Classification of Brazilian soils by using LIBS and variable selection in the wavelet domain.
This paper proposes a novel analytical methodology for soil classification based on the use of laser-induced breakdown spectroscopy (LIBS) and chemometric techniques. In the proposed methodology, linear discriminant analysis (LDA) is employed to build a classification model on the basis of a reduced subset of spectral variables. For the purpose of variable selection, three techniques are considered, namely the successive projection algorithm (SPA), the genetic algorithm (GA), and a stepwise formulation (SW). The use of a data compression procedure in the wavelet domain is also proposed to reduce the computational workload involved in the variable selection process. The methodology is validated in a case study involving the classification of 149 Brazilian soil samples into three different orders (Argissolo, Latossolo and Nitossolo). For means of comparison, soft independent modelling of class analogy (SIMCA) models are also employed. The best discrimination of soil types was attained by SPA-LDA, which achieved an average classification rate of 90% in the validation set and 72% in cross-validation. Moreover, the proposed wavelet compression procedure was found to be of value by providing a 100-fold reduction in computational workload without significantly compromising the classification accuracy of the resulting models.
Pontes
Márcio José Coelho
MJ
Universidade Federal da Paraíba, Departamento de Química, João Pessoa, PB, Brazil.
Cortez
Juliana
J
Galvão
Roberto Kawakami Harrop
RK
Pasquini
Celio
C
Araújo
Mário César Ugulino
MC
Coelho
Ricardo Marques
RM
Chiba
Márcio Koiti
MK
de Abreu
Mônica Ferreira
MF
Madari
Beáta Emöke
BE