Effect of Different Processing Parameters on Quality Factors and Image Texture Features of Bread

Abstract

In this research, image texture analysis as a nondestructive and rapid method was applied for estimation of mechanical texture property of bread. Bread samples were formulated using different emulsifiers (i.e. SSL, DATEM and E471) in three concentrations (0.2, 0.4 and 0.6%) at three proofing times (25, 35 and 45min) and sensory properties, specific volume, color components (i.e. L*, a* and b*), porosity, hardness and four image texture features (i.e. contrast, correlation, energy and homogeneity) were determined. The results indicated that emulsifier treated samples showed better (p<0.05) sensory perceptions in compression to control samples. However, the proofing time did not show significant effect. Application of E471 significantly increased the specific volume of bread. Emulsifier treated samples had higher lightness values. Application of higher concentration and longer proofing time led to higher porosity of bread. Emulsifiers, their concentrations and proofing times had positive significant (p<0.05) effects on softness of bread. On the other side of this research, contrast, correlation, energy and homogeneity were calculated from Grey Level CoOccurrence Matrix (GLCM). They showed splendid correlations with hardness of bread (0.958, 0.973, 0.966 and 0.91, respectively). Multiple Linear Regression (MLR) was conducted between hardness and four image texture features and the mathematical equation could predict hardness with high correlation of coefficient of 0.994. These results strongly suggest that image texture analysis can be applied as a nondestructive and rapid method for estimation of hardness of bread.

Texture and Color Analysis of Lentils and Rice for Instant Meal Using Image Processing Techniques

“…A high value of HOM in CR shows improvement of uniformity and smoothness of the images (Karimi et al . ). Low values in CON represent diminishment of local variation of pixels.…”

“…The GLCM shows the probability that a pixel of a particular gray level occurs at a specified direction and distance (d = 1) from its neighboring pixels. GLCM is represented by Pd,θ(i, j) where counts the neighboring pair pixels with gray values i and j at the distance of d and the direction of θ (Haralick et al 1973;Karimi et al 2012).…”

Scanning electron microscopy combined with image processing technique: Microstructure and texture analysis of legumes and vegetables for instant meal

“…HOM shows the level of uniformity on the image. High values of HOM show improvement of uniformity and smoothness of the image (Karimi et al, 2012). ENT is a measure of randomness, and takes low values for smooth images.…”

Non-destructive low-cost approach for fuzzy classification of tomato images based on firmness prediction using regression

“…The gray level co-occurrence matrix (GLCM) is a statistical and one of the most commonly used texture feature extraction method [15]. For examining texture, this method considers the spatial relationship of pixels.…”

“…However, the requirement of a distinctive imaging apparatus and cost is the major drawback concerning these techniques [13]. Simple imaging techniques using digital camera, scanners have also been adopted for products such as chicken nuggets [14], bread [15], and tea [4]. Researchers have obtained good performance in envisaging the mechanical properties of the products using image texture-based analysis techniques such as wavelet-transform, or GLCM.…”