Texture appearance characterization of pre-sliced pork ham images using fractal metrics: Fourier analysis dimension and lacunarity

Effect of cooking on structural changes in the common black bean (Phaseolus vulgaris var. Jamapa)

The cooking process is fundamental for bean consumption and to increase the bioavailability of its nutritional components. The study aimed to determine the effect of cooking on bean seed coat through morphological analyses with different microscopy techniques and image analyses. The chemical composition and physical properties of raw black bean (RBB) and cooked black bean (CBB) seeds were determined. The surface and cross-sectional samples were studied by Optical microscopy (OM), environmental scanning electron microscopy (ESEM), atomic force microscopy (AFM) and confocal laser scanning microscopy (CLSM). The composition of samples showed significant differences after the cooking process. OM images and gray level co-occurrence matrix algorithm (GLCM) analysis indicated that cuticle-deposited minerals significantly influence texture parameters. Seed coat surface ESEM images showed cluster cracking. Texture fractal dimension and lacunarity parameters were effective in quantitatively assessing cracks on CBB. AFM results showed arithmetic average roughness (Ra) (121.67 nm) and quadratic average roughness (Rq) (149.94 nm). The cross-sectional ESEM images showed a decrease in seed coat thickness. The CLSM results showed an increased availability of lipids along the different multilayer tissues in CBB. The results generated from this research work offer a valuable potential to carry out a strict control of bean seed cooking at industrial level, since the structural changes and biochemical components (cell wall, lipids and protein bodies) that occur in the different tissues of the seed are able to migrate from the inside to the outside through the cracks generated in the multilayer structure that are evidenced by the microscopic techniques used.

A Review on the Commonly Used Methods for Analysis of Physical Properties of Food Materials

The chemical composition of any food material can be analyzed well by employing various analytical techniques. The physical properties of food are no less important than chemical composition as results obtained from authentic measurement data are able to provide detailed information about the food. Several techniques have been used for years for this purpose but most of them are destructive in nature. The aim of this present study is to identify the emerging techniques that have been used by different researchers for the analysis of the physical characteristics of food. It is highly recommended to practice novel methods as these are non-destructive, extremely sophisticated, and provide results closer to true quantitative values. The physical properties are classified into different groups based on their characteristics. The concise view of conventional techniques mostly used to analyze food material are documented in this work.

Statistical Analysis of Nanofiber Mat AFM Images by Gray-Scale-Resolved Hurst Exponent Distributions

Two-dimensional structures, either periodic or random, can be classified by diverse mathematical methods. Quantitative descriptions of such surfaces, however, are scarce since bijective definitions must be found to measure unique dependency between described structures and the chosen quantitative parameters. To solve this problem, we use statistical analysis of periodic fibrous structures by Hurst exponent distributions. Although such a Hurst exponent approach was suggested some years ago, the quantitative analysis of atomic force microscopy (AFM) images of nanofiber mats in such a way was described only recently. In this paper, we discuss the influence of typical AFM image post-processing steps on the gray-scale-resolved Hurst exponent distribution. Examples of these steps are polynomial background subtraction, aligning rows, deleting horizontal errors and sharpening. Our results show that while characteristic features of these false-color images may be shifted in terms of gray-channel and Hurst exponent, they can still be used to identify AFM images and, in the next step, to quantitatively describe AFM images of nanofibrous surfaces. Such a gray-channel approach can be regarded as a simple way to include some information about the 3D structure of the image.

Multifractal analysis application to the study of fat and its infiltration in Iberian ham: Influence of racial and feeding factors and type of slicing

This paper explores the multifractal features of different commercial designations of Iberian ham (acorn 100% Iberian ham, acorn Iberian ham, feed/pasture Iberian ham and feed Iberian ham). This study has been done by taking as input the fatty infiltration patterns obtained from digital image analysis of ham cuts comparing mechanic and manual slicing. The yielded results show the multifractal nature of fatty connective tissue in Iberian ham, only when knife cutting is applied, confirming the differences between the designations according to their genetics and feeding. Thus, the multifractal parameters presented in this work could be considered as additional information for checking Iberian ham quality by using non-destructive methods based on the combination of image analysis and predictive techniques. Meat industry can take advantage of these methods to evaluate meat products, especially when fat-connective tissue with complex pattern distribution is involved.

Gradual Reduction in Sodium Content in Cooked Ham, with Corresponding Change in Sensorial Properties Measured by Sensory Evaluation and a Multimodal Machine Vision System

The European diet today generally contains too much sodium (Na(+)). A partial substitution of NaCl by KCl has shown to be a promising method for reducing sodium content. The aim of this work was to investigate the sensorial changes of cooked ham with reduced sodium content. Traditional sensorial evaluation and objective multimodal machine vision were used. The salt content in the hams was decreased from 3.4% to 1.4%, and 25% of the Na(+) was replaced by K(+). The salt reduction had highest influence on the sensory attributes salty taste, after taste, tenderness, hardness and color hue. The multimodal machine vision system showed changes in lightness, as a function of reduced salt content. Compared to the reference ham (3.4% salt), a replacement of Na(+)-ions by K(+)-ions of 25% gave no significant changes in WHC, moisture, pH, expressed moisture, the sensory profile attributes or the surface lightness and shininess. A further reduction of salt down to 1.7-1.4% salt, led to a decrease in WHC and an increase in expressible moisture.

The spatial distribution of β-carotene impregnated in apple slices determined using image and fractal analysis

Changes in the concentration profiles of β-carotene caused by diffusion through parenchymatic dried apple tissue were characterized by image and fractal analysis. Apple slices were dried by convection, and then impregnated with an aqueous β-carotene solution. Scanning electron microscopy images of dried apple slices were captured and the fractal dimension (FD) values of the textures of the images were obtained (FDSEM). It was observed that the microstructure of the foodstuff being impregnated have an important effect on the impregnation phenomenon, generating irregular concentration profiles of β-carotene, which are numerically described by the fractal dimension FDPROFILES and are related to the diffusion process during impregnation in dried edible tissue.

Changes of the water-holding capacity and microstructure of panga and tilapia surimi gels using different stabilizers and processing methods

Surimi gel is a food product traditionally manufactured from marine species; it has functional features including a specific texture and a high protein concentration . The objective of this study was to evaluate and compare the effect of the ultrasound extraction protein method and different stabilizers on the water-holding capacity (WHC), texture, and microstructure of surimi from panga and tilapia to potentially increase the value of these species. For this purpose, WHC was determined and texture profile analysis, scanning electron microscopy, and texture image analysis were carried out. The results showed that the ultrasound method and the sodium citrate can be used to obtain surimi gels from panga and tilapia with optimal textural properties such as the hardness and chewiness. Moreover, image analysis is recommended as a quantitative and non-invasive technique to evaluate the microstructure and texture image properties of surimis prepared using different processing methods and stabilizers.

Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection

A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets.