Using a partial least squares (PLS) method for estimating cyanobacterial pigments in eutrophic inland waters

2009 ◽  
Author(s):  
A. L. Robertson ◽  
L. Li ◽  
L. Tedesco ◽  
J. Wilson ◽  
E. Soyeux
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Inland Waters ◽  
Pls Method

A weighted ensemble method based on wavelength selection for near-infrared spectroscopic calibration

2019 ◽  
Vol 11 (36) ◽  
pp. 4593-4599
Author(s):  
Shaohui Yu ◽  
Jing Liu
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Ensemble Method ◽  
Wavelength Selection ◽  
Weighted Clustering ◽  
Infrared Spectroscopic ◽  
Selection For ◽  
Pls Method

A weighted clustering and pruning of wavelength variables-partial least squares (WCPV-PLS) method was proposed.

scholarly journals Automated Fitting Process Using Robust Reliable Weighted Average on Near Infrared Spectral Data Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2099
Author(s):  
Divo Dharma Silalahi ◽  
Habshah Midi ◽  
Jayanthi Arasan ◽  
Mohd Shafie Mustafa ◽  
Jean-Pierre Caliman
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Weighted Average ◽  
Optimal Number ◽  
Optimum Number ◽  
Plsr Model ◽  
Pls Method ◽  
Selection Of

With the complexity of Near Infrared (NIR) spectral data, the selection of the optimal number of Partial Least Squares (PLS) components in the fitted Partial Least Squares Regression (PLSR) model is very important. Selecting a small number of PLS components leads to under fitting, whereas selecting a large number of PLS components results in over fitting. Several methods exist in the selection procedure, and each yields a different result. However, so far no one has been able to determine the more superior method. In addition, the current methods are susceptible to the presence of outliers and High Leverage Points (HLP) in a dataset. In this study, a new automated fitting process method on PLSR model is introduced. The method is called the Robust Reliable Weighted Average—PLS (RRWA-PLS), and it is less sensitive to the optimum number of PLS components. The RRWA-PLS uses the weighted average strategy from multiple PLSR models generated by the different complexities of the PLS components. The method assigns robust procedures in the weighing schemes as an improvement to the existing Weighted Average—PLS (WA-PLS) method. The weighing schemes in the proposed method are resistant to outliers and HLP and thus, preserve the contribution of the most relevant variables in the fitted model. The evaluation was done by utilizing artificial data with the Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp. Based on the results, the method claims to have shown its superiority in the improvement of the weight and variable selection procedures in the WA-PLS. It is also resistant to the influence of outliers and HLP in the dataset. The RRWA-PLS method provides a promising robust solution for the automated fitting process in the PLSR model as unlike the classical PLS, it does not require the selection of an optimal number of PLS components.

The successive projections algorithm for interval selection in partial least squares discriminant analysis

2016 ◽  
Vol 8 (41) ◽  
pp. 7522-7530 ◽  
Author(s):  
David Douglas de Sousa Fernandes ◽  
Valber Elias Almeida ◽  
Licarion Pinto ◽  
Germano Véras ◽  
Roberto Kawakami Harrop Galvão ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Multivariate Calibration ◽  
Successive Projections Algorithm ◽  
Selection Approach ◽  
Interval Selection ◽  
Successive Projections ◽  
Pls Method

This paper proposes a new interval selection approach for PLS-DA modelling, which is developed as an extension of the recently introduced iSPA-PLS method for multivariate calibration.

Characterization and analysis of soils using mid-infrared partial least-squares .2. Correlations with some laboratory data

Soil Research ◽  
1995 ◽  
Vol 33 (4) ◽  
pp. 637 ◽  
Author(s):  
LJ Janik ◽  
JO Skjemstad
Keyword(s):  
Soil Properties ◽  
Least Squares ◽  
Partial Least Squares ◽  
Analytical Data ◽  
Quantitative Information ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
Organic Components ◽  
Pls Analysis ◽  
Pls Method

Infrared partial least squares (PLS) analysis is shown to provide a simple, rapid chemometric technique for the simultaneous analysis of soil properties. The method is capable of extracting both qualitative and quantitative information from soil spectra. A number of the mineral and organic components which are responsible for certain soil properties have been identified and the prediction of these properties assessed. Diffuse reflectance infrared Fourier-transform (DRIFT) spectra of whole soils were recorded to form a large training data set. The spectral information from this set was compressed into a small number of subspectra (called weight loadings) which contained positive and negative peaks reflecting correlations between the soil mineral and organic components and corresponding analytical data. Positive peaks in the weight loadings corresponding to organic components including alkyl, carboxylic and amide species were highly correlated with OC and N. Likewise, smectite, kaolinite and gibbsite clay minerals, together with organic alkyl and carboxylic species, contributed either positively or negatively to pH, sum of cations and clay content. Positive peaks due to calcite were well resolved in the first carbonate weight loading. Quartz was identified as an 'interference' for all analyses, with a series of negative peaks in the weight loadings. Implications were that quartz exerted a strong spectral signal for the majority of soil spectra, although it was not directly related to particular analyses. The usual PLS method, in which there is assumed to be a linear relationship between the loading intensities and soil property values, was found to give nonlinear prediction regression. The nonlinearity was assumed to be due to the effects of nonlinear response of the DRIFT signal and to significant compositional variability between calibration samples with high and low analyte concentrations. An alternative strategy of using a locally linear PLS model was tested, where small subsets of the total span of analytical values were independently used for PLS analysis. This approach improved prediction linearity and precision improved significantly for most analyses. The PLS method was thus shown to provide a useful surrogate technique for the study of soils, with which the PLS analysis of a single spectrum could provide simultaneous qualitative and quantitative information on a number of widely different soil analyses.

Partial Least-Squares Quantitative Analysis of Infrared Spectroscopic Data. Part I: Algorithm Implementation

1988 ◽  
Vol 42 (2) ◽  
pp. 217-227 ◽  
Author(s):  
M. P. Fuller ◽  
G. L. Ritter ◽  
C. S. Draper
Keyword(s):  
Quantitative Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Spectroscopic Data ◽  
Principal Component Regression ◽  
Principal Component ◽  
Advantages And Disadvantages ◽  
K Matrix ◽  
Algorithm Implementation ◽  
Pls Method

Various approaches to infrared multicomponent quantitative analysis including K-matrix, multivariate least-squares, principal component regression (PCR), and partial least-squares (PLS) are compared. The advantages and disadvantages of each are discussed. A particular implementation of the PLS method is detailed, with emphasis on the methods provided for calibration optimization and evaluation.

scholarly journals Locality-Preserving Partial Least Squares Regression

2022 ◽  
pp. 173-188
Author(s):  
Jing Wang ◽  
Jinglin Zhou ◽  
Xiaolu Chen
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Data Sets ◽  
Least Squares Regression ◽  
Nonlinear Structure ◽  
Structure Preserving ◽  
The Core ◽  
Locality Preserving ◽  
Pls Method

AbstractThis chapter proposes another nonlinear PLS method, named as locality-preserving partial least squares (LPPLS), which embeds the nonlinear degenerative and structure-preserving properties of LPP into the PLS model. The core of LPPLS is to replace the role of PCA in PLS with LPP. When extracting the principal components of $$\boldsymbol{t}_i$$ t i and $$\boldsymbol{u}_i$$ u i , two conditions must satisfy: (1) $$\boldsymbol{t}_i$$ t i and $$\boldsymbol{u}_i$$ u i retain the most information about the local nonlinear structure of their respective data sets. (2) The correlation between $$\boldsymbol{t}_i$$ t i and $$\boldsymbol{u}_i$$ u i is the largest. Finally, a quality-related monitoring strategy is established based on LPPLS.

Study TCM Prescription Compatibility Dose-Response Relationship Based on MSC-PLS

2014 ◽  
Vol 687-691 ◽  
pp. 4042-4045
Author(s):  
Bin Nie ◽  
Ri Yue Yu ◽  
Zhuo Wang
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Least Squares Method ◽  
Regression Coefficient ◽  
Dose Response Relationship ◽  
Signal Correction ◽  
Medicine Prescription ◽  
Chinese Medicine Prescription ◽  
Partial Least Squares Method ◽  
Pls Method

Study traditional Chinese medicine prescription compatibility based on multiplicative signal correction and partial least squares (MSC-PLS). Method: mathematical modeling base on MSC-PLS. Results: gain the regression coefficient and equation, VIP sorting, loadings Bi plot, and seek out the optimized direction of the prescription.Conclusion: using multiplicative signal correction and partial least squares method optimize the compatibility of the dachengqi decoction cure ileus rats is feasible and effective.

Mid-Infrared Spectra from Near-Infrared Spectra Using Partial Least-Squares

1993 ◽  
Vol 47 (11) ◽  
pp. 1747-1750 ◽  
Author(s):  
Raymond Lew ◽  
Stephen T. Balke
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Absorbance Spectrum ◽  
Mid Infrared ◽  
Near Infrared Spectra ◽  
Mir Spectra ◽  
Evaluation Work ◽  
Pls Method

In this novel application of a multivariate method, partial least-squares (PLS) was used to generate mid-infrared (MIR) spectra (rather than selected concentrations) from near-infrared (NIR) spectra. The NIR spectra were obtained by in-line monitoring of a molten polymer blend of polyethylene with polypropylene during extrusion. Off-line MIR spectra of blends were used to calibrate the PLS method. Then PLS was used to generate the MIR absorbance spectrum of a 50:50-by-weight blend not included in the calibration set from its NIR spectrum. The synthesized MIR spectrum agreed very well with a directly measured one. The exception was absorbance peaks which were so strong that they apparently represented responses that were nonlinear with respect to concentration. Although more evaluation work has yet to be done, these results are encouraging, and they indicate that NIR interpretation may readily borrow the strengths of MIR interpretation both qualitatively and quantitatively.

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