Articles
Modelling basil (Ocimum basilicum) visual quality as affected by storage temperature
Article number
1311_7
Pages
55 – 60
Language
English
Abstract
Visual quality is the main sensory quality factor limiting sweet basil (Ocimum basilicum L.) shelf life.
The aim of the present study was the development of a model for the prediction of basil visual quality depending on storage temperature and time.
Commercial clamshell recycled polyethylene tetraphyte (RPET) packages containing 20 g of basil were stored at eight different temperatures (2, 4, 7, 10, 12, 15, 22, 30°C). Five packages were stored at each temperature.
Basil packages were evaluated daily until the end of shelf-life by a panel composed by trained members.
Different visual quality characteristics were evaluated (yellowing, chilling injury, mold growth, firmness) to give an overall visual quality score using a hedonic scale.
The experiment was repeated three times.
Data on the overall visual quality of basil were used for the development of a model for the prediction of basil visual quality decay at different temperatures.
First, the kinetics of visual quality deterioration was characterized.
Visual quality as a function of time was modelled by a linear, quadratic, logistic, and exponential equations.
Based on the goodness-of-fit statistic (R2) and model complexity the most suitable primary model was chosen.
The parameter (decay rate) of this model was fitted by a secondary model to describe the decay rate as a function of temperature.
As the data set was limited, primary and secondary Modelling was performed in one step on the global data set.
The model was fitted using nonlinear regression.
A linear model was selected to model visual quality as a function of time taking into account the complexity of the models tested.
A quadratic function was selected to describe the quality decay rate as a function of storage temperature.
The coefficient of determination (R2) of the obtained model was 0.71. The maximum shelf life of 6 days is predicted at 15°C. The model should be validated under dynamic temperature conditions.
The aim of the present study was the development of a model for the prediction of basil visual quality depending on storage temperature and time.
Commercial clamshell recycled polyethylene tetraphyte (RPET) packages containing 20 g of basil were stored at eight different temperatures (2, 4, 7, 10, 12, 15, 22, 30°C). Five packages were stored at each temperature.
Basil packages were evaluated daily until the end of shelf-life by a panel composed by trained members.
Different visual quality characteristics were evaluated (yellowing, chilling injury, mold growth, firmness) to give an overall visual quality score using a hedonic scale.
The experiment was repeated three times.
Data on the overall visual quality of basil were used for the development of a model for the prediction of basil visual quality decay at different temperatures.
First, the kinetics of visual quality deterioration was characterized.
Visual quality as a function of time was modelled by a linear, quadratic, logistic, and exponential equations.
Based on the goodness-of-fit statistic (R2) and model complexity the most suitable primary model was chosen.
The parameter (decay rate) of this model was fitted by a secondary model to describe the decay rate as a function of temperature.
As the data set was limited, primary and secondary Modelling was performed in one step on the global data set.
The model was fitted using nonlinear regression.
A linear model was selected to model visual quality as a function of time taking into account the complexity of the models tested.
A quadratic function was selected to describe the quality decay rate as a function of storage temperature.
The coefficient of determination (R2) of the obtained model was 0.71. The maximum shelf life of 6 days is predicted at 15°C. The model should be validated under dynamic temperature conditions.
Authors
F. López-Gálvez, A. Vermeulen, M. Eriksson, P. Ragaert, F. Devlieghere
Keywords
fresh herb, sensory quality, deterioration, post-harvest, predictive model
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