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The Rheology, Texture, and Molecular Dynamics of Plant-Based Hot Dogs
The rising demand for plant-based alternatives to traditional meat products has led to the development of plant-based sausages (PBSs) that closely mimic the texture and taste of their meat counterparts. This study investigates the rheological and textural properties, as well as proton molecular dynamics, of hot dog-type PBSs and batters used in their production. Various formulations were analyzed to understand how different ingredients and processing methods affect the characteristics of the final products. Our findings reveal that the incorporation of specific plant proteins and hydrocolloids significantly influences the rheological behavior and texture profile of sausages. The hardness of the samples ranged from 4.33 to 5.09 N/mm and was generally higher for the products with inorganic iron sources. Regarding the viscoelastic properties, all the samples showed larger values of the storage modulus than the loss modulus, which indicates their solid-like behavior. Additionally, the study utilized advanced proton nuclear magnetic resonance (NMR) techniques to elucidate the molecular dynamics within plant-based matrices, providing insights into water distribution and mobility. Key findings highlight the impact of different plant proteins and additives on the texture and stability of sausage analogs.
The Prediction of Pectin Viscosity Using Machine Learning Based on Physical Characteristics—Case Study: Aglupectin HS-MR
In the era of technology development, the optimization of production processes, quality control and at the same time increasing production efficiency without wasting food, artificial intelligence is becoming an alternative tool supporting many decision-making processes. The work used modern machine learning and physical analysis tools to evaluate food products (pectins). Various predictive models have been presented to estimate the viscosity of pectin. Based on the physical analyses, the characteristics of the food product were isolated, including L*a*b* color, concentration, conductance and pH. Prediction was determined using the determination index and loss function for individual machine learning algorithms. As a result of the work, it turned out that the most effective estimation of pectin viscosity was using Decision Tree (R2 = 0.999) and Random Forest (R2 = 0.998). In the future, the prediction of pectin properties in terms of viscosity recognition may be significantly perceived, especially in the food and pharmaceutical industries. Predicting the natural pectin substrate may contribute to improving quality, increasing efficiency and at the same time reducing losses of the obtained final product.