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Abstract
In this paper, digital image processing techniques are applied to measure some of the quality parameters of the durum wheat semolina. One of these parameters is the semolina colour value in the lab colour space L*a*b*, which is the commonly employed colour space in food field. Several numerical methods are developed and analysed for mapping the RGB digital images to L*a*b*. These methods are direct, polynomial regression, and neural network methods. The accuracy of each method is obtained with respect to the measured L*a*b* values captured with a Chroma-Meter instrument. The numerical models outcomes showed lowest colour deviations of 0.72. The results also demonstrated a significant effect of the training data set on the numerical L*a*b* outputs. Moreover, a partial least-squares regression model was developed to numerically predict the β–carotene content in semolina, as another important quality parameter. The model proved a correlation coefficient of 0.94 between numerical predictions and experimental measurements according to the ICC standard method 152 for extracting the durum carotenoids, thus bears a high potential for facilitating carotene detection in durum.
Highlights
Food colour is the primary quality parameter checked by consumers, who use it as a tool to accept or reject the product
The best performance was achieved with 10 hidden layer neurons that had the lower mean square error (MSE) of 2.32x10-5
A data set of 50 semolina samples was divided into 80% (40 samples) for training the regression model and 20% (10 samples) for validating the model outputs
Summary
Food colour is the primary quality parameter checked by consumers, who use it as a tool to accept or reject the product. The colour has been widely demonstrated that it correlates with physical, chemical and sensorial indicators of product quality [1]. The colour measuring instruments (colorimeters) are commonly used since they are measuring the colours values mapped on a device-independent colour space with the coordinate L* for the lightness and a* and b* the colour-opponent dimensions. Some of these instruments require measuring samples that have extremely smooth and flat surfaces to avoid the dissipation of light in/out the measured area under the device prop. The flatness and smoothness of the tested samples is not guaranteed as well the accuracy of the colour measurements
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