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Journal of Analytical Atomic Spectrometry

Volumn 27, Issue 1, 2012, Pages 92-98

Comparison of principal components regression, partial least squares regression, multi-block partial least squares regression, and serial partial least squares regression algorithms for the analysis of Fe in iron ore using LIBS

(3) Yaroshchyk, P a Death, D L a Spencer, S J a

a CSIRO (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DATA BLOCKS; DATA-DRIVEN; FE CONTENT; LATENT VARIABLE; PARTIAL LEAST SQUARES; PARTIAL LEAST SQUARES REGRESSION; PREDICTION ACCURACY; PREDICTIVE ALGORITHMS; PRINCIPAL COMPONENTS; SPECTRAL DATA; SPECTRAL FEATURE; UV- AND;

ALGORITHMS; ATOMIC EMISSION SPECTROSCOPY; FEATURE EXTRACTION; IRON ORES; IRON RESEARCH; MULTIVARIANT ANALYSIS; ORE ANALYSIS; REGRESSION ANALYSIS; SPECTROMETERS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 83455212190 PISSN: 02679477 EISSN: 13645544 Source Type: Journal
DOI: 10.1039/c1ja10164a Document Type: Conference Paper

Times cited : (89)

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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.