Different approaches in Partial Least Squares and Artificial Neural Network models applied for the analysis of a ternary mixture of Amlodipine, Valsartan and Hydrochlorothiazide

Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy

Volumn 122, Issue , 2014, Pages 744-750

  Darwish, Hany W ^a,b   Hassan, Said A ^a   Salem, Maissa Y ^a   El Zeany, Badr A ^a  

^a CAIRO UNIVERSITY   (Egypt)

^b KING SAUD UNIVERSITY   (Saudi Arabia)

Author keywords

Amlodipine; ANN; GA; Hydrochlorothiazide; PLS; Valsartan

Indexed keywords

AMLODIPINE; ANN; HYDROCHLOROTHIAZIDE; PLS; VALSARTAN;

DATA COMPRESSION; GALLIUM; LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

MIXTURES;

AMLODIPINE; ANTIHYPERTENSIVE AGENT; DRUG DERIVATIVE; HYDROCHLOROTHIAZIDE; TETRAZOLE DERIVATIVE; VALINE; VALSARTAN;

ANN; ARTICLE; ARTIFICIAL NEURAL NETWORK; DRUG DOSAGE FORM; GA; PLS; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; ULTRAVIOLET SPECTROPHOTOMETRY;

AMLODIPINE; ANN; GA; HYDROCHLOROTHIAZIDE; PLS; VALSARTAN;

AMLODIPINE; ANTIHYPERTENSIVE AGENTS; DOSAGE FORMS; HYDROCHLOROTHIAZIDE; LEAST-SQUARES ANALYSIS; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; SPECTROPHOTOMETRY, ULTRAVIOLET; TETRAZOLES; VALINE;

EID: 84891621227     PISSN: 13861425     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.saa.2013.11.045     Document Type: Article

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