Comparison of Partial Least Squares and Artificial Neural Network for the prediction of antioxidant activity in extract of Pegaga (Centella) varieties from 1H Nuclear Magnetic Resonance spectroscopy

Food Research International

Volumn 54, Issue 1, 2013, Pages 852-860

  Maulidiani, ^a   Abas, Faridah ^a   Khatib, Alfi ^a,b   Shitan, Mahendran ^a   Shaari, Khozirah ^a   Lajis, Nordin H ^a,c  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

^b INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA   (Malaysia)

^c TAIBAH UNIVERSITY   (Saudi Arabia)

Author keywords

1H Nuclear Magnetic Resonance; Anti oxidant activity; Artificial Neural Network; Centella asiatica; Partial Least Square regression

Indexed keywords

ANTI-OXIDANT ACTIVITIES; CENTELLA ASIATICA; CHLOROGENIC ACIDS; MULTIVARIATE DATA ANALYSIS; PARTIAL LEAST SQUARE (PLS); PARTIAL LEAST SQUARE REGRESSION; PEGAGA VARIETIES; REGRESSION COEFFICIENT;

COMPLEXATION; FLAVONOIDS; FORECASTING; LEAST SQUARES APPROXIMATIONS; MULTIVARIANT ANALYSIS; NEURAL NETWORKS; NUCLEAR MAGNETIC RESONANCE; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PRINCIPAL COMPONENT ANALYSIS; VALUE ENGINEERING;

PLANTS (BOTANY);

EID: 84884384458     PISSN: 09639969     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foodres.2013.08.029     Document Type: Article

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