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.

The Successive Projections Algorithm for interval selection in trilinear partial least-squares with residual bilinearization

2014 ◽  
Vol 811 ◽  
pp. 13-22 ◽  
Author(s):  
Adriano de Araújo Gomes ◽  
Mirta Raquel Alcaraz ◽  
Hector C. Goicoechea ◽  
Mario Cesar U. Araújo
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Successive Projections Algorithm ◽  
Interval Selection ◽  
Successive Projections

scholarly journals Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm

Talanta ◽  
2018 ◽  
Vol 181 ◽  
pp. 38-43 ◽  
Author(s):  
Valber Elias de Almeida ◽  
Adriano de Araújo Gomes ◽  
David Douglas de Sousa Fernandes ◽  
Héctor Casimiro Goicoechea ◽  
Roberto Kawakami Harrop Galvão ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Sugar Production ◽  
Successive Projections Algorithm ◽  
Kernel Partial Least Squares ◽  
Interval Selection ◽  
Successive Projections

scholarly journals Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jordi Ortuño ◽  
Sokratis Stergiadis ◽  
Anastasios Koidis ◽  
Jo Smith ◽  
Chris Humphrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Plant Ecology ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

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