Development of Robust Partial Least Squares Regression Model for Spectroscopic Determination of Diclofenac Sodium in Environmental Samples

2020 ◽  
Vol 16 (3) ◽  
pp. 241-249
Author(s):  
Biswanath Mahanty ◽  
Angel P. John
Keyword(s):  
Variable Selection ◽  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Environmental Samples ◽  
Partial Least Square ◽  
Least Square ◽  
Analytical Sensitivity ◽  
Pls Regression ◽  
Spiked Soil

Background: Diclofenac (DCF) is an important widely used non-steroidal antiinflammatory drug. Disposal of expired formulation, excretion from administered dose, the poor performance of sewage treatment process, contributes to its frequent detection in environment. Analysis of DCF in environmental sample requires time consuming pretreatment, extraction steps. Though, UV absorption analysis of DCF is simple but spectral interference of soil organic matter is a problem. The aim of this paper is to establish appropriate partial least square chemometric model for DCF quantitation through variable selection, and validation of analytical method through multivariate figure of merit analysis. Methods: Spectral data of DCF spiked soil solution is recorded and variants of partial least squares (PLS) regression viz., backward-interval PLS (biPLS), synergy-interval PLS (siPLS) and genetic algorithm (GA) based PLS models (GA-PLS) are developed from autoscaled and 2nd order differential spectrum. Prediction fidelity of the selected models was evaluated from a blind-folded semi-synthetic spectral data. The method was validated through figures of merit estimates, such as selectivity, analytical sensitivity, limits of detection and quantitation. Results: The siPLS model developed offered the minimum root mean square error of crossvalidation (RMSECV) of 0.1896 mg/l and 0.1910 mg/l for autoscaled data (9 variables) and derivative spectra (12 variables), respectively. Refinement of the derivative spectrum with GA offered a simplified model (RMSECV:0.1712, 10 variable). Conclusion: The GA based variable selection for PLS regression analysis offers robust analytical tool for DCF in environmental samples. Further research is warranted to model variable interference in spectral data unknown to analyst in priori.

Determination of Iodine Value of Palm Oils Using Partial Least Squares Regression-Fourier Transform Infrared Data

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Nor Fazila Rasaruddin ◽  
Mas Ezatul Nadia Mohd Ruah ◽  
Mohamed Noor Hasan ◽  
Mohd Zuli Jaafar
Keyword(s):  
Fourier Transform ◽  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Correlation Coefficient ◽  
Iodine Value ◽  
Cross Validation ◽  
Pls Regression ◽  
Pure Sample

This paper shows the determination of iodine value (IV) of pure and frying palm oils using Partial Least Squares (PLS) regression with application of variable selection. A total of 28 samples consisting of pure and frying palm oils which acquired from markets. Seven of them were considered as high-priced palm oils while the remaining was low-priced. PLS regression models were developed for the determination of IV using Fourier Transform Infrared (FTIR) spectra data in absorbance mode in the range from 650 cm-1 to 4000 cm-1. Savitzky Golay derivative was applied before developing the prediction models. The models were constructed using wavelength selected in the FTIR region by adopting selectivity ratio (SR) plot and correlation coefficient to the IV parameter. Each model was validated through Root Mean Square Error Cross Validation, RMSECV and cross validation correlation coefficient, R2cv. The best model using SR plot was the model with mean centring for pure sample and model with a combination of row scaling and standardization of frying sample. The best model with the application of the correlation coefficient variable selection was the model with a combination of row scaling and standardization of pure sample and model with mean centering data pre-processing for frying sample. It is not necessary to row scaled the variables to develop the model since the effect of row scaling on model quality is insignificant.

Research on Relevance Vector Machine Model Based on Kernel Partial Least Square in Properties of Engineering Materials

2014 ◽  
Vol 952 ◽  
pp. 311-314
Author(s):  
Xu Sheng Gan ◽  
Can Yang ◽  
Hai Long Gao
Keyword(s):  
Feature Extraction ◽  
Least Squares ◽  
Partial Least Squares ◽  
Relevance Vector Machine ◽  
Partial Least Square ◽  
Least Square ◽  
Machine Model ◽  
Prediction Of Properties ◽  
Kernel Partial Least Squares ◽  
Engineering Materials

To improve the prediction of properties of engineering materials, a Relevance Vector Machine (RVM) regression algorithm based on Kernel Partial Least Squares (KPLS) is proposed. In the algorithm, firstly execute the feature extraction from the original samples using KPLS, and then use obtained feature to realize RVM regression. The simulation shows that the hybrid regression algorithm can effectively reduce the difficulty on RVM modeling and has a wide application in prediction of properties of engineering materials.

Dynamic Partial Least Square Path Modeling for the Front-end Product Design and Development

2012 ◽  
Vol 134 (10) ◽  
Author(s):  
Chathura Withanage ◽  
Taezoon Park ◽  
Truong Ton Hien Duc ◽  
Hae-Jin Choi
Keyword(s):  
Least Squares ◽  
Product Design ◽  
Partial Least Squares ◽  
Partial Least Square ◽  
Least Square ◽  
Path Model ◽  
Percentage Error ◽  
Product Attributes ◽  
Design And Development ◽  
Front End

The dynamic nature of today’s technology market requires new value-characteristic modeling methods; mainstream methods have limitations due to unrealistic assumptions, such as static customer preferences and no multicollinearity among product attributes. In particular, products with longer cycle times can suffer because the static model ignores changes in the market during the concept-to-customer lead time. This study proposes a dynamic, partial least squares path model for customer driven product design and development in order to reduce model uncertainty by formulating preference models to reflect market dynamics. The proposed dynamic model adopted partial least squares regression to handle the limited observations plagued by multicollinearity among product attributes. The main advantage of the proposed model is its ability to evaluate design alternatives during the front-end concept screening phase, using the overall product-value metric, customer-revealed value. A case study analyzing the US car market data for sedans from 1990 to 2010 showed the potential for the proposed method to be effective, with a 3.40 mean absolute percentage error.

A Consensus Partial Least Squares Regression for Analysis of Near-Infrared Spectroscopy

2013 ◽  
Vol 765-767 ◽  
pp. 528-531
Author(s):  
Dan Peng ◽  
Qing Chen Nie
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Squares Regression ◽  
Prediction Ability ◽  
Successful Model ◽  
Consensus Strategy

To improve the prediction performance of partial least square regression algorithm (PLS), the consensus strategy was applied to develop the multivariate regression model using near-infrared (NIR) spectra and named as C-PLS. Coupled with the consensus strategy, this algorithm can take the advantage of reducing dependence on single model to obtain prediction precision and stability by randomly changing the calibration set. Through an optimization of the parameters involved in the model including criterion threshold and number of sub-models, a successful model was achieved by effectively combining many sub-models with different accuracy and diversity together. To validate the C-PLS algorithm, it was applied to measure the original extract concentration of beer using NIR spectra. The experimental results showed that the prediction ability and robustness of model obtained in subsequent partial least squares calibration using consensus strategy was superior to that obtained using conventional PLS algorithm, and the root mean square error of prediction can improve by up to 45.2%, indicating that it is an efficient tool for NIR spectra regression.

Information Granule Reduction and Cluster Based Partial Least Squares Applied in EEG Signals

2014 ◽  
Vol 496-500 ◽  
pp. 2256-2259
Author(s):  
Zhen Dong Mu ◽  
Jian Feng Hu ◽  
Jing Hai Yin
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Clustering Algorithm ◽  
Main Idea ◽  
Electrical Signal ◽  
Partial Least Square ◽  
Least Square ◽  
Complex Signal ◽  
Eeg Signals ◽  
Information Granule

EEG is a complex signal source, feature extraction and classification algorithm was studied for the brain electrical signal is also a key point in the research of brain waves, information granule clustering algorithm is one of the main idea, at the same time, the partial least square method is an effective method of dimension reduction, this paper, the use of information granule and partial least squares analysis of visual evoked potential EEG signals, the results show that this method can effectively extract the characteristics.

scholarly journals libPLS: An Integrated Library for Partial Least Squares Regression and Discriminant Analysis

2014 ◽  
Author(s):  
Hongdong Li ◽  
Qingsong Xu ◽  
Yizeng Liang
Keyword(s):  
Discriminant Analysis ◽  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Model Assessment ◽  
Pls Regression ◽  
Least Squares Regression ◽  
Integrated Platform ◽  
Pls Modeling

Partial least squares (PLS) have gained wide applications especially in chemometrics, metabolomics/metabonomics as well as bioinformatics. To our knowledge, an integrated PLS library that include not only basic PLS modeling algorithms but also advanced and/or recently developed methods on model assessment, outlier detection and variable selection is in lack. Here we present libPLS which provides an integrated platform for developing PLS regression and/or discriminant analysis (PLS-DA) models. This library is written in MATLAB and freely available at www.libpls.net.

scholarly journals Fault identification for chiller sensor based on partial least square method

2021 ◽  
Vol 233 ◽  
pp. 03057
Author(s):  
Bang Wu ◽  
Yunpeng Hu ◽  
Chuanhui Zhou ◽  
Guaiguai Chen ◽  
Guannan Li
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Fault Identification ◽  
Least Squares Regression

Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.

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