High pressure/thermal treatment effects on the texture of beef muscle

The effects of high pressure (to 800 MPa) applied at different temperatures (20-70 degreesC) for 20 min on beef post-rigor longissimus dorsi texture were studied. Texture profile analysis showed that when heated at ambient pressure there was the expected increase in hardness with increasing temperature and when pressure was applied at room temperature there was again the expected increase in hardness with increasing pressure. Similar results to those found at ambient temperature were found when pressure was applied at 40 degreesC. However, at higher temperatures, 60 and 70 degreesC it was found that pressures of 200 MPa caused large and significant decreases in hardness. The results found for hardness were mirrored by those for gumminess and chewiness. To further understand the changes in texture observed, intact beef longissimus dorsi samples and extracted myofibrils were both subjected to differential scanning calorimetry after being subjected to the same pressure/temperature regimes. As expected collagen was reasonably inert to pressure and only at temperatures of 60-70 degreesC was it denatured/unfolded. However, myosin was relatively easily unfolded by both pressure and temperature and when pressure denatured a new and modified structure was formed of low thermal stability. Although this new structure had low thermal stability at ambient pressure it still formed in both the meat and myofibrils when pressure was applied at 60 degreesC. It seems unlikely that structurally induced changes can be a major cause of the significant loss of hardness observed when beef is treated at high temperature (60-70 degreesC) and 200 MPa and it is suggested that accelerated proteolysis under these conditions is the major cause. (C) 2004 Elsevier Ltd. All rights reserved.

Texture segmentation in Williams syndrome

Williams syndrome (WS) is a developmental disorder in which visuo-spatial cognition is poor relative to verbal ability. At the level of visuo-spatial perception, individuals with WS can perceive both the local and global aspects of an image. However, the manner in which local elements are integrated into a global whole is atypical, with relative strengths in integration by luminance, closure, and alignment compared to shape, orientation and proximity. The present study investigated the manner in which global images are segmented into local parts. Segmentation by seven gestalt principles was investigated: proximity, shape, luminance, orientation, closure, size (and alignment: Experiment I only). Participants were presented with uniform texture squares and asked to detect the presence of a discrepant patch (Experiment 1) or to identify the form of a discrepant patch as a capital E or H (Experiment 2). In Experiment 1, the pattern and level of performance of the WS group did not differ from that of typically developing controls, and was commensurate with the general level of non-verbal ability observed in WS. These results were replicated in Experiment 2, with the exception of segmentation by proximity, where individuals with WS demonstrated superior performance relative to the remaining segmentation types. Overall, the results suggest that, despite some atypical aspects of visuo-spatial perception in WS, the ability to segment a global form into parts is broadly typical in this population. In turn, this informs predictions of brain function in WS, particularly areas V1 and V4. (c) 2006 Elsevier Ltd. All rights reserved.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

Classification-based probabilistic modeling of texture transition for fast line search tracking and delineation

We introduce a classification-based approach to finding occluding texture boundaries. The classifier is composed of a set of weak learners, which operate on image intensity discriminative features that are defined on small patches and are fast to compute. A database that is designed to simulate digitized occluding contours of textured objects in natural images is used to train the weak learners. The trained classifier score is then used to obtain a probabilistic model for the presence of texture transitions, which can readily be used for line search texture boundary detection in the direction normal to an initial boundary estimate. This method is fast and therefore suitable for real-time and interactive applications. It works as a robust estimator, which requires a ribbon-like search region and can handle complex texture structures without requiring a large number of observations. We demonstrate results both in the context of interactive 2D delineation and of fast 3D tracking and compare its performance with other existing methods for line search boundary detection.

Interaction of visual and haptic information in simulated environments: texture perception

This paper describes experiments relating to the perception of the roughness of simulated surfaces via the haptic and visual senses. Subjects used a magnitude estimation technique to judge the roughness of “virtual gratings” presented via a PHANToM haptic interface device, and a standard visual display unit. It was shown that under haptic perception, subjects tended to perceive roughness as decreasing with increased grating period, though this relationship was not always statistically significant. Under visual exploration, the exact relationship between spatial period and perceived roughness was less well defined, though linear regressions provided a reliable approximation to individual subjects’ estimates.

Evaluating mid-infrared spectroscopy as a new technique for predicting sensory texture attributes of processed cheese

The objective of this study was to investigate the potential application of mid-infrared spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates mid-infrared spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n = 32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4 degrees C, mid-infrared spectra ( 640 to 4,000 cm(-1)) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range ( 930 to 1,767 cm(-1)). The remaining attributes were most successfully modeled using a combined range ( 930 to 1,767 cm(-1) and 2,839 to 4,000 cm(-1)). The root mean square errors of cross-validation for the models were 7.4(firmness; range 65.3), 4.6 ( rubbery; range 41.7), 7.1 ( creamy; range 60.9), 5.1(chewy; range 43.3), 5.2(mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 ( melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions ( range error ratio = 9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that mid-infrared spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality..

Prediction of processed cheese instrumental texture and meltability by mid-infrared spectroscopy coupled with chemometric tools

The objective of this study was to determine the potential of mid-infrared spectroscopy coupled with multidimensional statistical analysis for the prediction of processed cheese instrumental texture and meltability attributes. Processed cheeses (n = 32) of varying composition were manufactured in a pilot plant. Following two and four weeks storage at 4 degrees C samples were analysed using texture profile analysis, two meltability tests (computer vision, Olson and Price) and mid-infrared spectroscopy (4000-640 cm(-1)). Partial least squares regression was used to develop predictive models for all measured attributes. Five attributes were successfully modelled with varying degrees of accuracy. The computer vision meltability model allowed for discrimination between high and low melt values (R-2 = 0.64). The hardness and springiness models gave approximate quantitative results (R-2 = 0.77) and the cohesiveness (R-2 = 0.81) and Olson and Price meltability (R-2 = 0.88) models gave good prediction results. (c) 2006 Elsevier Ltd. All rights reserved..

Application of mid-infrared spectroscopy to the prediction of maturity and sensory texture attributes of cheddar cheese

The objective of this study was to determine the potential of mid-infrared spectroscopy in conjunction with partial least squares (PLS) regression to predict various quality parameters in cheddar cheese. Cheddar cheeses (n = 24) were manufactured and stored at 8 degrees C for 12 mo. Mid-infrared spectra (640 to 4000/cm) were recorded after 4, 6, 9, and 12 mo storage. At 4, 6, and 9 mo, the water-soluble nitrogen (WSN) content of the samples was determined and the samples were also evaluated for 11 sensory texture attributes using descriptive sensory analysis. The mid-infrared spectra were subjected to a number of pretreatments, and predictive models were developed for all parameters. Age was predicted using scatter-corrected, 1st derivative spectra with a root mean square error of cross-validation (RMSECV) of 1 mo, while WSN was predicted using 1st derivative spectra (RMSECV = 2.6%). The sensory texture attributes most successfully predicted were rubbery, crumbly, chewy, and massforming. These attributes were modeled using 2nd derivative spectra and had, corresponding RMSECV values in the range of 2.5 to 4.2 on a scale of 0 to 100. It was concluded that mid-infrared spectroscopy has the potential to predict age, WSN, and several sensory texture attributes of cheddar cheese..

Texture, realism, performance: exploring the intersection of transtexts and the contemporary sitcom

Contemporary US sitcom is at an interesting crossroads: it has received an increasing amount of scholarly attention (e.g. Mills 2009; Butler 2010; Newman and Levine 2012; Vermeulen and Whitfield 2013), which largely understands it as shifting towards the aesthetically and narratively complex. At the same time, in the post-broadcasting era, US networks are particularly struggling for their audience share. With the days of blockbuster successes like Must See TV’s Friends (NBC 1994-2004) a distant dream, recent US sitcoms are instead turning towards smaller, engaged audiences. Here, a cult sensibility of intertextual in-jokes, temporal and narrational experimentation (e.g. flashbacks and alternate realities) and self-reflexive performance styles have marked shows including Community (NBC 2009-2015), How I Met Your Mother (CBS 2005-2014), New Girl (Fox 2011-present) and 30 Rock (NBC 2006-2013). However, not much critical attention has so far been paid to how these developments in textual sensibility in contemporary US sitcom may be influenced by, and influencing, the use of transmedia storytelling practices, an increasingly significant industrial concern and rising scholarly field of enquiry (e.g. Jenkins 2006; Mittell 2015; Richards 2010; Scott 2010; Jenkins, Ford and Green 2013). This chapter investigates this mutual influence between sitcom and transmedia by taking as its case studies two network shows that encourage invested viewership through their use of transtexts, namely How I Met Your Mother (hereafter HIMHM) and New Girl (hereafter NG). As such, it will pay particular attention to the most transtextually visible character/actor from each show: HIMYM’s Barney Stinson, played by Neil Patrick Harris, and NG’s Schmidt, played by Max Greenfield. This chapter argues that these sitcoms do not simply have their particular textual sensibility and also (happen to) engage with transmedia practices, but that the two are mutually informing and defining. This chapter explores the relationships and interplay between sitcom aesthetics, narratives and transmedia storytelling (or industrial transtexts), focusing on the use of multiple delivery channels in order to disperse “integral elements of a fiction” (Jenkins, 2006 95-6), by official entities such as the broadcasting channels. The chapter pays due attention to the specific production contexts of both shows and how these inform their approaches to transtexts. This chapter’s conceptual framework will be particularly concerned with how issues of texture, the reality envelope and accepted imaginative realism, as well as performance and the actor’s input inform and illuminate contemporary sitcoms and transtexts, and will be the first scholarly research to do so. It will seek out points of connections between two (thus far) separate strands of scholarship and will move discussions on transtexts beyond the usual genre studied (i.e. science-fiction and fantasy), as well as make a contribution to the growing scholarship on contemporary sitcom by approaching it from a new critical angle. On the basis that transmedia scholarship stands to benefit from widening its customary genre choice (i.e. telefantasy) for its case studies and from making more use of in-depth close analysis in its engagement with transtexts, the chapter argues that notions of texture, accepted imaginative realism and the reality envelope, as well as performance and the actor’s input deserve to be paid more attention to within transtext-related scholarship.

Texture analysis and classification using deterministic tourist walk

In this paper, we present a study on a deterministic partially self-avoiding walk (tourist walk), which provides a novel method for texture feature extraction. The method is able to explore an image on all scales simultaneously. Experiments were conducted using different dynamics concerning the tourist walk. A new strategy, based on histograms. to extract information from its joint probability distribution is presented. The promising results are discussed and compared to the best-known methods for texture description reported in the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Texture analysis using graphs generated by deterministic partially self-avoiding walks

Texture is one of the most important visual attributes for image analysis. It has been widely used in image analysis and pattern recognition. A partially self-avoiding deterministic walk has recently been proposed as an approach for texture analysis with promising results. This approach uses walkers (called tourists) to exploit the gray scale image contexts in several levels. Here, we present an approach to generate graphs out of the trajectories produced by the tourist walks. The generated graphs embody important characteristics related to tourist transitivity in the image. Computed from these graphs, the statistical position (degree mean) and dispersion (entropy of two vertices with the same degree) measures are used as texture descriptors. A comparison with traditional texture analysis methods is performed to illustrate the high performance of this novel approach. (C) 2011 Elsevier Ltd. All rights reserved.

Texture analysis based on maximum contrast walker

Recently, the deterministic tourist walk has emerged as a novel approach for texture analysis. This method employs a traveler visiting image pixels using a deterministic walk rule. Resulting trajectories provide clues about pixel interaction in the image that can be used for image classification and identification tasks. This paper proposes a new walk rule for the tourist which is based on contrast direction of a neighborhood. The yielded results using this approach are comparable with those from traditional texture analysis methods in the classification of a set of Brodatz textures and their rotated versions, thus confirming the potential of the method as a feasible texture analysis methodology. (C) 2010 Elsevier B.V. All rights reserved.