Pure Texture Research Articles

Effects of bacterial cellulose/thyme essential oil emulsion coating on the shelf life of chilled chicken meat.

Bacterial cellulose (BC) is a fiber substance produced by microbial fermentation. It is widely used in the food preservation industry because of its extremely pure texture, high crystallinity and high biocompatibility. In the present study, bacterial cellulose/thyme essential oil (BC/TEO-E) with antibacterial and fresh-keeping functions was prepared by ultrasonic treatment of modified bacterial cellulose for encapsulation of thyme essential oil, which effectively inhibited the spoilage of chilled chicken. The purified BC, produced by Acetobacter xylinum ATCC 53524, was ultrasonically treated wih different times (0, 30, 60 and 90 min). Transmission electron microscopy, scanning electron microscopy, Fourier transformed infrared spectroscopy, X-ray diffraction, differential scanning calorimetry and zeta potential were used to characterize the structure of BC after ultrasound, showing that BC, treated for 30 min, had the optimal fiber structure, crystallinity (85.8%), thermal stability (347.77 °C) and solution stability (-26.63 ± 1.96 mV). BC/TEO-E was prepared by a homogenizer for the preservation of chilled chicken. Optical microscopy indicated that the BC/TEO-E prepared by 0.5% BC had optimal dispersion and stability, and even no delamination was observed in the emulsion. Compared with other groups (control, 0.5% BC and Tween-E), the total number of colonies and coliforms in chilled chicken treated with 0.5% BC/TEO-E was the lowest during the whole storage period (12 days), indicating that it can effectively inhibit bacterial growth. In addition, total volatile base nitrogen (TVB-N), thiobarbituric acid reactive substances, pH and drip loss results showed that 0.5% BC/TEO-E could effectively inhibit the spoilage of chilled chicken compared to the other treatment groups. All of the results acquired in the present study indicate that BC/TEO-E has a potential application in chilled chicken preservation. © 2024 Society of Chemical Industry.

Impact of a low water input, genotype and process on the texture of purees from two tomato cultivars

Impact of a low water input, genotype and process on the texture of purees from two tomato cultivars

Whole Tumor Radiomics Analysis for Risk Factors Associated With Rapid Growth of Vestibular Schwannoma in Contrast-Enhanced T1-Weighted Images

To investigate the features associated with rapid growth of vestibular schwannoma using radiomics analysis on magnetic resonance imaging (MRI) together with clinical factors. From August 2005 to February 2019, 67 patients with vestibular schwannoma underwent contrast-enhanced T1-weighted MRI at least twice as part of their diagnosis. After excluding 3 cases with an extremely short follow-up period of 15 days or less, 64 patients were finally enrolled in this study. Ninety-three texture features were extracted from the tumor image data using 3D Slicer software (http://www.slicer.org/). We determined the texture features that significantly affected maximal tumor diameter growth of more than 2 mm/year using Random Forest and Bounty. We also analyzed age and tumor size as clinical factors. We calculated the areas under the curve (AUCs) using receiver operating characteristic analysis for prediction models using texture, clinical, and mixed factors by Random Forest and 5-fold cross-validation. Two texture features, low minimum signal and high inverse difference moment normalized (Idmn), were significantly associated with rapid growth of vestibular schwannoma. The mixed model of texture features and clinical factors offered the highest AUC (0.69), followed by the pure texture (0.67), and pure clinical (0.63) models. The minimum signal was the most important variable followed by tumor size, Idmn, and age. Our radiomics analysis found that texture features were significantly associated with the rapid growth of vestibular schwannoma in contrast-enhanced T1-weighted images. The mixed model offered a higher diagnostic performance than the pure texture or clinical models.

AUTOMATIC ENRICHMENT OF INDOOR 3D MODELS USING A DEEP LEARNING APPROACH BASED ON SINGLE IMAGES WITH UNKNOWN CAMERA POSES

Abstract. 3D building modeling is a diverse field of research with a multitude of challenges, where data integration is an inherent component. The intensively growing market of BIM-related consumer applications requires methods and algorithms that enable efficient updates of existing 3D models without the need for cost-intensive data capturing and repetitive reconstruction processes. We propose a novel approach for semantic enrichment of existing indoor models by window objects, based on amateur camera RGB images with unknown exterior orientation parameters. The core idea of the approach is the parallel estimation of image camera poses with semantic recognition of target objects and their automatic mapping onto a 3D vector model. The presented solution goes beyond pure texture matching and links deep learning detection techniques with camera pose estimation and 3D reconstruction. To evaluate the performance of our procedure, we compare the estimated camera parameters with reference data, obtaining median values of 13.8 cm for the camera position and 1.1° for its orientation. Furthermore, a quality of 3D mapping is assessed based on the comparison to the reference 3D point cloud. All the windows presented in the data source were detected successfully, with a mean distance between both point sets equal to 3.6 cm. The experimental results prove that the presented approach achieves accurate integration of objects extracted from single images with an input 3D model, allowing for an effective increase of its semantic coverage.

Mid-infrared technique to forecast cooked puree properties from raw apples: A potential strategy towards sustainability and precision processing

The potential of MIRS was investigated to: i) differentiate cooked purees issued from different apples and process conditions, and ii) predict the puree quality characteristics from the spectra of homogenized raw apples. Partial least squares (PLS) regression was tested both, on the real spectra of cooked purees and their reconstructed spectra calculated from the spectra of homogenized raw apples by direct standardization. The cooked purees were well-classified according to apple thinning practices and cold storage durations, and to different heating and grinding conditions. PLS models using the spectra of homogenized raw apples can anticipate the titratable acidity (the residual predictive deviation (RPD) = 2.9), soluble solid content (RPD = 2.8), particle averaged size (RPD = 2.6) and viscosity (RPD ≥ 2.5) of cooked purees. MIR technique can provide sustainable evaluations of puree quality, and even forecast texture and taste of purees based on the prior information of raw materials.

Fresh, freeze-dried or cell wall samples: Which is the most appropriate to determine chemical, structural and rheological variations during apple processing using ATR-FTIR spectroscopy?

Attenuated total reflectance Fourier transform spectroscopy (ATR-FTIR) was applied on fresh (NF), freeze-dried (FD) and cell wall materials (AIS) of raw and processed apples. These samples prepared from 36 apple sets and the corresponding 72 purees, issued from different varieties, agricultural practices, storage periods and processing conditions, were used to build models including exploratory analysis, supervised classification and multivariate calibration. Fresh and freeze-dried samples presented similar fingerprint spectral variations due to processing. ATR-FTIR directly on fresh purees satisfactorily predicted textural properties such as particle average size and volume (RPD>3.0), while freeze-drying improved assessment of chemical (RPD>3.2) and rheological (RPD>3.1) parameters using partial least-squares regression. The assessment of texture and macrocomponents of purees can be obtained with a limited sample preparation. For research applications because of a need of sample preparation, changes of cell wall composition during fruit processing could be assessed in relationship with pectin degradation.

Deep learning-based predictive imaging biomarker model for EGFR mutation status in non-small cell lung cancer from CT imaging.

3106 Background: Deep learning based radiogenomic (DLR) models present a promising performance in assisting lung cancer care. The purpose of this study was 1) To develop and validate DLR signatures to predict the EGFR mutation, 2) To assess the incremental value of these DLR signatures in comparison to the traditional clinical and semantic features. Methods: 223 patients were selected from two phase III randomized trials in patients with advanced non-squamous NSCLC with EGFR-sensitizing mutation and EGFR wild type who were planned to receive palliative therapy (trial 1: gefitinib or gefitinib plus pemetrexed and carboplatin and trial 2: pemetrexed maintenance and erlotinib maintenance). Our method is an end-to-end pipeline that requires only the manually selected tumour region in a CT image without precise tumour boundary segmentation or human-defined features. Two deep convolutional neural networks with 3D U-Net architectures are trained to segment lung masses and nodules from 3D regions of the CT image. The primary end point was EGFR prediction using Radiomics and DLR pipeline. We also compared the performance of combination of models in predicting the mutational status. Results: A total of 223 patients (mean age, 54.18 years; age range, 28–80 years) were included in this study. There were 121 (54.3%) patients with EGFR mutation and 102 (45.7%) patients who were EGFR wild type. On multivariate logistic regression analysis, Clinical variable and CT semantic features that were found to be significantly associated EGFR mutation were tumor stage, smoking status, pure solid texture, presence of non-tumor lobe nodule, and average enhancement. For predicting EGFR mutation, ROC curve plotted with clinical variables model, CT semantic variables model, Radiomics model, DLR model showed an AUC value of 0.70, 0.73, 0.94, 0.72 respectively. Clinical variables and semantic features were added to the radiomics predictive model and deep learning predictive model independently, showed further improvement in the accuracy for either model from AUC 0.94+/-0.02 to 0.96+/-0.02 and from AUC 0.72+/-0.02 to 0.82+/-0.04 respectively. Conclusions: The radiomics and DLR model by machine-learned information, extracted from CT images without precise manual segmentation, could predict EGFR mutation with very high accuracy. This AI based model can be used as non-invasive and easy-to-use surrogate imaging biomarker for EGFR mutation status prediction. Clinical trial information: CTRI/2018/10/022102 .

Baby purees elaborated with andean crops. Influence of germination and oils in physico-chemical and sensory characteristics

Baby foods must be nutritious, semi-solid and of easy digestion. Quinoa and amaranth are a good source of macro and micronutrients. Germination can improve their nutritional properties; however, their technological and sensory characteristics are affected. The aim of this work was to develop purees formulated with non-germinated and germinated Andean grain flours (NGGF and GGF, respectively), and to evaluate their nutritional, technological and sensory characteristics. Formulations (9) with potato, pumpkin, and quinoa and amaranth flours (NGGF, GGF and mix of both) were analyzed. In each formulation soybean:sunflower, canola, and sunflower:chia oils were used. Chemical composition, water activity, pH, soluble solids, color, rheology and texture of purees, and in vitro protein digestibility of grain flours, were determined. Sensory characteristics of samples were studied using projective mapping. Nutritional content of purees did not differ, but the protein digestibility of grain flours improved with germination. Soluble solids and pH with GGF were higher and lower than purees with NGGF. GGF and canola oil caused deterioration in rheological and textural properties of the purees. The sensory evaluation differentiated the purees with NGGF, GGF and mix of both; GGF worsened the sensory characteristics. Mixed flours and sunflower:chia oil were the best alternative to formulate baby purees.

ARCHETYPAL ANALYSIS: AN ALTERNATIVE TO CLUSTERING FOR UNSUPERVISED TEXTURE SEGMENTATION

Texture segmentation is one of the main tasks in image applications, specifically in remote sensing, where the objective is to segment high-resolution images of natural landscapes into different cover types. Often the focus is on the selection of discriminant textural features, and although these are really fundamental, there is another part of the process that is also influential, partitioning different homogeneous textures into groups. A methodology based on archetype analysis (AA) of the local textural measurements is proposed. AA seeks the purest textures in the image and it can find the borders between pure textures, as those regions composed of mixtures of several archetypes. The proposed procedure has been tested on a remote sensing image application with local granulometries, providing promising results.

Control of ferroelectric and linear piezoelectric response of PZT films through texture

AbstractThe high and low field ferroelectric response of freestanding PbZr0.52Ti0.48O3 (PZT) films, with texture varying from 100% (001) to 100% (111) was investigated via 500 nm thick PZT unimorphs deposited on the same substrate. It is shown that the ferroelectric and piezoelectric properties depend strongly on texture, and the effective transverse strain and stress coefficients vary linearly with %(001) and %(111) texture factors. PZT films with 100% (001) orientation displayed 150%, 140%, and 80% larger linear piezoelectric strain coefficient, saturated strain coefficient, and saturated stress coefficient, respectively, compared to films with 100% (111) orientation. As a result, pure (001) textured PZT films with 20% higher dielectric constant have 50% higher figure of merit in sensing than films with pure (111) texture. The piezoelectric and ferroelectric properties of all but one combinations of (001) and (111) texture were shown for the first time to be bounded by the values for 100% (001) and 100% (111) texture. A notable exception was PZT films comprised of 73% (001) and 27% (111) texture which showed stable piezoelectric coefficients at all electric fields, with major technological implication to low power microdevices. Finally, the coercive field was shown to decrease with (001) texture factor and excess‐Pb in the PZT and the PbTiO3 seed layer.

Information Based Universal Feature Extraction in Shallow Networks

In many real-world image based pattern recognition tasks, the extraction and usage of task-relevant features are the most crucial part of the diagnosis. In the standard approach, either the features are given by common sense like edges or corners in image analysis, or they are directly determined by expertise. They mostly remain task-specific, although human may learn the life time, and use different features too, although same features do help in recognition. It seems that a universal feature set exists, but it is not yet systematically found. In our contribution, we try to find such a universal image feature set that is valuable for most image related tasks. We trained a shallow neural network for recognition of natural and non-natural object images before different backgrounds, including pure texture and handwritten digits, using a Shannon information-based algorithm and learning constraints. In this context, the goal was to extract those features that give the most valuable information for classification of the visual objects, hand-written digits and texture datasets by a one layer network and then classify them by a second layer. This will give a good start and performance for all other image learning tasks, implementing a transfer learning approach. As result, in our case we found that we could indeed extract unique features which are valid in all three different kinds of tasks. They give classification results that are about as good as the results reported by the corresponding literature for the specialized systems, or even better ones.

Gaze behavior during 3-D face identification is depth cue invariant.

Gaze behavior during scene and object recognition can highlight the relevant information for a task. For example, salience maps-highlighting regions that have heightened luminance, contrast, color, etc. in a scene-can be used to predict gaze targets. Certain tasks, such as face recognition, result in a typical pattern of fixations on high salience features. While local salience of a 2-D feature may contribute to gaze behavior and object recognition, we are perfectly capable of recognizing objects from 3-D depth cues devoid of meaningful 2-D features. Faces can be recognized from pure texture, binocular disparity, or structure-from-motion displays (Dehmoobadsharifabadi & Farivar, 2016; Farivar, Blanke, & Chaudhuri, 2009; Liu, Collin, Farivar, & Chaudhuri, 2005), and yet these displays are devoid of local salient 2-D features. We therefore sought to determine whether gaze behavior is driven by an underlying 3-D representation that is depth-cue invariant or depth-cue specific. By using a face identification task comprising morphs of 3-D facial surfaces, we were able to measure identification thresholds and thereby equate for task difficulty across different depth cues. We found that gaze behavior for faces defined by shading and texture cues was highly comparable, but we observed some deviations for faces defined by binocular disparity. Interestingly, we found no effect of task difficulty on gaze behavior. The results are discussed in the context of depth-cue invariant representations for facial surfaces, with gaze behavior being constrained by low-level limits of depth extraction from specific cues such as binocular disparity.

Control of mechanical response of freestanding PbZr0.52Ti0.48O3 films through texture

The texture of piezoelectric lead zirconate titanate (PZT) thin films plays a key role in their mechanical response and linearity in the stress vs. strain behavior. The open circuit mechanical properties of PZT films with controlled texture varying from 100% (001) to 100% (111) were quantified with the aid of direct strain measurements from freestanding thin film specimens. The texture was tuned using a highly {111}-textured Pt substrate and excess-Pb in the PbTiO3 seed layer. The mechanical and ferroelastic properties of 500 nm thick PZT (52/48) films were found to be strongly dependent on grain orientation: the lowest elastic modulus of 90 ± 2 GPa corresponded to pure (001) texture, and its value increased linearly with the percentage of (111) texture reaching 122 ± 3 GPa for pure (111) texture. These elastic modulus values were between those computed for transversely isotropic textured PZT films by using the soft and hard bulk PZT compliance coefficients. Pure (001) texture exhibited maximum non-linearity and ferroelastic domain switching, contrary to pure (111) texture that exhibited more linearity and the least amount of switching. A micromechanics model was employed to calculate the strain due to domain switching. The model fitted well the non-linearities in the experimental stress–strain curves of (001) and (111) textured PZT films, predicting 17% and 10% of switched 90° domains that initially were favorably aligned with the applied stress in (001) and (111) textured PZT films, respectively.

Anisotropic Surface Detection Over Coastal Environment Using Near-IR LiDAR Intensity Maps

Near-IR LiDAR intensity maps have been investigated to detect surfaces characterized by spatially anisotropic properties. The developed estimators present an important sensitivity to the surface anisotropic properties (degree of anisotropy and relative anisotropy) and can be corrected for signal-to-noise ratio, which can be highly variable over coastal environments due to heterogeneous moisture distribution in space and time. Two methodologies are proposed to investigate the data in order to unambiguously characterize the surface anisotropic properties by separating the pure textural information from the amplitude weighted one, which provides a more relevant information in geomorphology than the pure texture. As an example, data acquired over sandy beaches are considered to illustrate the potential of surface characterization using both degree of anisotropy and associated texture amplitude. Sea surfaces are also investigated to show how does the pure texture approach allow to discriminate different sea states.

The significance of structural properties for the development of innovative apple puree textures

The main objectives of this work are to better understand the effect of the structural parameters on the sensory perception of apple puree's texture. For this reason, a grinding–separation procedure was established in order to vary pulp content and particle size. The validation of the procedure was made by comparing laboratory samples and commercial products. It was observed that a third parameter, serum viscosity, should be included to complete the strategy; it was therefore modified by the addition of pectin. Thus two experimental designs were constructed, the first one for the effect of the pulp content and the particle size on the sensory properties, and the second one for the effect of thickening of the continuous phase and the admixing of purees with different particle sizes. A sensory descriptive profile was conducted: the most important attributes to describe the texture of apple purees were consistency and graininess. The thickening of the continuous phase led to an increase of the consistency perception and to a decrease of the graininess perception. Pulp content, particle size and serum viscosity are key structural parameters to master the texture of apple purees.

Kinetics of temperature increase during tomato processing modulate the bioaccessibility of lycopene

The nutritional benefit of bioactive metabolites depends on their bioavailability, i.e. the proportion that leaves the food matrix, and crosses the enteral barrier to reach their cellular target. The present study focused on lycopene, the major and bioactive tomato carotenoid, the bioavailability of which is known to be enhanced in cooked products.To better understand how processing may facilitate lycopene release, we assessed whether hot-break (HB) or cold-break (CB) treatments influence the tomato lycopene bioaccessibility. HB and CB are used in the tomato industry to modulate texture of purees through endogenous cell-wall lytic enzymes activity. HB and CB processes were mimicked through microwave heating, leading to a differentiated temperature rise in the product. The HB and CB models led to the expected differences, i.e. more viscous puree for HB with low methanol. The ability of the tomato matrix to release lycopene was measured as the extractability of lycopene to oil under standardized mixing conditions. We expected that CB treatment, by enhancing cell-wall degradation, would lead to enhanced lycopene bioaccessibility.The opposite was observed: oil contained three times less lycopene when mixed by CB (around 0.3μgml−1; similar to results obtained with fresh purees) than when mixed by HB (around 0.9μgml−1), although HB caused more lycopene degradation. Kinetics studies indicated that the quick rise of temperature at the beginning of HB treatment was a key parameter.

Infrared and Visible Spectrum Study on Yellowish Green Peridot from Jinlin

10 pieces of yellowish green peridot from Jilin province with fine and pure texture were selected to analyze the relationship between frequency drift in middle-infrared band and color appearance, through the replacement of Fe2+. The actual crystal chemical formula of the samples calculated by Oxygen atom method is: (Mg1.84,Fe0.19)2.04[(Si0.982,Al0.001)0.983O4], so prove that it is chrysolite [1] and the replacement of Fe2+ and Mg2+ in the position M, which led to the yellowish green appearance. With the help of Fourier infrared spectrometer TENSOR27 and One Way ANOVA analysis study the infrared spectra of 10 samples and analyze the impact Fe2+ had on frequency drift, getting the Correlation coefficient r between Fe2+ and the wave-number near 1040cm-1, 622cm-1, 522cm-1, and the value of r are -0.896,-0.884,-0.903, therefore, with Fe2+ increasing ,three reflection peaks drift to the lower frequency. Compare the reflectivity in the visible area (360nm ~ 750nm) tested by colorimeter Color i5 with the mafic peridot end of forsterite solid solution, finding that with the reflectance peaks drift to low frequency, reflection of blue and purple light decrease, blue and purple tone in peridot decreasing; reflection of the red and orange light increases, red and orange tone increasing. It is concluded that in the transformation of mafic Peridot end to chrysolite peridot end, reflection peaks in infrared band drift to lower frequency, causing the increase of red and orange tone, decrease of blue and purple tone in Peridot.

Development of highly cube textured nickel superconductor substrate tapes by Accumulative Roll Bonding (ARB)

Abstract Development of cube texture ({001}〈100〉) has been investigated in pure Ni (99.97%) deformed to ultrahigh straining (e = 6.4) by accumulative roll bonding and then annealed at different temperatures for use as coated superconductor substrate. The as accumulative roll bonding processed sheets reveal a typical pure metal or copper type homogeneous deformation texture. After annealing at different temperatures, strong cube texture formation is observed and a nearly 100% cube textured tape with almost twin-free microstructure is obtained after annealing at 800°C for 1 h. The development of a sharp cube texture is accompanied by the development of a predominantly low angle boundary network. The very sharp cube texture formation in this material appears to be due to the oriented nucleation of cube grains at early stages of recrystallization and selective growth of these grains with progressing recrystallization. The results obtained indicate that accumulative roll bonding could be a very attractive route for fabricating highly cube textured superconductor substrate tapes from Ni.

Effect of processing on rheological, structural and sensory properties of apple puree

The relation between rheological, structural and sensory properties of apple purees was studied taking into account the effect of processing. For this reason, a grinding - separation strategy was established in order to vary pulp content and particle size. By grinding, three different particle size distributions were obtained. A second heat treatment was applied to purees to see the impact on its rheological and structural properties. An experimental design was constructed, with two factors (pulp content and particle size) and 4 levels (25, 31, 42, 60%) for pulp content and 3 levels (200, 500, 1100μm) for particle size. The rheological properties of purees were characterized using a controlled stress rheometer by the flow curves obtained from 2.14 to 214s−1 shear rate range; frequency sweeps measurements were performed within the linear viscoelastic region, in the range of 0.1-40rad/s. Purees behaved as shear-thinning fluids presenting a yield stress. Apparent viscosity and yield stress increased as pulp content increased, and they decreased as particle size decreased. The least shear thinning behaviour was observed in purees with low pulp content and small particles. A second heat treatment affected cell wall structure inducing a decrease of the rheological properties of the puree. The most important attributes to explain the texture of apple purees are consistency and graininess, parameters that can be manipulated by controlling processing conditions.

A Statistical Texture Model of the Liver Based on Generalized N-Dimensional Principal Component Analysis (GND-PCA) and 3D Shape Normalization

We present a method based on generalized N-dimensional principal component analysis (GND-PCA) and a 3D shape normalization technique for statistical texture modeling of the liver. The 3D shape normalization technique is used for normalizing liver shapes in order to remove the liver shape variability and capture pure texture variations. The GND-PCA is used to overcome overfitting problems when the training samples are too much fewer than the dimension of the data. The preliminary results of leave-one-out experiments show that the statistical texture model of the liver built by our method can represent an untrained liver volume well, even though the mode is trained by fewer samples. We also demonstrate its potential application to classification of normal and abnormal (with tumors) livers.