A Principal Component Analysis of Main Factors Influencing Seed Germination of Bioenergy Grass

2013 ◽  
Vol 805-806 ◽  
pp. 236-239
Author(s):  
Dan Shan ◽  
Yan Ping Liu ◽  
En De Xing ◽  
Peng Cheng Gao
Keyword(s):  
Principal Component Analysis ◽  
Seed Germination ◽  
Alternative Energy ◽  
Hot Spot ◽  
Principal Component ◽  
Component Analysis ◽  
Seed Vigor ◽  
Biomass Energy ◽  
Factors Influencing ◽  
Main Factors

Biomass energy is considered to be an ideal renewable alternative energy and herbaceous energy plants as an important biomass resources became research hot spot all over the world. Switchgrass (Panicum virgatum L.) is international recognized as the most suitable bioenergy plant for the past few years. In order to obtain the basic data about seed germination characteristics on switchgrass as a bioenergy grass, different temperatures are investigated to select reliability indexes on seed germination of two species switchgrass from eight measurement indexes and then analyzed main factors influencing seed vigor by a principal component analysis. The result showed that co-efficiency of germination (CG), germination index (GI) and Peak value (PV) from eight germination indexes can be used as measurement indexes for detect seed germination of two species switchgrass.

scholarly journals Instrumental and Sensory Analysis of the Properties of Traditional Chinese Fried Fritters

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Daming Fan ◽  
Bowen Yan ◽  
Huizhang Lian ◽  
Jianxin Zhao ◽  
Hao Zhang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Factors Influencing ◽  
Human Evaluation ◽  
Main Factors ◽  
Instrumental Methods ◽  
The Relationship ◽  
Evaluation Techniques

The quality of traditional Chinese fried fritters is typically measured using human sensory evaluation techniques and physicochemical indices, the process of which is laborious and time-consuming. This study aimed to investigate the relationship between instrumental parameters, sensory criteria, and physicochemical indices. Significant correlations were found using principle component analysis. Volume, fat, texture, palatability, and instrumental parameters (hardness, fracturability, springiness, and gumminess) were found to be the main factors influencing the quality of Chinese fried fritters by principal component analysis (PCA) and instrumental methods, which were satisfactory replacement for human evaluation in correlation testing.

Principal component analysis of the main factors of line intensity enhancements observed in oscillating direct current plasma

2010 ◽  
Vol 65 (11) ◽  
pp. 927-934 ◽  
Author(s):  
Milovan M. Stoiljković ◽  
Igor A. Pašti ◽  
Miloš D. Momčilović ◽  
Jelena J. Savović ◽  
Mirjana S. Pavlović
Keyword(s):  
Principal Component Analysis ◽  
Direct Current ◽  
Line Intensity ◽  
Principal Component ◽  
Component Analysis ◽  
Direct Current Plasma ◽  
Main Factors

Study on Prediction Model of Grain Yield Based on Principal Component Analysis and BP Neural Network

2015 ◽  
Vol 713-715 ◽  
pp. 1939-1942
Author(s):  
Xing Mei Xu ◽  
Li Ying Cao ◽  
Jing Zhou
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Grain Yield ◽  
Bp Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Jilin Province ◽  
Average Error ◽  
Main Factors

Taking the grain yield data from 1980 to 2012 of Jilin Province for example, this paper analyzes the main factors that influences the grain yield based on the principle component analysis method. According to these main factors, the input samples of BP neutral network are definite. Thereby, the BP neutral networks could be trained to predict. The results show that the fertilizer consumption, large cattle head number, end grain sowing area, effective irrigation area and rural per capita living space are the main effect factor on grain yield. The BP neural network was built by using it as the input samples. The number of input nodes of the network is determined. Then build the prediction model of grain production in Jilin province. The simulation results show that, the average error of prediction results of BP neural network model based on principal component analysis is 4.48%.

scholarly journals Factors Influencing Consumers’ Attitude Towards Biopreservatives

2020 ◽  
Vol 12 (24) ◽  
pp. 10338
Author(s):  
Maria Angela Perito ◽  
Emilio Chiodo ◽  
Annalisa Serio ◽  
Antonello Paparella ◽  
Andrea Fantini
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Marketing Strategies ◽  
Organic Foods ◽  
Web Based ◽  
Factors Influencing ◽  
Potential Market

Biopreservatives have received considerable attention in recent years as natural alternatives to synthetic preservatives. This seems to be a response to an increased demand for natural and organic foods. This study investigates the potential market for products enriched with biopreservatives in Italy. Data were collected from a sample of Italian consumers (N = 479) using a web-based survey. The main results indicate that 64% of respondents declared themselves to be willing to consume biopreservatives only if they replaced synthetic preservatives. Principal component analysis (PCA) was applied to reduce the number of variables. The factorial scores of the components obtained from PCA were used for a Cluster Analysis related to consumers’ perceptions about biopreservatives. Moreover, the survey highlights that the respondents had positive opinions about biopreservatives, although they showed difficulty in perceiving the exact meaning of the term. The study could provide useful implications for food manufacturers and facilitate the design of marketing strategies for foods enriched with biopreservatives.

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