist among the lentil varieties (p < 0.05) with respect to their energy, fat, protein, carbohydrate, and dietary fiber content, and these are related to lentil type and seed size. Dazil and Greenstar were unique for their high resistant starch content (RS) and lower area under the starch hydrolysis curve (SHAUC) while Proclaim was opposite. SHAUC was positively correlated (p < 0.001) with rapidly digestible starch (RDS) content (r = 0.626) but negatively correlated with RS content (r = −0.635). Principal component analysis showed that the first three principal components accounted for 62.8% of the total variance and the contribution of SHAUC was 33.2%. These results confirm that in vitro SHAUC and a combination of RDS and RS may be predictive of the digestibility profile of cooked lentils.

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PREPARATION AND CHARACTERISATION OF ACETYLATED WHEAT STARCH SUPPORTING FOR DIABETES TREATMENT

Journal of Medicine and Pharmacy ◽

10.34071/jmp.2018.5.11 ◽

2018 ◽

Vol 8 (5) ◽

pp. 78-84

Author(s):

Uyen Tran Thi Ngoc ◽

Nam Nguyen Khac ◽

Dung Tran Huu

Keyword(s):

Physicochemical Properties ◽

Resistant Starch ◽

Starch Content ◽

Degree Of Substitution ◽

Wheat Starch ◽

Diabetes Treatment ◽

Glucose Response ◽

Swelling Capacity ◽

Wheat Starches

Background: The purpose of the study was to prepare acetylated wheat starches which have amylase hydrolysis resistant capacity to use as functional food supporting for diabetes treatment. Method: Acetate wheat starches were prepared by acetylation reaction of native wheat starch with different mole ratios of acetic anhydride. These starches were determined for the physicochemical properties by 1H-NMR, SEM, X-ray, DSC, solubility and swelling capacity, the resistant capacity by amylase hydrolysis in-vitro. Results: Acetate wheat starches were prepared successfully with the increase in acetyl content and degree of substitution corresponding with the increase of anhydride acetic, which resulted in the change of physicochemical properties of the wheat starches, including constitution, solubility, swelling capacity and contributed to the increase in resistant starch content in the acetate wheat starches. The AC150-9 containing 2.42% acetyl with degree of substitution 0,094 and resistant starch 32,11% is acceptable by FDA guideline about food safety. Conclusion: Acetate wheat starches contain low rate of digestive starch, while containing a higher proportion of resistant starch than natural wheat starch, possessing a high resistance to amylase activities. Thus, it is hope that this kind of starch to control the rapid increase of postprandual blood glucose response for diabetes treatments effectively. Key words: Acetate wheat starch, substitution, DS, RS, amylase

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Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

Water Practice & Technology ◽

10.2166/wpt.2006.017 ◽

2006 ◽

Vol 1 (1) ◽

Author(s):

K. Katayama ◽

K. Kimijima ◽

O. Yamanaka ◽

A. Nagaiwa ◽

Y. Ono

Keyword(s):

Principal Component Analysis ◽

Prediction Model ◽

Principal Components ◽

Prediction Method ◽

Principal Component ◽

Component Analysis ◽

Rainfall Data ◽

Radar Rainfall ◽

Input Variables ◽

Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

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Evaluation functions for integral mapping

Geodesy and Cartography ◽

10.22389/0016-7126-2017-921-3-24-29 ◽

2017 ◽

Vol 921 (3) ◽

pp. 24-29 ◽

Cited By ~ 2

Author(s):

S.I. Lesnykh ◽

A.K. Cherkashin

Keyword(s):

Principal Component Analysis ◽

Environmental Factors ◽

Principal Components ◽

Principal Component ◽

Evaluation Function ◽

Final Value ◽

Evaluation Functions ◽

Geographical Environment ◽

Environmental Background ◽

Integral Mapping

The proposed procedure of integral mapping is based on calculation of evaluation functions on the integral indicators (II) taking into account the feature of the local geographical environment, when geosystems in the same states in the different environs have various estimates. Calculation of II is realized with application of a Principal Component Analysis for processing of the forest database, allowing to consider in II the weight of each indicator (attribute). The final value of II is equal to a difference of the first (condition of geosystem) and the second (condition of environmental background) principal components. The evaluation functions are calculated on this value for various problems of integral mapping. The environmental factors of variability is excluded from final value of II, therefore there is an opportunity to find the invariant evaluation function and to determine coefficients of this function. Concepts and functions of the theory of reliability for making the evaluation maps of the hazard of functioning and stability of geosystems are used.

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The Design of Index about Corporate Governance Based on PCA Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.4085 ◽

2014 ◽

Vol 926-930 ◽

pp. 4085-4088

Author(s):

Chuan Jun Li

Keyword(s):

Principal Component Analysis ◽

Corporate Governance ◽

Principal Components ◽

Principal Component ◽

Component Analysis ◽

Contribution Rate ◽

Variance Contribution ◽

Pca Method

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.

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Identification of Liquor Brands Based on near Infrared Spectroscopy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.834-836.935 ◽

2013 ◽

Vol 834-836 ◽

pp. 935-938

Author(s):

Lian Shun Zhang ◽

Chao Guo ◽

Bao Quan Wang

Keyword(s):

Principal Component Analysis ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Principal Components ◽

Near Infrared ◽

Scatter Correction ◽

Principal Component ◽

Component Analysis ◽

Correction Method ◽

Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

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Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2015000800003 ◽

2015 ◽

Vol 50 (8) ◽

pp. 649-657 ◽

Cited By ~ 2

Author(s):

Regina Maria Villas Bôas de Campos Leite ◽

Maria Cristina Neves de Oliveira

Keyword(s):

Principal Component Analysis ◽

Disease Severity ◽

Principal Components ◽

Leaf Spot ◽

Oil Content ◽

Principal Component ◽

Component Analysis ◽

High Yield ◽

Gge Biplot ◽

Alternaria Leaf Spot

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.

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Principal component analysis for evaluating a ranking method used in the performance testing in sheep of Morada Nova breed

Semina Ciências Agrárias ◽

10.5433/1679-0359.2015v36n6p3909 ◽

2015 ◽

Vol 36 (6) ◽

pp. 3909

Author(s):

Michelle Santos da Silva ◽

Luciana Shiotsuki ◽

Raimundo Nonato Braga Lôbo ◽

Olivardo Facó

Keyword(s):

Principal Component Analysis ◽

Principal Components ◽

Correlation Coefficients ◽

Performance Testing ◽

Principal Component ◽

Component Analysis ◽

Ranking Method ◽

Daily Weight Gain ◽

Meat Production ◽

Body Depth

A multivariate approach was adopted to evaluate the relationship among traits measured in the performance testing of Morada Nova sheep, verify the efficiency of a ranking method used in these tests and identify the most significant traits for use in future analyses. Data from 150 young rams participating in five versions of the performance tests for the Morada Nova breed were used. Twenty traits were measured in each animal: initial weight (IW), final weight (FW), average daily weight gain (ADG), loin eye area (LEA), scrotal circumference (SC), fat thickness (FT), conformation (C), precocity (Pc), muscularity (M), breed features (BF), legs (L), withers height (WH), chest width (CW), rump height (RH), rump width (RW), rump length (RL), body length (BL), body depth (BD), heart girth (HG) and body condition scoring (BCS). The Pearson’s correlation coefficients ranged from –0.10 to 0.93, with the highest correlations were between body weight variables and morphometric measurements. The three first principal components explained 72.28% of the total variability among all traits. The variables related to animal size defined the first principal component, whereas those related to visual appraisal and suitability for meat production defined the second and third principal components, respectively. The combination of traits from the principal component analysis showed that the ranking method currently used in the performance testing of Morada Nova sheep is efficient for selecting larger rams with better breed features and higher degrees of specialization for meat production.

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Worldwide dietary patterns and their association with socioeconomic data: an ecological exploratory study

10.31232/osf.io/9x3gf ◽

2020 ◽

Author(s):

Gabriel Gonçalves da Costa ◽

Bruno Francisco Teixeira Simões ◽

Alessandra da Silva Pereira

Keyword(s):

Principal Component Analysis ◽

Trade Liberalization ◽

Food Availability ◽

Principal Components ◽

Dietary Patterns ◽

Dietary Pattern ◽

Principal Component ◽

Animal Origin ◽

Processed Foods ◽

Socioeconomic Data

Objective: To identify dietary patterns in food availability data at the global level using multivariate statistical methodology, to associate the identified dietary patterns with socioeconomic data and to analyze the adequacy of the applied statistical methods in data of food availability. Methods: Principal Component Analysis was applied to food availability data of 172 UN registered countries available at FAOSTAT database in Food Balance Sheets section. The Principal Components were correlated with socioeconomic data available from the World Bank database. Results: Five principal components were identified, each characterizing a dietary pattern. The first one, a westernized dietary pattern, was composed of energy-dense and processed foods, foods of animal origin, alcoholic beverages, but also, albeit less, by vegetables, fruits and nuts, being correlated with income, urbanization and trade liberalization. This westernized pattern was characterized more by western, animal origin and processed foods, yet preserving unprocessed and regional foods. The other dietary patterns were three agricultural patterns characterized more by regional foods, especially starchy staples, and one coastal dietary pattern composed of fish and seafoods, being associated with GINI index, poverty, and female labor force. Conclusions: Principal Component Analysis was adequate to identify dietary patterns in food availability data. A westernized dietary pattern was identified, being associated with income, urbanization and trade liberalization. This association did not occur for the remain of the dietary patterns identified, these being less driven by economic development.

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Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

10.5772/intechopen.101860 ◽

2022 ◽

Author(s):

Jaime González Maiz Jiménez ◽

Adán Reyes Santiago

Keyword(s):

Principal Component Analysis ◽

Stock Market ◽

Principal Components Analysis ◽

Principal Components ◽

Systematic Risk ◽

Principal Component ◽

Component Analysis ◽

Market Capitalization ◽

The Us ◽

Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

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Competitive and Recreational Running Kinematics Examined Using Principal Components Analysis

Healthcare ◽

10.3390/healthcare9101321 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1321

Author(s):

Wenjing Quan ◽

Huiyu Zhou ◽

Datao Xu ◽

Shudong Li ◽

Julien S. Baker ◽

...

Keyword(s):

Principal Component Analysis ◽

Principal Components ◽

Sagittal Plane ◽

Three Dimensional ◽

Frontal Plane ◽

Principal Component ◽

Component Analysis ◽

Ankle Inversion ◽

Motion System ◽

Recreational Runners

Kinematics data are primary biomechanical parameters. A principal component analysis (PCA) of waveforms is a statistical approach used to explore patterns of variability in biomechanical curve datasets. Differences in experienced and recreational runners’ kinematic variables are still unclear. The purpose of the present study was to compare any differences in kinematics parameters for competitive runners and recreational runners using principal component analysis in the sagittal plane, frontal plane and transverse plane. Forty male runners were divided into two groups: twenty competitive runners and twenty recreational runners. A Vicon Motion System (Vicon Metrics Ltd., Oxford, UK) captured three-dimensional kinematics data during running at 3.3 m/s. The principal component analysis was used to determine the dominating variation in this model. Then, the principal component scores retained the first three principal components and were analyzed using independent t-tests. The recreational runners were found to have a smaller dorsiflexion angle, initial dorsiflexion contact angle, ankle inversion, knee adduction, range motion in the frontal knee plane and hip frontal plane. The running kinematics data were influenced by running experience. The findings from the study provide a better understanding of the kinematics variables for competitive and recreational runners. Thus, these findings might have implications for reducing running injury and improving running performance.

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