Influence of tetraploid wheat (Triticum dicoccum) on low glycaemic index pizza base processing and its starch digestibility

The present study aimed to utilise Dicoccum wheat (DW), barley (BF) and soya flour (SF) in the development of low in vitro starch digestibility (IVSD) and estimated glycaemic index (EGI) PB. The BF and SF are mixed (1:1) (BFSF) and used to replace DW at 25%, 50% and 75% level and compared with 100% DW. The quality characteristics (rheological, physical, textural and chemical), IVSD and EGI of PB were determined. The incorporation of BFSF to DW increased the farinograph water absorption capacity (58.1%–64.2%) and mixing tolerance index (21-68 FU), and decreased the dough development time (4.3–2.8 min) and amylograph pasting properties, respectively. Further, the thickness, shear force and redness were decreased and lightness and yellowness increased. Based on the physico-sensory studies, 50% of BFSF along with emulsifier’s incorporation was optimum without affecting any sensory parameters and this PB had decreased IVSD (31) and EGI (54) when compared to the control 37 and 63, respectively.     

Evaluating the profile of myofibrillar proteins and its relationship with tenderness among five styles of dry-cured hams

In order to investigate the relationship between profile of myofibrillar proteins and tenderness among 2 kinds of Chinese hams (Jinhua and Xuanwei) and 3 kinds of European hams (Iberian, Serrano and Parma), shear force, myofibril fragmentation index (MFI), SDS-PAGE, carbonyls content and Raman spectroscopy were investigated. The shear force and salt content of Chinese hams were significantly higher than that of European hams, while moisture content was lower than that of European hams (p < 0.05). MFI values and SDS-PAGE profile revealed that the degradation of myofibrillar proteins in Chinese hams was lower than in European hams. In addition, Chinese hams showed significantly higher carbonyls content and β-sheet content compared with European hams, indicated that proteins aggregation intensively inhibited the degradation of myofibrillar proteins in Chinese hams. These results indicated that the higher shear force in Chinese style hams could be attributed to the lower moisture content and limited proteolysis.     

The influence of brewing method on bioactive compounds residues in spent coffee grounds of different roasting degree and geographical origin

The content of bioactive compounds in spent coffee grounds (SGC) was studied. SGC were obtained from Coffea arabica beans of different roasting degrees (light and dark) and different geographical origins (Nicaragua, Columbia and Mexico) processed using four brewing methods (mocha, filtered, drip and infusion). The highest caffeine and chlorogenic acid contents were determined in filtered spent coffee extracts. All extracts of light roasted spent coffee grounds showed lower browning index levels in comparison to that from dark roasted spent coffee grounds. Generally, the highest content of total polyphenolic compounds and highest antioxidant capacity were determined in extracts prepared in drip. In conclusion, the results obtained in this study indicate that the spent coffee grounds produced of domestic levels, especially those obtained from filter coffeemaker, could be considered as a good source of natural antioxidants.     

Effect of baking in different ovens on the quality and structural characteristics of saltine crackers

The aim of the study was to evaluate the physical and microstructural characteristics of crackers baked in four different industrial baking ovens (indirect radiation-cyclotherm, indirect convection, hybrid and industrial tunnel-ITO). Indirect convection and cyclotherm ovens provide the highest (5685.43 ± 51 W m⁻²) and the lowest (4860 ± 38.87 W m⁻²) amount of heat flux, respectively. Despite the amount of heat flux, indirect convection led to crackers with the highest moisture (7.86% vs. 4.82% in clyclotherm) and specific volume, but the lowest hardness. Cyclotherm resulted in crackers with lower specific volume, surface area, porosity, smooth and regular surface. Conversely, the hybrid and ITO ovens showed closer heat flux, leading to crackers with similar moisture content, texture parameters, specific volume, browning and inner porosity. Overall results show the potential of baking using different ovens for modifying the quality parameters of the crackers.     

Integration of machine learning and first principles models

Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white-box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black-box models) and artificial intelligence (AI) techniques. Hybrid models (gray-box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine-learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.     

High-entropy ferroelastic rare-earth tantalite ceramic: (Y0.2Ce0.2Sm0.2Gd0.2Dy0.2)TaO4

In this study, high-entropy rare-earth tantalate ceramics (Y_0.2Ce_0.2Sm_0.2Gd_0.2Dy_0.2)TaO₄ ((5RE_0.2)TaO₄) have been successfully fabricated. The possibility of formation of (5RE_0.2)TaO₄ was verified via first-principles calculations. In addition, the phase structure, ferroelastic toughening mechanism, thermophysical, and mechanical properties were systematically investigated. The (5RE_0.2)TaO₄ ceramics have lower phonon thermal conductivity (1.2–2.6 W·m^–1·K^–1) in the entire temperature range than that of RETaO₄ and YSZ. (5RE_0.2)TaO₄ has a higher fracture toughness and lower brittleness index than YSZ. The thermal expansion coefficients of (5RE_0.2)TaO₄ are as high as 10.3 × 10^-6 K^–1 at 1200°C and Young's modulus is 66–189 GPa, and thus, (5RE_0.2)TaO₄ possesses great potential for application in thermal barrier coatings (TBCs).     

Chemical Bonding by the Chemical Orthogonal Space of Reactivity

The fashionable Parr–Pearson (PP) atoms-in-molecule/bonding (AIM/AIB) approach for determining the exchanged charge necessary for acquiring an equalized electronegativity within a chemical bond is refined and generalized here by introducing the concepts of chemical power within the chemical orthogonal space (COS) in terms of electronegativity and chemical hardness. Electronegativity and chemical hardness are conceptually orthogonal, since there are opposite tendencies in bonding, i.e., reactivity vs. stability or the HOMO-LUMO middy level vs. the HOMO-LUMO interval (gap). Thus, atoms-in-molecule/bond electronegativity and chemical hardness are provided for in orthogonal space (COS), along with a generalized analytical expression of the exchanged electrons in bonding. Moreover, the present formalism surpasses the earlier Parr–Pearson limitation to the context of hetero-bonding molecules so as to also include the important case of covalent homo-bonding. The connections of the present COS analysis with PP formalism is analytically revealed, while a numerical illustration regarding the patterning and fragmentation of chemical benchmarking bondings is also presented and fundamental open questions are critically discussed.     

Characterization of Bi2O3 thin films for doping photoluminescent Er3+ ions

Undoped and Er³⁺-doped Bi₂O₃ thin films were sputter-deposited on Si(100) substrates. Sufficiently oxidized Bi₂O₃ films with refractive indices between 2.17?2.23 were obtained at a wavelength of 633 nm; these values are comparable to those of bulk Bi₂O₃ crystals. While the film composition was stable for deposition temperatures between room temperature (RT) and 450 °C, the refractive index steeply decreased above 450 °C and reached 1.4 at 600 °C. The lowering of the optical transmittance spectra indicated aggregation of metallic Bi and darkening of the film. All films exhibited X-ray diffraction patterns of α-Bi₂O₃. The direct and indirect bandgap energies derived from the Tauc plots were 3.4–3.7 eV and 1.9–2.5 eV, respectively, depending on the O₂ flow rate and deposition temperature. Upon excitation of Er³⁺-doped Bi₂O₃ films at 532 nm, Er³⁺ emissions peaking at 1537 and 1541 nm appeared, and the photoluminescence spectra included fine structures reflecting crystal-field splitting. Resonant excitation of Er³⁺ 4f levels and indirect excitation via the defect levels of Bi₂O₃ followed by energy transfer to Er³⁺ contributed to the emission. The films deposited at RT with Er concentrations of 2 at.% had the emission intensity of Er³⁺, but concentration quenching strongly suppressed the Er³⁺ emission because the doped Er³⁺ ions stayed inside the Bi₂O₃ crystals. At deposition temperatures above 400 °C, the concentration quenching was mitigated possibly because out-diffusion of Er³⁺ ions reduced the effective number of Er³⁺ ions in the Bi₂O₃ crystalline domains.     

Neural network-based hybrid models developed for free radical polymerization of styrene

In the present work, the free radical polymerization of styrene is modeled by considering the phenomenology of the process (a simplified model, which does not include the diffusional effects, gel, and glass effects) in combination with an empirical model represented by an artificial neural network. Differential evolution (DE) algorithm, belonging to the class of evolutionary algorithms, is applied for developing the neural models in optimal forms. For improving the results—predicted conversion and molecular weights as function of time, temperature, and initiator concentration—different combinations between phenomenological model and neural network are tested; also, individual and stacked neural networks have been developed for the polymerization process. This methodology based on hybrid models, including neural networks aggregated in stacks, provides accurate results.