Comparison of Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR) Methods for Protein and Hardness Predictions using the Near-Infrared (NIR) Hyperspectral Images of Bulk Samples of Canadian Wheat

2014 ◽  
Vol 8 (1) ◽  
pp. 31-40 ◽  
Author(s):  
S. Mahesh ◽  
D. S. Jayas ◽  
J. Paliwal ◽  
N. D. G. White
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Principal Components ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Hyperspectral Images ◽  
Least Squares Regression ◽  
Principal Components Regression

scholarly journals Wood species identification from Atlantic forest by near infrared spectroscopy

2019 ◽  
Vol 28 (3) ◽  
pp. e015
Author(s):  
José-Henrique Camargo Pace ◽  
João-Vicente De Figueiredo Latorraca ◽  
Paulo-Ricardo Gherardi Hein ◽  
Alexandre Monteiro de Carvalho ◽  
Jonnys Paz Castro ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Atlantic Forest ◽  
Wood Species ◽  
Native Species ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Planted Forests ◽  
Good Ability

Aim of study: Fast and reliable wood identification solutions are needed to combat the illegal trade in native woods. In this study, multivariate analysis was applied in near-infrared (NIR) spectra to identify wood of the Atlantic Forest species.Area of study: Planted forests located in the Vale Natural Reserve in the county of Sooretama (19 ° 01'09 "S 40 ° 05'51" W), Espírito Santo, Brazil.Material and methods: Three trees of 12 native species from homogeneous plantations. The principal component analysis (PCA) and partial least squares regression by discriminant function (PLS-DA) were performed on the woods spectral signatures.Main results: The PCA scores allowed to agroup some wood species from their spectra. The percentage of correct classifications generated by the PLS-DA model was 93.2%. In the independent validation, the PLS-DA model correctly classified 91.3% of the samples.Research highlights: The PLS-DA models were adequate to classify and identify the twelve native wood species based on the respective NIR spectra, showing good ability to classify independent native wood samples.Keywords: native woods; NIR spectra; principal components; partial least squares regression.

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