Principle Component Regression Models Research Articles

Analisis Regresi Komponen Utama untuk Mengatasi Multikolinearitas pada Faktor-Faktor yang Mempengaruhi Indeks Pembangunan Manusia

West Nusa Tenggara and East Nusa Tenggara provinces are among the provinces with low human development indexes. There are seven factors used in this study that are considered to affect the human development index in the two provinces, namely gross regional domestic product, poor population, open unemployment rate, population, life expectancy, labor force and average years of schooling. The method used by researchers in overcoming multicollinearity in this study is principal component regression. Therefore, this study aims to apply principal component regression in overcoming the problem of multicollinearity on the the effect of gross regional domestic product, poor population, open unemployment rate, population, life expectancy, labor force and average years of schooling on the human development index. Based on the results of the analysis that has been carried out, the principle component regression model is obtained as follows Y = 42.548 + 0.00000991X_1 + 0.0371X_2 + 0.343X_3 + 0.000005949X_4 + 0.2532 + 0.000012947X_6 + 0.1348X_7. With the coefficient of determination (R^2) 0.834.

“Forecasting particulate matter concentrations by combining statistical models”

Air pollutants have adverse effects on human health and play significant roles in urban planning and specifying air quality ranges. Atmospheric particulate matter is one of the criteria air pollutants that may have a dominant effect on air pollution. The present analysis was made using data collected over five years (2011–2015) in an urban area. Air pollutant concentrations and climate data are analyzed using four models: a multiple linear regression model, a principle component regression model, a logarithmic architecture, a principle component regression model with the variables that only have the highest factor loadings in each principal component. Thus, the proposed model combines both methods the multiple linear regression and the principal component analysis to obtain clearer and more reliable predictions. The prediction model’s accuracy has been verified operating several performance indicators, which revealed acceptable values, demonstrating that the proposed model can be used to predict pollutant concentrations. According to statistical indicators (RMSE, NMSE, CV, FB and IOA), the best prediction models were Model 3 for winter (0.06, 0.001, 0.03, −0.000, and 0.69), Model 4 for spring (0.08, 0.002, 0.04, −0.019, and 0.99), Model1 for summer (3.41, 0.005, 0.07, 0.000, and 0.98), and Model 2 for autumn (11.71, 0.018, 0.13, −0.000, and 0.57). In addition, Model2 generally gave appropirate values for all seasons and can be used as a common model. Finally, combined models based on principal component analysis and multiple linear regression outperformed models with only multiple linear regression in terms of error.

Application of TLC-Spectrodensitometric and Chemometric Methods for Determination of Momenta® Cream: A Comparative Study Applied on Ternary Mixture

A comparative study for the validation and advancement of two analytical approaches applied for the simultaneous determination of Mometasone Furoate (MF), Miconazole Nitrate (MIC) and Gentamicin (GM) formulated in Momenta® cream. The first approach was TLC-spectrodensitometric method, which was advanced by separating the three components on TLC aluminum plates coated with silica gel 60 F254 using chloroform: methanol: formic acid (4:0.3:0.15, v/v/v) as a mobile phase, then scanned at 254nm using Camage TLC scanner 3 operated in reflectance-absorbance mode. The second approach was the chemometric method using two models: Partial Least Squares (PLS) and Principle Component Regression Model (PCR). The proposed approaches were validated according to ICH guidelines and were applied for the determination of the ternary mixtures in their analytical mixtures and pharmaceutical preparation.

Determination of Mixderm Cream: A Comparative Study Applied on Quaternary Mixture

A comparative study for the validation of two analytical approaches applied for the simultaneous determination of betamethasone valerate (BV), tolnaftate (TF), gentamicin (GM), and clioquinol (CQ) formulated in mixderm® cream. The first approach was TLC-spectrodensitometric method, which was advanced by separating the four components on TLC aluminum plates using chloroform-methanol-acetic acid-formic acid (6:1:0.15:0.25, v/v/v/v) as a mobile phase, then scanned at 254 nm. The detection limit is in the range 0.07–0.48 mg mL−1. The second approach was the chemometric method using two models: partial least squares (PLS) and principle component regression model (PCR). The proposed approacheswere validated according to ICH guidelines were applied for the determination of the quaternary mixtures in their analytical mixtures and pharmaceutical preparations.

A chemometric approach for the quantification of free fatty acids in cottonseed oil by Fourier transform infrared spectroscopy

ABSTRACTThe aim of this study was to quantify free fatty acid in cottonseed oil (Gossypium) variety by a chemometric approach using Fourier transform infrared spectroscopy. Calibration standards were prepared by gravimetrical mixing of oleic acid (0.1–40 g/100 g) in neutralized cottonseed oil containing <0.1% free fatty acids. Fourier transform infrared technique coupled with partial least square and principle component regression models were used to develop calibrations in the specific absorption region of carbonyl between 1690–1727 cm−1. On the basis of regression coefficient and evaluated free fatty acids results with comparison to titration method, partial least square was found to be more accurate than principle component regression calibration model. All the analyzed cottonseed oil varieties showed high content of free fatty acids in the range of 17.1–38.5%. The results of the present study indicated that Fourier transform infrared method in combination with partial least square or principle component regression could be used as a greener alternative to the standard titration method.

Design, optimization, and validation of thin-layer chromatography–densitometry and chemometry-assisted spectrophotometry: A comparative study applied on quaternary mixture

This work presents a comparative study on the development and validation of two analytical techniques applied for the simultaneous determination of hydrocortisone acetate (HCA), fusidic acid (FSA), methyl paraben (MPB), and propyl paraben (PPB) formulated as a topical cream. The first technique was thin-layer chromatography (TLC)–densitometric method, which was developed by separating the four components on silica gel 60 F254 using methylene chloride–methanol–benzene in the ratio of 10:2:5, v/v, as the developing system, followed by densitometric measurement of the bands at 240 nm. The second technique was the chemometric method using two models: principle component regression model (PCR) and partial least squares (PLS). The suggested techniques were validated in compliance with the International Conference on Harmonization (ICH) guidelines and were successfully applied for the determination of the quaternary mixtures as prepared synthetically in laboratory and in the commercial topical pharmaceutical for...