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Gluten-Free Bread Enriched with Potato and Cricket Powder: Comparative Study of the Effects of Protein on Physicochemical Properties Bonds and Molecular Interactions
The increasing demand for diverse foods and tailored nutrition encourages the development of innovative products, such as bread enriched with cricket powder (CP) or potato protein (PP). This study presents the preparation and analysis of gluten-free breads with CP and PP, focusing on their nutritional value and physical properties. Analytical methods included water activity measurement, bread volume, crumb color analysis, FTIR spectroscopy, low-field NMR relaxometry, and texture profile analysis. Ash content ranged from 0.60 ± 0.03% to 1.16 ± 0.11%, and caloric values ranged from 216.2 to 229.5 kcal/100 g. Water activity remained stable across all samples (0.975–0.976). Crumb color analysis showed the greatest change in CP samples (ΔE = 14.07), while PP had minimal impact (ΔE = 2.15). FTIR spectra revealed increased amide I and II bands, indicating higher protein content. NMR results demonstrated shorter T1, T21, and T22 times for CP, suggesting reduced water mobility and a denser structure, while PP samples showed higher values, indicating a looser, more hydrated matrix. Texture analysis confirmed that CP increased firmness and compactness, whereas PP enhanced springiness. These findings suggest that CP and PP can improve the nutritional and structural properties of gluten-free bread, offering valuable alternatives for modern dietary needs.
Changes in the mechanical, sensory, and microbiological properties during the storage of innovative vegetable and meat soups for seniors
This study was conducted on vegetable soup with rabbit meat and vegetable soup with rabbit meat, beef balls, and carrots. The qualitative characteristics of the soups were adapted to the needs of elderly consumers. The soups used in the experiments were industrially produced. The aim of this study was to analyse changes in the mechanical, sensory, and microbiological properties of the soups occurring during their storage (1, 7, 14, and 21 days). Strength tests were performed at temperatures of 20 °C and 55 °C. Both soups had a high protein content (4.7–6.5%), low sugar (0.3–0.5%) and salt content (0.8%), and a fibre content of 1.4%. The texture analysis showed great similarity in the mechanical characteristics of both soups. The samples were characterised by low measured values for firmness (0.72 N) and cohesiveness (−0.14 N) in both temperatures. The average shear force of the beef balls with carrots at 20 °C was 12.3 N, but after heating, it decreased to 8.8 N (p < 0.05). The rheological tests on the soups showed that they were characterised by a relatively high viscosity (15–20 Pas at 55 °C). Storage of the soups for 21 days did not significantly affect their rheological parameters (p > 0.05). The soup with beef balls and carrots was rated higher by the sensory panel. On the 21st day of storage, the permitted limit of the count of bacteria was not exceeded in either of the samples. This study shows that the soups had desirable structural, nutritional, and sensory characteristics, which are important for this group of consumers. The values of the mechanical parameters of all the samples were low, and they were even significantly more reduced when the products were heated. This may suggest that the products should not be difficult to consume for seniors.
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.