Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging

TY - JOUR

T1 - Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging

AU - Calvini, Rosalba

AU - Ulrici, Alessandro

AU - Amigo Rubio, Jose Manuel

PY - 2015/8/5

Y1 - 2015/8/5

N2 - In the present work sparse-based methods are applied to the analysis of hyperspectral images with the aim at studying their capability of being adequate methods for variable selection in a classification framework. The key aspect of sparse methods is the possibility of performing variable selection by forcing the model coefficients related to irrelevant variables to zero. In particular, two different sparse classification approaches, i.e. sPCA. +. kNN and sPLS-DA, were compared with the corresponding classical methods (PCA. +. kNN and PLS-DA) to classify Arabica and Robusta coffee species. Green coffee samples were analyzed using near infrared hyperspectral imaging and the average spectra from each hyperspectral image were used to build training and test sets; furthermore a test image was used to evaluate the performances of the considered methods at pixel-level. In our case, sparse methods led to similar results as classical methods, with the advantage of obtaining more interpretable and parsimonious models. An important result to highlight is that variable selection performed with two different sparse classification approaches converged to the selection of same spectral regions, which implies the chemical relevance of those regions in the discrimination of Arabica and Robusta coffee species.

AB - In the present work sparse-based methods are applied to the analysis of hyperspectral images with the aim at studying their capability of being adequate methods for variable selection in a classification framework. The key aspect of sparse methods is the possibility of performing variable selection by forcing the model coefficients related to irrelevant variables to zero. In particular, two different sparse classification approaches, i.e. sPCA. +. kNN and sPLS-DA, were compared with the corresponding classical methods (PCA. +. kNN and PLS-DA) to classify Arabica and Robusta coffee species. Green coffee samples were analyzed using near infrared hyperspectral imaging and the average spectra from each hyperspectral image were used to build training and test sets; furthermore a test image was used to evaluate the performances of the considered methods at pixel-level. In our case, sparse methods led to similar results as classical methods, with the advantage of obtaining more interpretable and parsimonious models. An important result to highlight is that variable selection performed with two different sparse classification approaches converged to the selection of same spectral regions, which implies the chemical relevance of those regions in the discrimination of Arabica and Robusta coffee species.

U2 - 10.1016/j.chemolab.2015.07.010

DO - 10.1016/j.chemolab.2015.07.010

M3 - Journal article

SN - 0169-7439

VL - 146

SP - 503

EP - 511

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

ER -