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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

Non-Contact Measurement of Cereal Quality by Image Sensing and Numerical Regression Techniques