scholarly journals Evaluation of Physical Characteristics of Typical Maize Seeds in a Cold Area of North China Based on Principal Component Analysis

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1167
Author(s):  
Han Tang ◽  
Changsu Xu ◽  
Yeming Jiang ◽  
Jinwu Wang ◽  
Zhenhua Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Physical Properties ◽  
Food Processing ◽  
North China ◽  
Principal Component ◽  
Component Analysis ◽  
Physical Characteristics ◽  
Maize Seeds ◽  
Cold Area ◽  
Agricultural Materials

The physical properties of maize seeds are closely related to food processing and production. To study and evaluate the characteristics of maize seeds, typical maize seeds in a cold region of North China were used as test varieties. A variety of agricultural material test benches were built to measure the maize seeds’ physical parameters, such as thousand-grain weight, moisture content, triaxial arithmetic mean particle size, coefficient of static friction, coefficient of rolling friction, angle of natural repose, coefficient of restitution, and stiffness coefficient. Principal component and cluster comprehensive analyses were used to simplify the characteristic parameter index used to judge the comprehensive score of maize seeds. The results showed that there were significant differences in the main physical characteristics parameters of the typical maize varieties in this cold area, and there were different degrees of correlation among the physical characteristics. Principal component analysis was used to extract the first three principal component factors, whose cumulative contribution rate was over 80%, representing most of the information of the original eight physical characteristic parameters, and had good representativeness and objectivity. According to the test results, the classification standard of the evaluation of the physical characteristics of 15 kinds of maize seeds were determined, and appropriate evaluations were conducted. The 15 kinds of maize seeds were clustered into four groups by cluster analysis, and the physical characteristics of each groups were different. This study provides a new idea for the evaluation and analysis of the physical properties of agricultural materials, and provides a new method for the screening and classification of food processing raw materials.

scholarly journals Retraction: Osmotic dehydration of fish: Principal component analysis. APTEFF, 45 (2014) 45-53, DOI: 10.2298/APT1445045L

2016 ◽  
pp. 105-105
Author(s):  
E Editorial
Keyword(s):  
Principal Component Analysis ◽  
Food Processing ◽  
Osmotic Dehydration ◽  
Principal Component ◽  
Component Analysis ◽  
Artifical Neural Network ◽  
Retracted Article ◽  
Osmotic Treatment ◽  
Font Color

On the proposition of the Editorial Board and with the consent of the authors, the paper entitled: OSMOTIC DEHYDRATION OF FISH: PRINCIPAL COMPONENT ANALYSIS, by the authors: Biljana Lj. Loncar (n?e Curcic), Lato L. Pezo, Ljubinko B. Levic, Vladimir S. Filipovic, Milica R. Nicetin, Violeta M. Knezevic and Tatjana A. Kuljanin, published in 2014 (Vol. 45, pp. 45-53, DOI: 10.2298/APT1445045L), is retracted because it is an autoplagiarism of the paper of the authors B. L. Curcic, L. L Pezo, V. S. Filipovic, M.R. Nicetin and V. Knezevic ?OSMOTIC TREATMENT OF FISH IN TWO DIFFERENT SOLUTIONS - ARTIFICAL NEURAL NETWORK MODEL?, which was accepted for publication on May 9th 2014 (DOI: 10.1111/jfpp.12275) and published in 2015 in the Journal of Food Processing and Preservation (Vol. 39, pp. 671-680).<br><br><font color="red"><b> Link to the retracted article <u><a href="http://dx.doi.org/10.2298/APT1445045L">10.2298/APT1445045L</a></b></u>

Groundwater Hydrograph Patterns in North China Plain during 1982-1986 Interpreted Using Principal Component Analysis

2011 ◽  
Vol 356-360 ◽  
pp. 2320-2324
Author(s):  
Rui Gang Zhang
Keyword(s):  
Principal Component Analysis ◽  
North China Plain ◽  
North China ◽  
Seasonal Fluctuation ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Seasonal Fluctuations ◽  
Increasing Trend ◽  
Taihang Mountain

The groundwater table depths from 1982 to 1986 of 58 unconfined wells in North China Plain(NCP) were analyzed using principal component analysis method. Results showed there were mainly three hydrograph patterns over the area: increasing trend with steady moderate seasonal fluctuations in Taihang Mountain piedmont area; decreasing trend with large seasonal fluctuation magnitudes in central plain of NCP; increasing-decreasing trend with large variance of fluctuation magnitude in piedmont of Yan Mountain piedmont.The distribution of precipitation, irrigating abstraction, and water table depths were the main factors determining the hydrograph patterns and their distribution.

Use of principal component analysis to evaluate the physical properties of Mahon cheese

1999 ◽  
Vol 210 (1) ◽  
pp. 73-76 ◽  
Author(s):  
Miguel Frau ◽  
Susana Simal ◽  
Antoni Femenia ◽  
Esther Sanjuán ◽  
C. Rosselló
Keyword(s):  
Principal Component Analysis ◽  
Physical Properties ◽  
Principal Component ◽  
Component Analysis

scholarly journals Multivariate Statistical Analysis Applied to Physical Properties of Soybean Cultivars in The Design and Regulation of Post-Harvest Equipment

2021 ◽  
Author(s):  
Paulo Coradi ◽  
Josiane Oliveira ◽  
Larissa Teodoro ◽  
Dágila Rodrigues ◽  
Paulo Teodoro ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Physical Properties ◽  
Manufacturing Industry ◽  
Physical Property ◽  
Principal Component ◽  
Component Analysis ◽  
The Other ◽  
Specific Mass ◽  
Post Harvest ◽  
Soybean Cultivars

Abstract The present work had as aim to evaluate the similar of soybean cultivars according to physical properties as a guiding parameter for decision making in the design and regulation of post-harvest equipment using multivariate analysis. First, Pearson's correlation coefficients were estimated. Posteriorly, principal component analysis was performed to verify the interrelationship between variables and soybean cultivars. A biplot was built with the first two principal components. Finally, a boxplot was built for each variable considering the grouping presented by the analysis of main components. By principal component analysis, we identified the formation of two clusters (G1 and G2) of cultivars. Unit specific mass was the physical property that most contributed to the formation of G1, while the other physical properties contributed to the formation of G2. Soybean cultivars comprising the G1 are more similar to each other only for unit specific mass, and the cultivars allocated in group G2 are more similar for all the other properties evaluated. These results are recommended by the equipment manufacturing industry and the seed processing units to carry out projects and equipment adjustments to efficiently manage the post-harvest of soybean seeds.

scholarly journals New Student Drug Tests at College Using Principal Component Analysis Method

2021 ◽  
pp. 98-108
Author(s):  
Agnes Chrisnalia ◽  
Edwar Ali ◽  
Mardainis Mardainis ◽  
Rahmiati Rahmiati
Keyword(s):  
Principal Component Analysis ◽  
Drug Users ◽  
Human Life ◽  
Principal Component ◽  
Component Analysis ◽  
Physical Characteristics ◽  
People Who Use Drugs ◽  
Analysis Method ◽  
Principal Component Analysis Method ◽  
New Student

Drugs are substances or illegal drugs that can endanger human life. Someone who consumes it in an inappropriate way will become dependent and even result in death. The physical characteristics of people who use drugs vary, but the more obvious characteristics are on the faces of drug users such as red eyes, stiff facial muscles, dark spots, pupils susceptible to light, sunken face shape, and dullness. The lack of physical characteristics of drug users due to similarities with other diseases makes it difficult for people to recognize them initially. However, for users whose face data has been tracked by the National Narcotics Agency, the facial data is stored in the dataset. This research was conducted with the aim of building a system that can detect and recognize prospective students whether they have ever been included in drug users recorded in the National Narcotics Agency dataset or not as one of the requirements for new student admissions to universities. The system built using the Principal Component Analysis method to process and extract images of the physical characteristics of drug users through the facial image data of drug users stored in the dataset. If the detected face has similarities with the characteristics in the dataset, it is necessary to suspect that the detected face is a drug user. The results of this study are the system is able to detect the faces of drug users using the Principal Component Analysis method with an accuracy of 90% and it is hoped that with this research the system can be one solution in helping universities as an identification effort to minimize drug use so that it can be an additional identification tool which strengthens someone detected using drugs.

Principal Component Analysis for Physical Properties of Electric Arc Furnace Oxidizing Slag

2014 ◽  
Vol 627 ◽  
pp. 323-326
Author(s):  
Li Chen ◽  
Tzu Yi Pai
Keyword(s):  
Principal Component Analysis ◽  
Los Angeles ◽  
Physical Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Three Dimension ◽  
Arc Furnace ◽  
Los Angeles Abrasion

In this study, the principal component analysis (PCA) was used to analyze and classify the electric arc furnace oxidizing slag based on physical properties. The results indicated that about 91.44 % information could be explained using the previous four PC. The Los Angeles abrasion test (LAAT) and loss of sodium sulfate soundness test (LSSST) mainly contributed to the first PC, meanwhile the saturated surface-dry specific gravity (SSDSG) contributed mainly to the second PC. The significant physical properties of EAF slag including LAAT, LSSST, and SSDSG could be identified according to PCA. According to the two dimension classification using PC1 and PC2, the 60 samples could be approximately classified into two groups. They could be also classified into two groups in three dimension classification.

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