Spectroscopic Determination of Aboveground Biomass in Grass using Partial Least Square Regression Model

2017 ◽  
Vol 6 (9) ◽  
pp. 332
Author(s):  
Mayuri Laxman Padghan ◽  
Ratnadeep R. Deshmukh
Keyword(s):  
Regression Model ◽  
Aboveground Biomass ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Spectroscopic Determination ◽  
Least Square Regression

Mechanical Parameter Analysis of Rock Material via PLSR Model

2011 ◽  
Vol 467-469 ◽  
pp. 1826-1831 ◽  
Author(s):  
Zao Bao Liu ◽  
Wei Ya Xu ◽  
Fei Xu ◽  
Lin Wei Wang
Keyword(s):  
Regression Model ◽  
Principal Component ◽  
Partial Least Square ◽  
Rock Material ◽  
Least Square ◽  
Parameter Analysis ◽  
Partial Least Square Regression ◽  
Mechanical Parameter ◽  
Least Square Regression

Mechanical parameter analysis is a complicated issue since it is influenced by many factors. Closely related with the influencing factors of compressibility coefficients of rock material (sandstone), this article first introduces the way to process partial least square regression (PLSR) analysis. The process of carrying out PLSR is divided into six steps as for analysis and prediction of the regression model, which are data preparation, principle collection, regression model for first principle component, secondary principle analysis, establishment of final regression model and number determination of principal component l. And then introduces PLSR for application of analysis and prediction of compressibility coefficients with 30 experiment samples. Seven prediction samples are carried out by PLSR with the training process of 30 samples. The result shows PLSR has good accuracy in prediction under the condition that the model is properly deprived based on certain experimental samples. Finally, some conclusions are made for further study on both mechanical parameters and partial least square regression method.

Application of MALDI-TOFMS Combined with Partial Least Square Regression for the Determination of Mercury and Copper in Canned Tuna, Using Dithizone as the Complexing Agent and Ag(I) as Internal Standard

2018 ◽  
Vol 11 (10) ◽  
pp. 2835-2846 ◽  
Author(s):  
Manuel Mendez Garcia ◽  
Kazimierz Wrobel ◽  
Alejandra Sarahi Ramirez Segovia ◽  
Eunice Yanez Barrientos ◽  
Alma Rosa Corrales Escobosa ◽  
...  
Keyword(s):  
Internal Standard ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Complexing Agent ◽  
Canned Tuna ◽  
Least Square Regression
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