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Analiza proszku owocowego z wykorzystaniem spektroskopii w podczerwieni z transformacją Fouriera (FTIR ATR)
Exploring the impact of dietary fiber enrichment on molecular water properties and indicators of Maillard reaction (furosine, Nε-carboxymethyllysine, and Nε-carboxyethyllysine) in model gluten-free bread
Fruit Powder Analysis Using Machine Learning Based on Color and FTIR-ATR Spectroscopy - Case Study: Blackcurrant Powders
Fruits represent a valuable source of bioactivity, vitamins, minerals and antioxidants. They are often used in research due to their potential to extend sustainability and edibility. In this research, the currants were used to obtain currant powders by dehumidified air-assisted spray drying. In the research analysis of currant powders, advanced machine learning techniques were used in combination with Lab color space model analysis and Fourier transform infrared spectroscopy (FTIR). The aim of this project was to provide authentic information about the qualities of currant powders, taking into account their type and carrier content. In addition, the machine learning models were developed to support the recognition of individual blackcurrant powder samples based on Lab color. These results were compared using their physical properties and FTIR spectroscopy to determine the homogeneity of these powders; this will help reduce operating and energy costs while also increasing the production rate, and even the possibility of improving the available drying system.
Effect of flaxseed oil cake extract on the microbial quality, texture and shelf life of gluten-free bread
Extending the shelf life of gluten-free bread (GFB) is a challenge. Mainly due to the ingredients used and their characteristics, GFB has numerous drawbacks such as unsatisfactory texture and rapid staling beyond a low nutritional value. In the present study, flaxseed oil cake extract (FOCE) was used to replace water (25–100%) in GFB formulations in order to test FOCE’s potential to reduce GFB staling and extend microbial stability. Texture (TPA test), water activity (LF NMR), acidity (pH measurements) and microbiological quality of GFBs were tested. Moreover, the content of a lignan with broad health-promoting potential, secoisolariciresinol diglucoside (SDG), in GFB with FOCE was analyzed. The results showed that the use of FOCE enriched experimental GFB in valuable SDG (217–525 µg/100 g DM) while not causing adverse microbiological changes. A moderate level (25–50%) of FOCE did not change the main texture parameters of GFB stored for 72 h, the quality of which was comparable to control bread without FOCE. Meanwhile, higher proportions of FOCE (75–100% of water replacement) shortened GFB shelf life as determined by water activity and texture profile, suggesting that GFB with FOCE should be consumed fresh. To summarize, FOCE at moderate levels can add value to GFBs without causing a drop in quality, while still fitting in with the idea of zero waste and the circular economy.
An instrumental analysis of changes in the physicochemical and mechanical properties of smoked and mould salamis during storage
Insight into the Gluten-Free Dough and Bread Properties Obtained from Extruded Rice Flour: Physicochemical, Mechanical, and Molecular Studies
The present study aimed to evaluate the effect of the extrusion process and particle size on the properties of rice flour (microstructure, pasting properties), gluten-free dough (rheological properties), and bread (texture, specific volume, water absorption capacity, low-field nuclear magnetic resonance (LF NMR) relaxometry). Rice flours were extruded at 80 and 120 °C with feed moisture (15 and 30%) and with the same particle size (<132 and >132–200 µm). Significant differences were observed between the pasting profiles of the flours before and after extrusion. The pasting profile of extruded flours confirmed that hydrothermal treatment partially gelatinized the starch, decreasing the viscosity during heating. The water binding properties increased with the extrusion temperature and moisture content and also with the particle size of the flour. The most important parameter influencing the mechanical properties of the dough was the moisture content of the flour and significant differences were observed between fine (<132 μm) and coarse flours (>132–200 μm). The molecular dynamics of particles containing protons in the bound and bulk fractions in each sample do not depend on the extruder parameters or granulation of the obtained fraction. LF NMR results confirmed that extrusion of rice flour led to a significant decrease in the T21 value compared to the control sample and an increase in the T22 value in breads made with flours with particle size <132 μm. A linear relationship was found between the spin-spin relaxation times (T1) changes and the equilibrium water activity (ar). The results showed that bread with extruded rice flour at the same die temperature resulted in a significantly higher bread volume (31%) and lower hardness (27%) compared to the control. The highest hardness was observed in the case of samples prepared with extruded flour with the addition of 15% moisture, regardless of temperature and particle size.
The application of convolutional neural networks, LF-NMR, and texture for microparticle analysis in assessing the quality of fruit powders: Case study – blackcurrant powders
Abstract It can be observed that dynamic developments in artificial intelligence contributing to the evolution of existing techniques used in food research. Currently, innovative methods are being sought to support unit processes such as food drying, while at the same time monitoring quality and extending their shelf life. The development of innovative technology using convolutional neural networks (CNNs) to assess the quality of fruit powders seems highly desirable. This will translate into obtaining homogeneous batches of powders based on the specific morphological structure of the obtained microparticles. The research aims to apply convolutional networks to assess the quality, consistency, and homogeneity of blackcurrant powders supported by comparative physical methods of low-field nuclear magnetic resonance (LF-NMR) and texture analysis. The results show that maltodextrin, inulin, whey milk proteins, microcrystalline cellulose, and gum arabic are effective carriers when identifying morphological structure using CNNs. The use of CNNs, texture analysis, and the effect of LF-NMR relaxation time together with statistical elaboration shows that maltodextrin as well as milk whey proteins in combination with inulin achieve the most favorable results. The best results were obtained for a sample containing 50% maltodextrin and 50% maltodextrin (MD50-MD70). The CNN model for this combination had the lowest mean squared error in the test set at 2.5741 × 10−4, confirming its high performance in the classification of blackcurrant powder microstructures.
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.
Molecular Properties of Starch-Water Interactions in the Presence of Bioactive Compounds from Barley and Buckwheat-LF NMR Preliminary Study
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.