Data/hora: 31/05/2024 10:13:52

Biblioteca(s): 	Embrapa Arroz e Feijão; Embrapa Solos.	
Data corrente: 	12/11/2009	
Data da última atualização: 	17/05/2022	
Autoria: 	PONTES, M. J. C.; CORTEZ, J.; GALVÃO, R. K. H.; PASQUINI, C.; ARAÚJO, M. C. U.; COELHO, R. M.; CHIBA, M. K.; ABREU, M. F. de; MADARI, B. E.	
Afiliação: 	MÁRCIO JOSÉ COELHO PONTES, Universidade Federal da Paraíba; JULIANA CORTEZ, Unicamp; ROBERTO KAWAKAMI HARROP GALVÃO, Instituto Tecnológico de Aeronáutica; CÉLIO PASQUINI, Unicamp; MÁRIO CÉSAR UGULINO ARAÚJO, Universidade Federal da Paraíba; RICARDO MARQUES COELHO, Instituto Agronômico de Campinas; MÁRCIO KOITI CUNHA, Instituto Agronômico de Campinas; MÔNICA FERREIRA DE ABREU, Instituto Agronômico de Campinas; BEATA EMOKE MADARI, CNPAF.	
Título: 	Classification of Brazilian soils by using LIBS and variable selection in the wavelet domain.	
Ano de publicação: 	2009	
Fonte/Imprenta: 	Analytica Chimica Acta, v. 642, n. 1/2, p. 12-18, May 2009.	
DOI: 	https://doi.org/10.1016/j.aca.2009.03.001	
Idioma: 	Inglês	
Conteúdo: 	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.	
Palavras-Chave: 	Wavelet compression.	
Thesagro: 	Classificação do solo.	
Thesaurus Nal: 	Soil classification.	
Categoria do assunto: 	P Recursos Naturais, Ciências Ambientais e da Terra	
Registro original: 	Embrapa Arroz e Feijão (CNPAF)	
Exemplares: 	CNPAF - (29382-1) - UPC - AP - PP - 2009 -  - 2009 - DIS
CNPS - (20891-1) - UPC - AP - DD -  -  -  - DIS