Evaluation of Image Analysis Tools for Characterization of Sweet Bread Crumb Structure
Many approaches to evaluate bread crumb features by applying free or at least not too expensive image analysis (IA) software have been published; however, the described procedures showed noticeable differences. The aim of this work was to compare different image scanning resolutions and thresholding techniques to quantify sweet bread crumb features (cell density, mean cell area, shape factor) and their relation with fractal dimension. Two sets of experiments were carried out, one to determine the effect of scanning resolution and thersholding method and the other to validate the previous results by evaluating breads with different crumb structures. Nine different scanning resolutions (75, 100, 150, 200, 300, 355, 435, 515, 555 dpi) and two segmentation procedures (Otsu and Manual) were tested. Three different types of commercial sweet breads and a yeasted sweet bread added with different concentrations (six, 12%) of Chia flour (Salvia hispanica) were evaluated. Results showed that the percentage of particles with areas between 0.1 and 4.0 mm2 remained almost constant when using 350 dpi or larger resolution values, while the smallest particles (<0.1 mm2) increased their proportion up to 87% at the highest scanning resolution for both thresholding methods. IA was useful to detect crumb structure differences among commercial breads and breads added with Chia flour as obtained from cell density (154 ± 4.6–246 ± 2.5) and mean cell area (0.81 ± 0.02–0.7 ± 0.03) results. However, the number of selected objects to calculate these parameters produced different results. The addition of 6% of Chia flour did not affect the bread crumb features, while at the largest proportion more and smaller pores were obtained. Fractal texture was useful to evaluate bread crumb structure, as it not depends on the number of particles detected.
KeywordsImage analysis Sweet bread Crumb structure Fractal dimension of texture
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