Data/hora: 28/05/2024 22:25:27

Biblioteca(s): 	Embrapa Solos.	
Data corrente: 	01/09/2021	
Data da última atualização: 	08/11/2021	
Autoria: 	SANTANA, F. B. de; OTANI, S. K.; SOUZA, A. M. de; POPPI, R. J.	
Afiliação: 	FELIPE B. DE SANTANA, UNICAMP; SANDRO K. OTANI, UNICAMP; ANDRE MARCELO DE SOUZA, CNPS; RONEI J. POPPI, UNICAMP.	
Título: 	Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries.	
Ano de publicação: 	2021	
Fonte/Imprenta: 	Geoderma Regional, v. 27, e00436, Dec. 2021.	
DOI: 	https://doi.org/10.1016/j.geodrs.2021.e00436	
Idioma: 	Inglês	
Conteúdo: 	In this study a systematic comparison was carried out to assess differences on the accuracy between partial least squares (PLS) and support vector machine (SVM) regression algorithms in soil organic matter and particle size determinations using vis-NIR spectroscopy. The comparison consisted in investigating the influence on the size of calibration set on the external validation set accuracy. For this purpose, three vis-NIR soil libraries containing 14,212, 15,330 and 42,471 soil samples were used to determine sand, clay, and SOM content, respectively. To increase the variability of the results obtained, each calibration subset was randomly generated 49 times and for each iteration a PLS, SVM-Linear and SVM-RBF (radial basis function) regression models were built. These calibration subsets were composed by 250, 1000, 2000, 5000 and 8000 or 10,000 samples.	
Palavras-Chave: 	Carbono orgânico do solo; Machine learning; Molecular spectroscopy; Soil spectral library.	
Thesagro: 	Textura do Solo.	
Thesaurus Nal: 	Soil organic carbon; Soil texture.	
Categoria do assunto: 	P Recursos Naturais, Ciências Ambientais e da Terra	
Registro original: 	Embrapa Solos (CNPS)	
Exemplares: 	CNPS - (20718-1) - UPC - AP - DD -  -  -  - DIS