scholarly journals METODE STATISTIK PRINCIPAL COMPONENT REGRESSION DALAM ANALISIS HUBUNGAN KUANTITATIF STRUKTUR DAN AKTIVITAS PENGHAMBAT LIPOKSIGENASE SENYAWA TURUNAN KURKUMIN

2018 ◽  
Vol 1 (1) ◽  
pp. 60
Author(s):  
Didi Nurhadi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis

ABSTRAK Hubungan Kuantitatif Struktur dan Aktivitas (HKSA) pada suatu seri senyawa turunan kurkumin telah dikaji dengan menggunakan data muatan bersih atom hasil perhitungan semi empirik AM1 dengan pendekatan Principal Component Regression PCR. Pengkajian dilakukan terhadap data aktivitas antiinflamasi yang menghambat lipoksigenase (log (1/IC50)) sebagai fungsi linear dan variable laten (Tx) hasil transformasi data muatan bersih atom menggunakan Principal Component Analysis (PCA). Persamaan HKSA ditentukan berdasar kontribusi komponen yang terpilih dan selanjutnya dianalisis dengan pendekatan Model persamaan HKSA yang diperoleh adalah: log (1/IC50) = -0,669-1,816.T1+1,697.T2 –3,643.T3 Persamaan tersebut mempunyai tingkat kepercayaan 95 % dengan parameter statistik n =9,  r2 = 0.700,  SE = 0,355, Fhitung/Ftabel=1,19 dan PRESS = 0,082.  Kata kunci : HKSA, kurkumin, lipoksigenase, PCA, muatan bersih atom

Use of Chemometrics: Principal Component Analysis (PCA) and Principal Component Regression (PCR) for the Authentication of Orange Juice

1999 ◽  
Vol 32 (15) ◽  
pp. 3131-3141 ◽  
Author(s):  
Stella Vaira ◽  
Víctor. E. Mantovani ◽  
Juan C. Robles ◽  
Juan C. Sanchis ◽  
Héctor C. Goicoechea
Keyword(s):  
Principal Component Analysis ◽  
Orange Juice ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis

Quantitative measurement of complex substances dissolved in an ionic liquid using IR spectroscopy and chemometrics

2017 ◽  
Vol 84 (1) ◽  
Author(s):  
Johannes Kiefer ◽  
Andreas Bösmann ◽  
Peter Wasserscheid
Keyword(s):  
Principal Component Analysis ◽  
Ionic Liquid ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Least Squares Regression ◽  
Catalytically Active ◽  
Regression Techniques ◽  
Active Substances ◽  
Better Than

AbstractIn the past two decades, ionic liquids have found many applications as solvents for complex solutes. Prominent examples are the dissolution of biomass and carbohydrates as well as catalytically active substances. The chemical analysis of such solutions, however, is still a challenge due to the molecular complexity. In the present work, the use of infrared spectroscopy for quantifying the concentration of different solutes dissolved in an imidazolium-based ionic liquid is investigated. Binary solutions of glucose, cellubiose, and Wilkinson's catalyst in 1-ethyl-3-methylimidazolium acetate are studied as examples. For this purpose, different chemometric approaches (principal component analysis (PCA), partial least-squares regression (PLSR), and principal component regression (PCR)) for analyzing the spectra are tested. Principal component analysis was found to be suitable for classifying the different solutions. Both regression techniques were capable of deriving accurate concentration values. The performance of PLSR was slightly better than that of PCR for the same number of components.

Friction Properties Study of Metals by Principal Component Analysis

2014 ◽  
Vol 711 ◽  
pp. 231-234
Author(s):  
Zheng Zhu Zhou ◽  
Xiao Yi Jin ◽  
Xiang Wei Zhang ◽  
Yu Yi Lin
Keyword(s):  
Principal Component Analysis ◽  
Friction Coefficient ◽  
Contact Area ◽  
Friction And Wear ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Linear Regression Models ◽  
New Methods ◽  
Sliding Distance

Based on MMW-1A vertical multifunctional friction and wear tester for the study,taking steel 45 as the research object, randomly changing the experiment load, speed, sliding distance and the size of the contact area, then the data we collect are processed and analyzed by principal component analysis, and obtained linear regression models by principal component regression, regression model has been tested with good fitting effect. The results showed that the principal component analysis method is also suitable for experimental study of friction and wear, explore new methods in the analysis of tribology. It shows that load, speed and sliding distance have a weakening effect on the friction coefficient, on the contrary, the contact area has a promoting role to the friction coefficient.

Principal Component Regression Applied in Differential Optical Absorption Spectroscopy Measurement

2013 ◽  
Vol 663 ◽  
pp. 982-987
Author(s):  
Hong Lian Li ◽  
Yong Jie Wei ◽  
Wen Liang Chen
Keyword(s):  
Principal Component Analysis ◽  
Optical Absorption ◽  
Absorption Spectroscopy ◽  
Industrial Pollution ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Differential Optical Absorption Spectroscopy ◽  
Urban Atmosphere ◽  
Optical Absorption Spectroscopy

Differential optical absorption spectroscopy (DOAS) is a well-established and widely used technique for monitoring atmospheric pollution. The month performance of a DOAS system was assessed at a certain place in Tianjin University, China, where is farther away from the industrial pollution a source. Three methods were used to inverse the hourly concentrations of nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3). Principal component analysis (PCA) was performed to analyze the retrieving concentrations. Results were obtained for estimated temporal NO, NO2, SO2, O3 distributions over the urban atmosphere; demonstrating the capability of the principal component analysis applied in differential optical absorption spectroscopy (PCA-DOAS) technique.

scholarly journals Reconstruction of critical coalbed methane logs with principal component regression model: A case study

2020 ◽  
Vol 38 (4) ◽  
pp. 1178-1193
Author(s):  
Wan Li ◽  
Tongjun Chen ◽  
Xiong Song ◽  
Tianqi Gong ◽  
Mengyue Liu
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Analysis ◽  
Regression Model ◽  
Coalbed Methane ◽  
Multivariate Regression ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Methane Content ◽  
Sonic Logs

Wireline logging plays a critical role in coalbed methane exploration. However, the lack of crucial log data, such as neutron and sonic logs, makes coalbed methane exploration difficult. In this paper, we propose a principal component regression model incorporating a multiscale wavelet analysis, a histogram calibration, a principal component analysis, and a multivariate regression to reconstruct essential neutron and sonic logs from conventional logs (i.e., density, resistivity, gamma ray, spontaneous potential, and caliper logs). Our proposed model does not need core-related correlation, and there is no local optimization. We have applied the model to evaluate coalbed methane content in a real case. Firstly, we use the multiscale wavelet analysis and histogram calibration to improve logs’ reliability and lateral comparability. Then, we apply principal component analysis to transform the well-correlated wireline logs into linearly independent components and regress reconstruction functions for neutron and sonic logs with multivariate regression. The reconstructed logs are like the measured logs in trend, mean, and scale. Finally, we apply the reconstructed neutron logs to predict the coalbed methane-content distribution. The predicted distribution is not only following the regional distribution characteristics of coalbed methane enrichment zones but also validated by the coalbed methane production data. In summary, the successful applications of wireline-log reconstruction and regional coalbed methane-content prediction have demonstrated the reliability of the proposed principal component regression model.

scholarly journals PERAN EKONOMI KREATIF (SUBSEKTOR KULINER, KRIYA, DAN FESYEN) SERTA VARIABEL LAINNYA TERHADAP PENGANGGURAN

2021 ◽  
Vol 2020 (1) ◽  
pp. 1305-1315
Author(s):  
Oqxa Vyedo Samsul Zaman ◽  
Atik Mar'atis Suhartini
Keyword(s):  
Principal Component Analysis ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis

Fenomena bonus demografi akan dihadapi Indonesia pada tahun 2030-2040. Pada periode tersebut jumlah penduduk usia produktif diprediksi akan mencapai 64 persen dari jumlah penduduk total. Hal ini menjadi salah satu tantangan tersendiri baik bagi pemerintah maupun masyarakat. Jika pemerintah gagal memanfaatkan fenomena terebut, maka akan terjadi peningkatan angka pengangguran. Saat ini pengangguran yang terjadi di Indonesia lebih didominasi pengangguran usia muda. Penduduk usia muda adalah penduduk yang kaya akan ilmu pengetahuan, inovasi, kreativitas, dan semangat pantang menyerah. Salah satu sektor yang dapat menampung ide-ide kreatif dan munculnya pengangguran muda ialah sektor ekonomi kreatif. Penelitian ini bertujuan untuk mengetahui peran sektor ekonomi kreatif dan variabel-variabel lain dalam mengatasi masalah pengangguran di Indonesia tahun 2016 dengan menggunakan metode analisis Principal Component Regression (PCR). Metode ini merupakan regresi linier berganda yang digabungkan dengan Principal Component Analysis (PCA). Hasil penelitian menunjukkan bahwa sektor ekonomi kreatif belum mampu menunjukkan pengaruh signifikan terhadap tingkat pengangguran pada tahun 2016. Sedangkan upah minimum provinsi, indeks pembangunan manusia, dan investasi berpengaruh positif terhadap tingkat pengangguran. Kontrol 3 arah antara pemerintah, pengusaha dan pekerja terhadap UMP, peningkatan skill kewirausahaan cari penganggur terdidik serta kebijakan pemerintah untuk investasi bisa diarahkan sebagian ke sector ekonomi kreatif sebagai alternative dalam mengatasi permasalahan perngangguran khususnya usia muda. Selain itu diperlukan data penelitian yang lebih panjang untuk mengetahui pengaruh sector ekonomi kreatif terhadap pengangguran.

scholarly journals REGRESI KOMPONEN UTAMA ROBUST S-ESTIMATOR UNTUK ANALISIS PENGARUH JUMLAH PENGANGGURAN DI JAWA TENGAH

2019 ◽  
Vol 8 (4) ◽  
pp. 439-450
Author(s):  
Jeffri Nelwin J. O. Siburian ◽  
Rita Rahmawati ◽  
Abdul Hoyyi
Keyword(s):  
Principal Component Analysis ◽  
Regression Analysis ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Minimum Volume ◽  
Robust Approach ◽  
Central Java ◽  
Principal Component Regression Analysis ◽  
Minimum Volume Ellipsoid

Robust principal component regression s-estimator is principal component regression that applies robust approach method at principal component analysis and s-estimator at principal component regression analysis. The aim of robust principal component regression s-estimator is to overcome multicollinearity problems in multiple linier regression Ordinary Least Square (OLS) and to overcome outlier problems in principal component regression so get the most effective model. Minimum Volume Ellipsoid (MVE) is one of the robust approach methods that applied when doing principal component analysis and S-Estimator is one of the estimation methods that applied when doing principal component regression analysis. The case in this study is the factors that influence the Number of Unemployment in Central Java in 2017. The model that provides the most effective result to handling multicolliniearity and ouliers in the case study  Number of Unemployment in Central Java in 2017 is using robust principal component regression MVE-(S-Estimator) with Adjusted R2 value of 0.9615 and RSE value of 0.4073. Keywords: Robust Principal Component Regression S-Estimator, Multicollinearity, Outliers, Minimum Volume Ellipsoid (MVE), Number of Unemployment.

scholarly journals PENERAPAN METODE LEAST MEDIAN SQUARE-MINIMUM COVARIANCE DETERMINANT (LMS-MCD) DALAM REGRESI KOMPONEN UTAMA

2013 ◽  
Vol 2 (4) ◽  
pp. 6
Author(s):  
I PUTU EKA IRAWAN ◽  
I KOMANG GDE SUKARSA ◽  
NI MADE ASIH
Keyword(s):  
Principal Component Analysis ◽  
Covariance Matrix ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Square ◽  
Multivariate Normal ◽  
Minimum Covariance Determinant ◽  
The Difference ◽  
Parameter Estimators

Principal Component Regression is a method to overcome multicollinearity techniques by combining principal component analysis with regression analysis. The calculation of classical principal component analysis is based on the regular covariance matrix. The covariance matrix is optimal if the data originated from a multivariate normal distribution, but is very sensitive to the presence of outliers. Alternatives are used to overcome this problem the method of Least Median Square-Minimum Covariance Determinant (LMS-MCD). The purpose of this research is to conduct a comparison between Principal Component Regression (RKU) and Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD) in dealing with outliers. In this study, Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD) has a bias and mean square error (MSE) is smaller than the parameter RKU. Based on the difference of parameter estimators, still have a test that has a difference of parameter estimators method LMS-MCD greater than RKU method.

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