scholarly journals New model for flavour quality evaluation of soy sauce

2013 ◽  
Vol 31 (No. 3) ◽  
pp. 292-305 ◽  
Author(s):  
J. Feng ◽  
X.-B. Zhan ◽  
Z.-Y. Zheng ◽  
D. Wang ◽  
L.-M. Zhang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Principal Component ◽  
Objective Evaluation ◽  
Component Analysis ◽  
Soy Sauce ◽  
Complex Information

The soy sauce samples established a model for its flavour quality evaluation. Initially, 39 types of flavour compounds, organic acids and free amino acids in six different types of soy sauce were identified and determined by HS-SPME GC/MS and HPLC. The model was developed based on the principal component analysis method for assessing and ranking of flavour quality of soy sauce. Using the principal component analysis which simplifies complex information, our correlative evaluation model was established, tested by comparing the traditional sensory evaluation method, providing a new methodology for objective evaluation of the flavour quality of soy sauce.  

Tactile evaluation of down jacket fabric by the comprehensive handle evaluation system for fabrics and yarns

2020 ◽  
pp. 004051752097720
Author(s):  
Yuan Tian ◽  
Yi Sun ◽  
Zhaoqun Du ◽  
Dongming Zheng ◽  
Haochen Zou ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Principal Component Analysis ◽  
Evaluation System ◽  
Principal Component ◽  
Objective Evaluation ◽  
Component Analysis ◽  
Quality Level ◽  
Fabric Handle ◽  
Good Agreement

Down jacket fabric is greatly important in determining the quality of a down jacket. In order to enrich the research on fabric handle, subjective and objective evaluations were made for down jacket fabrics that were less studied. The comprehensive handle evaluation system for fabrics and yarns (CHES-FY) can be used to evaluate the tactile handle of the fabric by accurately and efficiently measuring the basic mechanical properties of the fabric. Therefore, the CHES-FY was used to link the objective evaluation with the subjective handle, so as to effectively estimate the total handle value of the down jacket fabric. Fifty-two kinds of down jacket fabrics were objectively tested through measuring 17 extracted parameters, and principal component analysis was adopted to establish the five main handle characteristics of fullness, softness, stiffness, smoothness, looseness and tightness to characterize basic style of the down jacket fabrics. The results showed that the subjective and objective results were in good agreement. These characteristics can be used as indicators to characterize fabric performance, and the principal component expression to characterize fabric handle can better predict the handle characteristics of down jacket fabrics. This also proves that the CHES-FY can quickly and accurately obtain the fabric handle value, and can also evaluate the fabric quality level.

Evaluation of English interpretation teaching quality based on GA optimized RBF neural network

2020 ◽  
pp. 1-8
Author(s):  
Lu Chen ◽  
He Being
Keyword(s):  
Neural Network ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Principal Component ◽  
Teaching Quality ◽  
Network Teaching ◽  
Time Performance

Aiming at the problem of low accuracy of the current English interpretation teaching quality evaluation, a teaching quality evaluation method based on a genetic algorithm (GA) optimized RBF neural network is proposed. First, the principal component analysis is used to select the teaching quality evaluation index, and then design The RBF neural network teaching evaluation model is used, and GA is used to optimize the initial weights of the RBF neural network. Experimental results show that this method can effectively evaluate the quality of English interpretation teaching, and has high accuracy and real-time performance.

Drivability evaluation model using principal component analysis and optimized extreme learning machine

2019 ◽  
Vol 25 (16) ◽  
pp. 2274-2281 ◽  
Author(s):  
Wei Huang ◽  
Hai Jiang Liu ◽  
Yi Fei Ma
Keyword(s):  
Principal Component Analysis ◽  
Extreme Learning Machine ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Principal Component ◽  
Component Analysis ◽  
Optimal Number ◽  
Pass Rate ◽  
Learning Machine ◽  
Hidden Layer

The accuracy of the evaluation method is essential to optimize the control system and improve a vehicle’s drivability quality. This study aimed at exploring a more effective drivability evaluation method and a drivability evaluation model was proposed on the basis of principal component analysis and optimization of an extreme learning machine. The drivability evaluation model was built using an extreme learning machine. The input of the model was determined by the principal component analysis method, and the optimal number of neurons in the hidden layer of the drivability evaluation model was obtained by a particle swarm optimization algorithm. The experimental results show that considering the evaluation index coupling factors can improve the prediction accuracy of the evaluation model. The R correlation between the score predicted by the drivability evaluation model proposed in this paper and the actual score reached 0.979, and the predicted pass rate also reached 95%, which indicate the model was more accurate and stable than others. The evaluation model can be extended to fuel economy and handling stability. It also has theoretical guidance and application value in practical problems.

Disassembly Evaluation Based on Principal Component Analysis

2013 ◽  
Vol 712-715 ◽  
pp. 2894-2899
Author(s):  
Na Zhao ◽  
Rui Mu ◽  
Wen Wu Wang
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Quantitative Evaluation ◽  
Information Integration ◽  
Evaluation Model ◽  
Principal Component ◽  
Evaluation Process ◽  
Objective Evaluation ◽  
Component Analysis ◽  
Disassembly Process

A method for evaluating the ease of disassembly of products was presented in this paper. In order to conduct more objective evaluation and reduce the fuzziness and subjectivity in evaluation process, the disassembly evaluation model based on the principal component analysis (PCA) was proposed in this paper. The evaluation metrics mainly incorporated factors closely related with disassembly process, and the principal component analysis enabled more objectively quantitative evaluation, through dimensionality reduction and information integration of metrics. Finally, an example of disassembly evaluation was given to verify the effectiveness of the method.

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