Digital imaging as a tool to study the structure of porous baked foods

Use of Horse Chestnut (Aesculus hippocastanum L.) Starch in Gluten-Free Cakes: Physicochemical, Nutritional, Textural Properties, and Determination of Pore Structure Using Conventional Thresholding Algorithms

Research into alternative starch sources for the production of gluten-free products continues. In this study, starch production from horse chestnut seeds was carried out using alkali and ultrasound-assisted methods, and the starches produced were used in the production of gluten-free cakes. The obtained horse chestnut starches were used in the preparation of gluten-free cakes and compared with gluten-free cakes prepared with rice, maize, and potato starches. The physical quality parameters of the gluten-free cakes were determined using image processing methods. The chemical, nutritional, and textural properties of the gluten-free cakes were also determined. Physical, chemical, nutritional, and textural properties of gluten-free cakes were determined. After 28 days of storage, the hardness values of gluten-free cakes ranged from 50.13 to 68.41 N, and the springiness values ranged from 36.28% to 47.34%. The RDS values of horse chestnut starch and gluten-free cakes were found to be 37.71% and 32.76%, respectively. The pore structures (cell count, total area, mean cell size, cell periphery, and fractal distribution) of gluten-free cakes were determined using five different thresholding algorithms (Huang, MaxEntropy, Intermodes, Isodata, and Otsu). Gluten-free cakes made with horse chestnut starch were similar to rice starch in terms of physical and textural properties, maize starch in terms of slowly digestible starch and PGI, and maize and rice starch in terms of pore structure. The Huang, Isodata, and Otsu algorithms were more effective in determining the pore structure of gluten-free cakes. These results suggest that horse chestnut starch may be a promising alternative for use in gluten-free products. PRACTICAL APPLICATION: Pore structure is one of the most important quality criteria in products such as cakes and bread. The pore structure determined by different methods is not efficient due to the disadvantages of the methods. The pore structure of cakes has been successfully determined by thresholding algorithms. Huang, Isodata, and Otsu algorithms showed more successful results. In addition, an alternative starch source for the production of gluten-free products is proposed.

Utilization of green lentil wastewater as egg replacer in green lentil flour based muffins

Successful pretreatments for green lentil wastewater (GLW) were developed to substitute egg. Water to lentil ratio and microwave pretreatment were found to affect foam and emulsion quality, while the addition of salt had no effect on foam and emulsion quality of GLW. The GLW obtained at optimum preconditions was used in the determination of best formulation for muffin quality. Oven type, green lentil flour ratio, GLW ratio leading to the maximum moisture content, volume index, total phenolic content, percent area of air cells, and minimum ΔE values with a constraint of control muffin's hardness were determined. Conventional oven baking with the formulation of 5.71% green lentil flour and 18.15% GLW produced comparable product with wheat flour and egg formulation. This study proved that discarded GLW can be used as a substitute for egg, which is an expensive ingredient in bakery.

Graphical Abstract

Identifying the quality characteristics of pork floss structure based on deep learning framework

Pork floss is a traditional Chinese food with a long history. Nowadays, pork floss is known to consumers as a leisure food. It is made from pork through a unique process in which the muscle fibers become flaky or granular and tangled. In this study, a deep learning-based approach is proposed to detect the quality characteristics of pork floss structure. Describe that the experiments were conducted using widely recognized brands of pork floss available in the grocery market, omitting the use of abbreviations. A total of 8000 images of eight commercially available pork flosses were collected and processed using sharpening, image gray coloring, real-time shading correction, and binarization. After the machine learning model learned the features of the pork floss, the images were labeled using a manual mask. The coupling of residual enhancement mask and region-based convolutional neural network (CRE-MRCNN) based deep learning framework was used to segment the images. The results showed that CRE-MRCNN could be used to identify the knot features and pore features of different brands of pork floss to evaluate their quality. The combined results of the models based on the sensory tests and machine vision showed that the pork floss from TC was the best, followed by YJJ, DD and HQ. This also shows the potential of machine vision to help people recognize the quality characteristics of pork floss structure.

Porosity determination of Vienna sausages through digital images analysis

BACKGROUND

This research aimed to determine the porosity and particle size distribution in canned Vienna-type sausages using digital image analysis (DIA) on photographs captured with a digital camera and applying a Monte Carlo simulation. The methodology determined morphometric parameters (area and Feret diameter) by DIA of transverse and longitudinal sections of canned sausages. Those images were previously contrast enhanced, color threshold adjusted, and binarized. Subsequently, the estimation of the pore volume was carried out from the inverse Gaussian distributions of Feret diameter and area, as well as the porosity, using Monte Carlo simulation.

RESULTS

The pores had an average Feret diameter of 0.335 mm and an average area of 0.085 mm² . The highest estimated bivariate kernel density was presented for the smallest pores (around 0.02 mm² in area and 0.25 mm in diameter). Simulation average values of pore volume, assumed as a cylinder, and porosity were 1.455 mm³ and 0.737 respectively. The average porosity value was consistent with the value experimentally estimated by the indirect method, in concordance with the definition of porosity, which was 0.715, presenting a mean relative percentage error of 3.08% concerning the estimated experimental value as well.

CONCLUSION

This research presents interesting perspectives for the quantitative analysis of the microstructure of food and biological materials through a novel, low-cost, reliable, and fast proposal. Moreover, this is the first study to report the porosity determination in canned sausages by DIA. © 2022 Society of Chemical Industry.

Physicochemical and Sensory Parameters of "Petipan" Enriched with Heme Iron and Andean Grain Flours

Enrichment is the addition of nutrients to a food that does not contain them naturally, which is conducted in a mandatory manner and in order to solve a nutritional deficiency in the population. Enriched petipan are products that contain heme iron. The objective of this research was to evaluate the physical, chemical, mechanical and sensory characteristics of petipan produced with Andean grain flours and heme iron concentrate. A completely randomized design (CRD) with five experimental treatments was used with different levels of heme iron. The results show the direct influence of the heme concentration level on the chromatic, mechanical and textural characteristics of petipan. As the heme concentrate increases, its mechanical properties are considerably affected, with there being a correlation between the color intensity and a considerable reduction in its porosity. Samples without heme iron (T0) and 1% heme iron concentrate (T1) present the best mechanical and sensory characteristics; however, the incorporation of heme concentrate directly influences its nutritional, textural, and mainly chromatic components.

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

One of the most widely researched topics in the food industry is bread quality analysis. Different techniques have been developed to assess the quality characteristics of bakery products. However, in the last few decades, the advancement in sensor and computational technologies has increased the use of computer vision to analyze food quality (e.g., bakery products). Despite a large number of publications on the application of imaging methods in the bakery industry, comprehensive reviews detailing the use of conventional analytical techniques and imaging methods for the quality analysis of baked goods are limited. Therefore, this review aims to critically analyze the conventional methods and explore the potential of imaging techniques for the quality assessment of baked products. This review provides an in-depth assessment of the different conventional techniques used for the quality analysis of baked goods which include methods to record the physical characteristics of bread and analyze its quality, sensory-based methods, nutritional-based methods, and the use of dough rheological data for end-product quality prediction. Furthermore, an overview of the image processing stages is presented herein. We also discuss, comprehensively, the applications of imaging techniques for assessing the quality of bread and other baked goods. These applications include studying and predicting baked goods' quality characteristics (color, texture, size, and shape) and classifying them based on these features. The limitations of both conventional techniques (e.g., destructive, laborious, error-prone, and expensive) and imaging methods (e.g., illumination, humidity, and noise) and the future direction of the use of imaging methods for quality analysis of bakery products are discussed.

Correlation of steam explosion severity with morphological and physicochemical characterization of soybean meal

Steam explosion, a novel effective technology for cereal modification, integrates high-temperature autohydrolysis and structural disruption, which can significantly influence the morphological and physicochemical characterization of the feedstocks. The deep knowledge of the structural changes that are brought about by the treatment severity is connected with the technological demands to improve the processing efficiency and to increase the industrial application of the feedstocks by steam explosion. In this study, the changes in morphological and physicochemical properties of soybean meal induced by steam explosion were investigated. The correlation of steam explosion severity with soybean meal's final quality was also analyzed. The results showed that steam explosion effectively increased the fractal dimension from 1.6553 to 1.8871, the glycinin content from 151.38 to 334.94 mg/g, and the 2,2-diphenylpicrylhydrazyl (DPPH) radical scavenging activity from 28.69 to 63.78%. The gray value, color (L* and a* values), and the total phenol and polysaccharide contents of soybean meal were reduced with greater steam explosion severity. Steam explosion severity had a remarkable positive correlation with the fractal dimension and DPPH radical scavenging activity. However, steam explosion severity had no significant correlation with the textural and adsorption properties of the soybean meal. This study focused on the morphological and physicochemical property changes of the soybean meal during a steam explosion process, which could guide the application of steam explosion in food systems.

Influence of Baking Temperature and Formulation on Physical, Sensorial, and Morphological Properties of Pogácsa Cake: An Image Analysis Study

Pogácsa is a high-demand bakery product with a unique texture, where crumb structure is a determining factor for its textural quality and consumer acceptability. At present, there is no non-destructive in-line inspection method for textural quality assessment of pogácsa. Therefore, this study was aimed to evaluate the texture of pogácsa using the image processing technique, which was prepared using different cheeses with varying moisture contents (MC) and was baked at 200 and 215 °C. Samples were assessed for textural, visual, physical, and sensorial properties. The findings indicated that the highest porosity (72.75%) was found for the sample baked at 215 °C with low-moisture cheese (58%), while the lowest porosity (32.66%) was observed for cheese-free sample baked at 200 °C. Pore volumetric ratio and MC showed strong correlations (p < 0.01) with hardness (−0.90 and −0.89), resilience (0.87 and 0.83), cohesiveness (0.84 and 0.82), springiness (0.87 and 0.90), gumminess (−0.92 and −0.92), and chewiness (−0.92 and −0.92), respectively. The pore volumetric ratio showed a strong correlation (p < 0.01) with reference porosity (0.71). Overall, the current study indicated that adding cheese with varying MC and baking temperature could affect the texture of pogácsa cake, which could be detected by image analysis.

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.

Nondestructive evaluation of baking parameters on pogácsa texture

This study aimed to investigate the potential application of image texture processing method on visible crumb structure of salty cake pogácsa, which was prepared with different baking times (5 and 7 min) and temperatures (200, 215, and 230°C). For this purpose, changes in gray level co-occurrence matrix (GLCM) features including energy, contrast, correlation, homogeneity, and entropy were monitored and their relationship with the instrumental texture parameters (hardness, adhesiveness, cohesiveness, springiness, gumminess, and chewiness) were assessed. The pore ratios were also extracted and visualized using image processing technique. Texture profile parameters indicated strong correlation (p < .01) with the image pattern parameters in different pogácsa groups. Gumminess showed strong correlation with contrast (0.503), correlation (-0.498), and homogeneity (0.401). Hardness also exhibited correlation with contrast (0.517), entropy (0.341), and correlation (-0.476). The pore ratio showed marked variation in crumb structure when different times and temperatures were used. Baking at 230°C for 7 min maximized the pore ratio (0.56). Penalty analysis revealed that oiliness, pore structure, and color of products were linked with baking time and temperature. Overall, the results suggested that the GLCM-based technique had the potential to be used as a nondestructive method for rapid quality assessment of pogácsa.