Journal of texture studies最新文献

Image texture refers to the visual patterns, variations, or configurations of pixel intensities within an image. Classifying textures is a fundamental goal in computer vision, applicable in areas ranging from medical picture analysis to distant sensing. Throughout the years, numerous strategies have been proposed to address this challenge; however, recent advances in deep learning have significantly transformed the subject. The proposed work delineates reliable and resilient local descriptors termed Texture Classification using Effective Texture Descriptors (TCETD), which integrates Locally Directional and Extremal Pattern (LDEP) with Gray-Level Co-occurrence Matrix (GLCM) to effectively acquire directional, extremum statistics, and spatial relationships among pixel intensities. To communicate directions related to the local area, it first obtains the directional local difference count pattern (DLDCP), which is divided into symmetric and asymmetric positions. By integrating the extremum location, differential, and compression pattern from adjacent sites, we extract the neighbor's extremum-related local pattern to acquire the extremum data generated by the initial segment. The two elements are combined to create the LDEP. The GLCM extracts spatial correlations, pixel intensity patterns, and features based on the distance and angle of pixels within an image. This descriptor can be utilized alongside the LDEP approach to offer a more thorough and resilient representation of the picture texture features, hence enhancing classification accuracy. The outcomes of experiments performed on three notable texture image databases—Klyberg (Stex), Kth-tips2-a, and CUReT—exhibit comparable correct classification rates of 97.91%, 93.82%, and 97.25%, respectively. These rates were achieved using our recently proposed TCETD descriptor under diverse conditions, including rotational and illumination variations, scale differences, and viewpoint alterations, in contrast to traditional methods for classifying texture images. The efficacy of the proposed strategy is corroborated using the Bonn BTF dataset, and the recommended method demonstrated superior performance.

To investigate the effects of irrational irrigation on tomato yield and quality, and the effects of different irrigation regimes on tomato biotexture mechanics, irrigation experiments were carried out in greenhouses, focusing on the two factors of irrigation frequency and irrigation quantity. Results showed that when the irrigation regime was A1 (150% ETc, 1 time/interval 1 day) or E3 (50% ETc, 1 time/interval 13 days), the fruit cracking rate was up to 69.53%; when the irrigation regime was C3 (50% ETc, 1 time/interval 5 days), the cracking rate was as low as 8.75%. Irrigation regime also had significant effects on the mechanical indicators. Meanwhile, the irrigation regime significantly affected the content and status of pectin in the cell wall but had no significant effect on hemicellulose and cellulose. With a short irrigation cycle and large irrigation amount, WSP (Water-soluble Fraction) increased, CSP (CDTA-soluble Fraction) and NSP (Na2CO3-soluble Fraction) decreased, and the fruit was easy to crack. The crystallinity, supporting power, and cracking resistance of pectin polysaccharides in C were strong, while those in A, B, D, and E were opposite. Research showed that the cracking of tomato induced by irrigation was due to the change of the composition and structure of the polysaccharide in the cell wall. This study can provide a reasonable irrigation regime such as C3 treatment for fruit farmers, which has the effect of both saving water and improving quality. The screened mechanical phenotypic indexes are conducive to reverse localization of crack control genes and enhancing tomato crack resistance breeding level.

The study of particle size is particularly important, as it influences the functional, textural, and physical properties of food products. This work evaluated the effect of milling extruded nixtamalized blue corn on anthocyanin content and tortilla texture. Blue corn was milled, conditioned with 0.3% lime at 25% moisture, and extruded at 90°C with a screw speed of 145 rpm. Extrudates were dried and milled using different mesh sizes (0.5, 0.8, 1.0, and 2.0 mm) to produce flours with fine and coarse particle sizes. Tortillas were machine-made and stored at room temperature for 48 h. The results showed that milling mesh size significantly affected the properties of masa and tortillas. Coarser flours (2.0 mm) led to improved tortilla texture, rollability, and higher anthocyanin retention during storage. These findings highlight the importance of milling and particle size selection in the manufacture of non-traditional tortillas to enhance their functional and textural quality.

This study investigated the regulatory effects of lipase on the viscoelastic behavior and microstructural evolution of soybean oil–wheat gluten (SOWG) composites in a model system. Dynamic frequency sweep and creep-recovery tests showed that lipase addition significantly influenced the viscoelastic properties of SOWG after 60 min of relaxation at 37°C, with optimal effects at 800 U/g. This dosage effectively maintained elasticity and recovery ability. Molecular-level analysis indicated enhanced hydrophobic interactions and reduced exposure of fluorescent groups, while also inhibiting covalent cross-linking (e.g., disulfide bond formation). Size exclusion chromatography revealed a broader molecular weight distribution in lipase-treated samples, suggesting a more complex network structure. FTIR analysis showed that 800 U/g lipase stabilized β-sheet content, whereas excessive addition (2000 U/g) reduced structural order and storage modulus by 43% and induced irregular macroporous regions (> 5 μm). In contrast, moderate lipase levels (800 U/g) promoted a more uniform and compact gluten network, with an optimized average pore size of 2.1 ± 0.4 μm. Correlation analysis revealed a synergistic relationship between viscoelastic parameters (e.g., network area, branch length, k and J values) and microstructural evolution, indicating that lipase enables multiscale regulation to improve the structural and rheological properties of SOWG composites.

This study investigated the effects of super-heated steam drying (SSD) on the textural, functional, cooking, rheological, and structural characteristics of millet analogue rice (MAR), formulated from pearl millet, sorghum, and parboiled rice through hot extrusion technology. Drying experiments were conducted at varying temperatures (120°C, 140°C, and 160°C) and thicknesses (2 mm, 4 mm, and 6 mm). Their impact on quality parameters, including water absorption index (WAI), color difference (ΔE), water absorption ratio (WAR), cooking loss (CL), water solubility index (WSI), and cooking time (CT), was analyzed and optimized using the Central Composite Design (CCD) within the framework of Response Surface Methodology (RSM). Optimal drying conditions were identified at 120°C and 2 mm thickness, achieving a ΔE of 3.51, a CT of 28.74 min, a WAR of 7.12, a CL of 8.06%, a WAI of 3.45 g/g, and a WSI of 6.23%, with a desirability value of 0.969. The optimized drying parameters significantly influenced the pasting and textural attributes of MAR (p < 0.05) compared to undried MAR. Rheological analysis demonstrated that both super-heated steam-dried and undried MAR exhibited superior elastic properties relative to viscosity. FTIR spectroscopy revealed modifications in the spectral region of 900–1100 cm⁻¹, indicating higher crystallinity in the dried MAR. Differential Scanning Calorimetry (DSC) analysis indicated reduced onset temperature and enthalpy, suggesting partial gelatinization and loss of crystalline order due to drying. Furthermore, scanning electron microscopy (SEM) revealed smoother and flatter surface morphology in the dried MAR compared to undried MAR. Overall, the findings elucidate the role of super-heated steam drying in enhancing the quality and structural integrity of MAR, thereby contributing to the development of superior MAR under optimized drying conditions.

Penetration test is the most direct and widely recognized standard method for assessing fruit firmness. Texture analyzers are increasingly employed for accurate fruit firmness measurement, offering advantages in reducing operator errors compared to the traditional manual test. The diverse options of texture analyzers provide flexibility for users, but also present challenges for standardizing firmness measurements. In addressing these issues, factors influencing firmness characterization, including peeling, probe size, penetration speed, penetration depth, and measurement parameters were analyzed for peach and nectarine. The results show that, compared to skin-off parameters, skin-on parameters exhibited absolute advantages for the nectarine cultivar. We also found the importance of probe size selection for accurate firmness detection for different cultivars, and it appears to be closely related to whether the fruits were peeled. The peach cultivar “HJ” is suitable for using a thicker probe (8 mm) after peeling, while the nectarine cultivar “ZNJH” is suitable for using a relatively thin probe (5 mm) with the skin on. The influence of speed is relatively low; however, a penetration speed of 5 mm/s is more recommended due to its better performance in capturing firmness differences. The force values at the steady phase of force-displacement curves perform best among all tested parameters. Penetration depths of 8 and 10 mm show no significant difference, suggesting that once the steady phase is reached, the influence of penetration depth is minimal.

In this study, potato slices were fried in four different vegetable oils (corn, olive, palm olein, and sunflower) to investigate how oil type influences the characteristics of potato chips. The diffusion coefficient of oils was attempted to be correlated with the final moisture, oil uptake, and textural parameters of potato chips. The diffusion coefficients were determined using two approaches. First, they were experimentally measured in a model matrix using dynamic light scattering (DLS) at 60°C, 70°C, and 80°C and subsequently extrapolated to the frying temperature (180°C) using the Arrhenius equation. Second, the effective diffusion coefficients were calculated based on Fick's law of diffusion. All chips were fried to a low moisture content (~1%–2%), but fat contents ranged widely from about 32% to 43% depending on the oil used. Chips fried in the highly saturated palm olein absorbed the least oil (~32%) and had the highest hardness (peak force), whereas those fried in polyunsaturated sunflower oil had the highest oil content (~43%) and lowest hardness. Diffusion coefficient measurements in the model system also differed by oil type (e.g., sunflower oil showed the lowest molecular diffusion rate), although the calculated effective diffusion coefficients of oils into chips were of similar order (~10⁻⁶ m²/s) for all oils. The correlation analyses revealed strong correlations between oil diffusion coefficients and chip moisture, fat content, and texture, highlighting the role of oil mobility in determining product quality. Understanding these correlations can help optimize frying processes and oil selection to produce chips with lower oil content and desirable crispness.