Abstract
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.
| Original language | English |
|---|---|
| Journal | Food Chemistry |
| Volume | 173 |
| Pages (from-to) | 1220-1227 |
| Number of pages | 8 |
| ISSN | 0308-8146 |
| DOIs | |
| Publication status | Published - 2015 |
| Externally published | Yes |
Keywords
- Chemometrics
- Conventional hardness methods
- Locally weighted partial least squares (LW-PLS) regression
- Maize hardness
- Milling quality
- Rapid Visco Analyser (RVA)
- White maize