scholarly journals Principal Component Analysis of Phenolic Acid Spectra

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Ronald A. Holser
Keyword(s):  
Principal Component Analysis ◽  
Ir Spectra ◽  
Phenolic Acids ◽  
Phenolic Acid ◽  
Principal Component ◽  
Component Analysis ◽  
Uv Spectra ◽  
Classification Model ◽  
Plant Metabolites ◽  
Spectral Models

Phenolic acids are common plant metabolites that exhibit bioactive properties and have applications in functional food and animal feed formulations. The ultraviolet (UV) and infrared (IR) spectra of four closely related phenolic acid structures were evaluated by principal component analysis (PCA) to develop spectral models for their rapid detection. Results demonstrated that UV and IR spectra could discriminate between each of the phenolic acids in overall models. Calculation of model scores and loadings showed that derivative UV spectra accounted for 99% variation with 2 principal components (PC) while derivative IR spectra required 3 PCs. Individual PCA models were developed for ferulic acid and p-coumaric acid using derivative UV spectra for detection and classification by soft independent modeling of class analogy (SIMCA). The application of this spectral technique as a classification model is expected to promote the use of agricultural residues as a source of these phenolic compounds.

Effect of Spectral Shifts on the Resolution of trans-1-(2-Naphthyl)-2-phenylethene Conformer UV Spectra Based on Principal Component Analysis with Self-Modeling

1994 ◽  
Vol 98 (50) ◽  
pp. 13162-13170 ◽  
Author(s):  
Jack Saltiel ◽  
Jong-Oh Choi ◽  
Donald F. Sears ◽  
David W. Eaker ◽  
Frank B. Mallory ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Uv Spectra ◽  
Spectral Shifts

scholarly journals Risk Assessment of Coal Mine Water Inrush Based On PCA-DBN

2021 ◽  
Author(s):  
Zhang ye ◽  
Tang Shoufeng ◽  
Shi Ke
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Coal Mine ◽  
Water Inrush ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model ◽  
Mine Safety ◽  
Training Time

Abstract To provide an effective risk assessment of water inrush for coal mine safety production, a BP neural network prediction method for water inrush based on principal component analysis and deep confidence network optimization was proposed. Because deep belief network (DBN) is disadvantaged by a long training time when establishing a high-dimensional data classification model, the principal component analysis (PCA) method is used to reduce the dimensionality of many factors affecting the water inrush of the coal seam floor, thus reducing the number of variables of the research object, redundancy and the difficulty of feature extraction and shortening the training time of the model. Then, a DBN network was used to extract secondary features from the processed nonlinear data, and a more abstract high-level representation was formed by combining low-level features to find the expression of the nonlinear relationship between the characteristics of water inbursts. Finally, a prediction model was established to predict the water inrush in coal mines. The superiority of this method was verified by comparing the prediction of the actual working face with the actual situation in typical mining areas of North China.

scholarly journals A comparative study on dimensionality reduction between principal component analysis and k-means clustering

2019 ◽  
Vol 16 (2) ◽  
pp. 752
Author(s):  
Norsyela Muhammad Noor Mathivanan ◽  
Nor Azura Md.Ghani ◽  
Roziah Mohd Janor
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Time Complexity ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model ◽  
Small Data ◽  
Feature Selection Technique ◽  
Data Set ◽  
Selection Technique

<span>The curse of dimensionality and the empty space phenomenon emerged as a critical problem in text classification. One way of dealing with this problem is applying a feature selection technique before performing a classification model. This technique helps to reduce the time complexity and sometimes increase the classification accuracy. This study introduces a feature selection technique using K-Means clustering to overcome the weaknesses of traditional feature selection technique such as principal component analysis (PCA) that require a lot of time to transform all the inputs data. This proposed technique decides on features to retain based on the significance value of each feature in a cluster. This study found that k-means clustering helps to increase the efficiency of KNN model for a large data set while KNN model without feature selection technique is suitable for a small data set. A comparison between K-Means clustering and PCA as a feature selection technique shows that proposed technique is better than PCA especially in term of computation time. Hence, k-means clustering is found to be helpful in reducing the data dimensionality with less time complexity compared to PCA without affecting the accuracy of KNN model for a high frequency data.</span>

scholarly journals A principal component analysis of quasar UV spectra atz ~  3

2011 ◽  
Vol 530 ◽  
pp. A50 ◽  
Author(s):  
I. Pâris ◽  
P. Petitjean ◽  
E. Rollinde ◽  
E. Aubourg ◽  
N. Busca ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Uv Spectra
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