TY - JOUR

T1 - Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation

AU - Guelpa, Anina

AU - Bevilacqua, Marta

AU - Marini, Federico

AU - O'Kennedy, Kim

AU - Geladi, Paul

AU - Manley, Marena

PY - 2015

Y1 - 2015

N2 - It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method.

AB - It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method.

KW - Chemometrics

KW - Conventional hardness methods

KW - Locally weighted partial least squares (LW-PLS) regression

KW - Maize hardness

KW - Milling quality

KW - Rapid Visco Analyser (RVA)

KW - White maize

U2 - 10.1016/j.foodchem.2014.10.149

DO - 10.1016/j.foodchem.2014.10.149

M3 - Journal article

C2 - 25466147

AN - SCOPUS:84911882208

VL - 173

SP - 1220

EP - 1227

JO - Food Chemistry

JF - Food Chemistry

SN - 0308-8146

ER -