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Comparison of technological and physicochemical properties of cricket powders of different origin
Despite the widely described high nutritional value of insects, many authors suggest significant differences in the nutrient content depending on the breeding conditions, preparation methods, or even geographical origin. To date, there is no reports on the technological and physical properties of cricket powder (CP). This article describes the properties of 3 CPs of various geographic origins. The oil-absorption, water-binding, foaming capacities and foam stability were analysed. Thermal changes by DSC, water behaviour by LF-NMR and FTIR analysis were performed as well. On the obtained results, it was found that all analysed cricket powders were characterized by a high content of protein and fat. The geographical origin did not affect oil absorption, while the differences were recorded for water-binding. No foaming properties were observed in any of CPs. Thermal analysis showed the beginning of protein degradation at temperatures above 110 °C. Despite the differences in the water behaviour of dry CPs, no significant changes in hydrated CPs were observed. On the basis of the obtained results, it was found that the geographic origin of cricket powder will not affect the differences in technological properties, and thus the application of CP as an additive increasing the nutritional value can be widely used.
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.
Physicochemical and Morphological Study of the Saccharomyces cerevisiae Cell-Based Microcapsules with Novel Cold-Pressed Oil Blends
Vegetable oils rich in polyunsaturated fatty acids are a valuable component of the human diet. Properly composed oil blends are characterized by a 5:1 ratio of ω6/ω3 fatty acids, which is favorable from a nutritional point of view. Unfortunately, their composition makes them difficult to use in food production, as they are susceptible to oxidation and are often characterized by a strong smell. Encapsulation in yeast cells is a possible solution to these problems. This paper is a report on the use of native and autolyzed yeast in the encapsulation of oils. The fatty acid profile, encapsulation efficiency, morphology of the capsules obtained, and thermal behavior were assessed. Fourier transform infrared analysis and low-field nuclear magnetic resonance relaxation time measurements were also performed. The process of yeast autolysis changed the structure of the yeast cell membranes and improved the loading capacity. Lower encapsulation yield was recorded for capsules made from native yeast; the autolysis process significantly increased the value of this parameter. It was observed that NY-based YBMCs are characterized by a high degree of aggregation, which may adversely affect their stability. The average size of the AY capsules for each of the three oil blends was two times smaller than the NY-based capsules. The encapsulation of oils in yeast cells, especially those subjected to the autolysis process, ensured better oxidative stability, as determined by DSC, compared to fresh blends of vegetable oils. From LF NMR analysis of the relaxation times, it was shown that the encapsulation process affects both spin-lattice T1 and spin-spin T2* relaxation times. The T1 time values of the YBMCs decreased relative to the yeast empty cells, and the T2* time was significantly extended. On the basis of the obtained results, it has been proven that highly unsaturated oils can be used as an ingredient in the preparation of functional food via protection through yeast cell encapsulation.
Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles
In the modern times of technological development, it is important to select adequate methods to support various food and industrial problems, including innovative techniques with the help of artificial intelligence (AI). Effective analysis and the speed of algorithm implementation are key points in assessing the quality of food products. Non-invasive solutions are being sought to achieve high accuracy in the classification and evaluation of various food products. This paper presents various machine learning algorithm architectures to evaluate the efficiency of identifying blackcurrant powders (i.e., blackcurrant concentrate with a density of 67 °Brix and a color coefficient of 2.352 (E520/E420) in combination with the selected carrier) based on information encoded in microscopic images acquired via scanning electron microscopy (SEM). Recognition of blackcurrant powders was performed using texture feature extraction from images aided by the gray-level co-occurrence matrix (GLCM). It was evaluated for quality using individual single classifiers and a metaclassifier based on metrics such as accuracy, precision, recall, and F1-score. The research showed that the metaclassifier, as well as a single random forest (RF) classifier most effectively identified blackcurrant powders based on image texture features. This indicates that ensembles of classifiers in machine learning is an alternative approach to demonstrate better performance than the existing traditional solutions with single neural models. In the future, such solutions could be an important tool to support the assessment of the quality of food products in real time. Moreover, ensembles of classifiers can be used for faster analysis to determine the selection of an adequate machine learning algorithm for a given problem.
An instrumental analysis of changes in the physicochemical and mechanical properties of smoked and mould salamis during storage
Analiza proszku owocowego z wykorzystaniem spektroskopii w podczerwieni z transformacją Fouriera (FTIR ATR)
Skuteczność rozpoznawania proszków porzeczkowych za pomocą zespołów klasyfikatorów (classifier ensembles)
The Functional and Physicochemical Properties of Rice Protein Concentrate Subjected to Acetylation
The aim of the present study was to increase the value of rice protein concentrate (RPC) by improving the functional properties of a preparation subjected to acetylation and analyze the impact of this chemical modification on chemical composition, digestibility, and protein patterning using SDS-PAGE electrophoresis and FT-IR spectroscopy. In the modified samples, the protein content increased (80.90–83.10 g/100 g cf. 74.20 g/100 g in the control). Electrophoresis revealed that the content of the main rice protein fractions (prolamin and glutelin) decreased as the concentration of the modifying reagent increased. Through spectroscopic analysis, wavenumbers, corresponding to the presence of proteins or lipids, aromatic systems, and carbohydrates, were observed. The use of acetic anhydride did not change the digestibility of the modified RPC significantly when compared to that of the control sample. The acetylation of the RPC caused a significant increase in its emulsifying properties at pH 8 (1.83–14.74%) and its water-binding capacity but did not have a statistically significant impact on the oil-absorption capacity. There was a slight increase in protein solubility and a decrease in foaming capacity in the modified RPC.
Characteristics of Langmuir monomolecular monolayers formed by the novel oil blends
Abstract The aim of this work was to assess the physical properties of Langmuir monolayers of three new oil blends “RBWg” (obtained by mixing rapeseed oil, black cumin oil, and wheat germ oil), “REp” (rapeseed oil and evening primrose oil), and “CRb” (camelina oil and rice bran oil), as well as to characterize the molecular dynamics of their protons using low-field nuclear magnetic resonance (LF NMR) method. The studied blends are rich in oleic acid (C18:1), linolenic acid (C18:2), and α-linolenic acid (18:3). The chromatographically determined ratio of n6 to n3 fatty acids was found to be in the range of 5.18–5.27. The appropriate n6/n3 fatty acid ratio was also confirmed by FT-IR analysis. The spin–lattice relaxation rate (R 1) and spin–spin relaxation time (R 2) measured by LF NMR method were similar for the RBWg and REp blends but different from the third oil blend (CRb), which indicates lower proton mobility in CRb. The observed changes in the properties of monolayers of oil blends suggest that the refined rice bran oil in the CRb blend also significantly changes the viscoelastic properties of this blend. The results obtained in this study provide a theoretical basis for the development of a well-balanced approach to using oils in food production technology.
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.
The Process of Pasting and Gelling Modified Potato Starch with LF-NMR
Currently, society expects convenience food, which is healthy, safe, and easy to prepare and eat in all conditions. On account of the increasing popularity of modified potato starch in food industry and its increasing scope of use, this study focused on improving the physical modification of native starch with temperature changes. As a result, it was found that the suggested method of starch modification with the use of microwave power of 150 W/h had an impact on the change in starch granules. The LF-NMR method determined the whole range of temperatures in which the creation of a starch polymer network occurs. Therefore, the applied LF-NMR technique is a highly promising, noninvasive physical method, which allows obtaining a better-quality structure of potato starch gels.