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AbstractThis paper utilizes the extended Fourier amplitude sensitivity test (EFAST) method to analyze the effects of varying speeds on the friction coefficient of the coated textured end seal surface in rotary vane actuators. The analysis focuses on the first-order, total, and coupled sensitivities of root mean square (RMS) roughness, texture depth, and texture area ratio. The accuracy and validity of the numerical simulations are experimentally verified. At low speeds (20 rpm), RMS roughness shows the highest first-order sensitivity, 1.247 and 2.181 times greater than texture depth and texture area ratio. Using Support Vector Regression and Particle Swarm Optimization, the optimal parameters were determined: an RMS roughness ofs 0.1012 μm, a texture area ratio of 77.99%, and a texture depth of 2.9601 μm. The model can also predict the friction coefficient for various parameter combinations at different speeds. [ABSTRACT FROM AUTHOR]

Copyright of Materiały Budowlane is the property of Wydawnictwo SIGMA-NOT and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water-lubricated rubber bearings are a critical component of the propulsion systems in underwater vehicles. Particularly under conditions of low speed and high load, friction-induced vibration and wear often occur. Surface texturing technology has been proven to improve lubrication performance and reduce friction and wear; however, research on how different texture parameters affect friction-induced vibration and wear mechanisms remains insufficient. In this study, various texture patterns with different area ratios and aspect ratios were designed on the surface of water-lubricated rubber bearings. By combining these designs with an in situ observation system based on computer vision technology, the effects of texture parameters on bearing friction, vibration, and wear were thoroughly investigated. The experimental results show that surface textures play a critical role in improving hydrodynamic effects and stabilizing the lubrication film at the friction interface. Specifically, textures with a high area ratio (15%) and aspect ratio (3:1) exhibited the best vibration suppression effect, primarily due to the reduction in actual contact area. However, excessively high area ratios may lead to increased surface wear. This study concludes that a reasonable selection of texture area and aspect ratios can significantly reduce frictional force fluctuations and vibration amplitude, minimize surface wear, and extend bearing life. [ABSTRACT FROM AUTHOR]

CoCrMo alloys are often used in the manufacture of artificial joints in the medical field, and their special working environment will lead to corrosion and abrasion of the surface materials, thus reducing the service life of the joints. Surface texturing technology can improve the friction performance of joints by reducing the friction area, storing abrasive particles and lubricants, etc. Its application in the field of CoCrMo alloy joint surface strengthening is of great significance. This paper analyses the characteristics of surface fabrication processing technology and the friction reduction mechanism of surface fabrication under fluid lubrication and dry friction conditions, summarizes the influence of various fabrication parameters (fabrication depth, size, density, shape, etc.) on the friction performance of the alloy, introduces other surface technologies of CoCrMo alloy as well as common medical materials, summarizes the existing problems in the research of CoCrMo alloy surface reinforcement technology at this stage and makes a study on the development direction of the surface reinforcement technology of CoCrMo alloy in the field of artificial joints. The development direction of CoCrMo alloy in the field of artificial joints is also prospected. [ABSTRACT FROM AUTHOR]

One of the most crucial factors in determining the consumer acceptability of processed or non-processed food products is their textural characteristics. Textural properties of food items can be created during processing by different techniques, one of which is drying. Drying which is one of the most widely used preservation methods of food products can extend their shelf life and generate unique sensory characteristics. The complicated process of drying is characterized by massive deformations while simultaneously transferring heat and moisture. Texture parameters of the dried food products vary depending on the type of dried food product (fruit and vegetables, grain and cereals, meat and marine), including the final volume, porosity, crust formation, and bulk density. Thus, this review focused on the examination of the textural characteristics of dried food items, the impact of the drying process, and the applied method on textural parameters. This study has also investigated the importance of pretreatment methods in improving the textural properties and quality of dried products. Furthermore, this review also considers the importance of modeling for texture prediction, discussing the different modeling approaches and their principles and applications. Recent applied nondestructive methods for textural analysis have been discussed in terms of application and performance. [ABSTRACT FROM AUTHOR]

The objective of the study was to perform a nutritional analysis and antioxidant capacity of three varieties of sweet potatoes, Ipomoea batatas L., from Mexico, classified by color as: purple, yellow and white. In addition, sweet potato extrudates were produced and evaluated for their nutritional, antioxidant, sensory and texture properties. The average content of macronutrients for the three varieties was 77.92 g of carbohydrate, 10.51 g of dietary fiber, 8.25 g of protein, and 0.53 g of lipid per 100 g tuber on a dry matter (DM) basis. The purple variety exhibited the highest content of fiber as well as zinc and sodium, and the white one displayed the highest content of protein. In turn, contents of calcium, iron, and magnesium were the highest in yellow potatoes. Ascorbic acid content ranged from 60.6 to 106.0 mg/100 g DM, being higher in the yellow potatoes, and the total phenolic content ranged from 216 to 581 mg GAE/100 g DM, being higher in the purple potatoes. The average antioxidant capacity was 40.7 and 23.4 μmol TE/g DM in DPPH and ABTS assays. A lower total phenolic content and antioxidant capacity of extruded sweet potatoes were found with respect to the fresh ones. Among the extrudates, the purple ones had the highest total phenolic content (307 mg GAE/100 g DM) and exhibited the highest antiradical activity in the ABTS assay (15.5 µmol TE/g DM). They were also scored the highest in the sensory analysis, although the instrumental texture analysis showed their greater hardness (64.4 N) compared to the yellow and white extrudates (46.9 and 30.5 N, respectively). Extrudates of the three potato varieties exhibited a sweet taste and, thus, can be considered as sweetener substitutes in snacks with increased nutritional and bioactive potential.

Copyright of Modern Food Science & Technology is the property of Editorial Office of Modern Food Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aimed to formulate flour blends from modified cassava flour (MCF) and suweg flour (SF) and use them to produce gluten-free steamed brownies (GFSB) with desirable qualities. The blends varied in the proportions of MCF and SF by weight, namely 80:20 (F1), 70:30 (F2), and 60:40 (F3). Wheat flour-based steamed brownies (WFSB) served as the control. The results showed that the MCF to SF ratio significantly influenced various parameters of flour blends, such as chemical compositions, color characteristics, pasting, and functional properties. Higher SF content in the blend decreased peak viscosity, breakdown, and setback. Water absorption capacity also decreased as the MCF to SF ratio in the flour blend increased. The color of flour blends with an increasing proportion of SF was getting darker and more different from the color of wheat flour. GFSB exhibited lower volume expansion than WFSB. Increasing SF content in the flour blend increased the texture parameters of GFSB including hardness, cohesiveness, gumminess, and chewiness Values of color difference, representing color variation between gluten-free and control brownies, ranged from 2.30 to 6.32, where the GFSB-F1 was more similar in color to WFSB. Preference levels for color, aroma, and texture of GFSB did not significantly differ from WFSB. However, GFSB-F2 was preferred in taste and overall acceptance over WFSB. The utilization of modified cassava flour and suweg flour blends in gluten-free steamed brownies offers a promising avenue for diversifying gluten-free baking options.

The effects of the supplier and the mass of pork thighs together with some production parameters including mass loss during ham production, mass of the final products and relative content of non-protein nitrogen (NPN) on the texture profile of Slovenian dry-cured ham – Kraški pršut were investigated to determine its optimal texture. The study included 32 dry-cured hams from two mass classes (heavy with thigh mass at salting of 11.7–12.1 kg and light with thigh mass at salting of 10.5–10.7 kg) and two different pork thigh suppliers from regular production. Slices cut transversally from the thigh at approximately 8 cm from the femur head were used for physicochemical parameter (pH, contents of moisture, total fat, protein, NaCl, and NPN) analysis, instrumental texture analysis profile analysis (TPA), and sensory analysis. Pork thigh mass had a significant effect on the TPA parameters; light samples were harder, chewier, springier, more cohesive and resilient compared to the heavy samples. NaCl content and sensory scores for saltiness were higher in the light Kraški pršut samples than in the heavy ones. Based on the median for hardness in the sensory analysis, the samples were classified into three ranks of texture using linear discriminant analysis (9 variables, 100% correct classification): optimal (median 4.0; 19% of samples), slightly too soft (median 3.5, 72% of samples), and soft (median 3.0; 9% of samples). Kraški pršut mass and hardness were significantly correlated to saltiness, pastiness, aroma, and all TPA parameters (p≤0.05). Positive correlation (p≤0.05) was also observed between NPN and pastiness. These findings indicate the importance of supplier, mass of pork thighs, and production parameters for optimizing the texture of Kraški pršut.

Leaf Area Index (LAI) remains one of the most important forest structural attributes, as accurate estimation of LAI is crucial for predicting the growth of different species. Using the Partial Least Squares Regression (PLS-R) algorithm, this study investigated three image processing techniques to determine the best technique for estimating LAI using WorldView-3 imagery in the Midlands, KwaZulu-Natal province of South Africa. The PLS-R texture ratios model achieved the highest accuracy of R2 = 0.70, RMSE = 1.21 (2.32% of the mean measured LAI) and R2 = 0.72, RMSE = 1.26 (2.41% of the mean measured LAI) for the wet and dry seasons, respectively. This was followed by the PLS-R single texture band model that produced an accuracy of R2 = 0.65, RMSE = 1.35 (2.58% of the mean measured LAI) and R2 = 0.67, RMSE = 1.32 (2.52% of the mean measured LAI) for the wet and dry seasons, respectively. The PLS-R model using a combination of vegetation indices had the lowest estimation accuracy of R2 = 0.59, RMSE = 1.38 (2.64% of the mean measured LAI) and R2 = 0.60, RMSE = 1.40 (2.67% of the mean measured LAI) for the wet and dry seasons, respectively. The results of this study provided evidence that image texture ratios can be used to estimate LAI effectively. [ABSTRACT FROM AUTHOR]

In order to study the effects of the distribution,number,density,placement angle and depth of different shapes of texture on the bearing performance of the misaligned gas foil thrust bearing,the Reynolds equation for the gas foil thrust bearing was solved by combining Successive Over Relaxation (SOR) and the finite difference method (FDM), and the load carrying capacity and frictional moment of the bearing were obtained by MatLab simulation under different texture parameters.The results show that the bearing load carrying capacity is significantly increased when the texture is distributed at the junction of the base area and the wedge area of the thrust bearing compared to other distribution methods. The load carrying capacity and frictional moment of the bearing increase first and then decrease with the increase of the proportion of micro-texture.The bearing performance is best improved when the maximum cross section of different shaped texture is placed along the radial direction of the thrust bearing. The micro-texture can improve the bearing performance only when its depth reaches a certain range.It is found that there exists optimal number, density,and depth of micro-textures that can make the thrust bearing have a higher bearing load capacity at a smaller friction torque. Compared to the bearings without texture, the elliptical micro texture with optimal micro-texture parameters can increase the load carrying capacity by 41.22% and reduce the friction torque by 0.81%. [ABSTRACT FROM AUTHOR]

This study aims to explore the changing trends in external characteristics of passion fruit pericarp and their application in predicting shelf-life, assessing storage quality, and advancing the field of non-destructive testing. Through storage experiments, the evolving patterns of passion fruit pericarp during storage and correlations among relevant physical parameters were analyzed. Three attribute parameters, namely minor-axis diameter, hardness, and mass, were extracted from the characteristic parameters of passion fruit, exhibiting the strongest interrelationships with storage time variable. The findings indicate a negative correlation between storage duration and minor-axis diameter of passion fruit, while no correlation was observed with major-axis diameter. The pericarp's contraction behavior is influenced by both thickness reduction and internal collapse. Moreover, a linear correlation existed between hardness of pericarp layers. The minor-axis diameter was positively correlated with puncture force at two peak positions (R = 0.87, P < 0.01; R = 0.95, P < 0.01). Simultaneously, minor-axis positively correlated with thickness and mass (R = 0.97, P < 0.01; R = 0.98, P < 0.01). To better quantify the degree of pericarp change, a passion fruit pericarp change index was proposed. Using the index as an independent variable and storage duration as the dependent variable, an equivalent fruit age prediction model was established. The results show a coefficient of determination (R2) of 0.997 and a significance level of P < 0.05 between predicted and actual values. This research has the potential to provide a theoretical foundation and methodological framework for predicting passion fruit shelf-life in the context of food preservation and storage, and to offer practical support for real-world applications in the field. [ABSTRACT FROM AUTHOR]

This study aimed to formulate flour blends from modified cassava flour (MCF) and suweg flour (SF) and use them to produce gluten-free steamed brownies (GFSB) with desirable qualities. The blends varied in the proportions of MCF and SF by weight, namely 80:20 (F1), 70:30 (F2), and 60:40 (F3). Wheat flour-based steamed brownies (WFSB) served as the control. The results showed that the MCF to SF ratio significantly influenced various parameters of flour blends, such as chemical compositions, color characteristics, pasting, and functional properties. Higher SF content in the blend decreased peak viscosity, breakdown, and setback. Water absorption capacity also decreased as the MCF to SF ratio in the flour blend increased. The color of flour blends with an increasing proportion of SF was getting darker and more different from the color of wheat flour. GFSB exhibited lower volume expansion than WFSB. Increasing SF content in the flour blend increased the texture parameters of GFSB including hardness, cohesiveness, gumminess, and chewiness. Values of color difference, representing color variation between gluten-free and control brownies, ranged from 2.30 to 6.32, where the GFSB-F1 was more similar in color to WFSB. Preference levels for color, aroma, and texture of GFSB did not significantly differ from WFSB. However, GFSB-F2 was preferred in taste and overall acceptance over WFSB. The utilization of modified cassava flour and suweg flour blends in gluten-free steamed brownies offers a promising avenue for diversifying gluten-free baking options. [ABSTRACT FROM AUTHOR]

The effects of the supplier and the mass of pork thighs together with some production parameters including mass loss during ham production, mass of the final products and relative content of non-protein nitrogen (NPN) on the texture profile of Slovenian dry-cured ham -- Kraški pršut were investigated to determine its optimal texture. The study included 32 dry-cured hams from two mass classes (heavy with thigh mass at salting of 11.7-12.1 kg and light with thigh mass at salting of 10.5-10.7 kg) and two different pork thigh suppliers from regular production. Slices cut transversally from the thigh at approximately 8 cm from the femur head were used for physicochemical parameter (pH, contents of moisture, total fat, protein, NaCl, and NPN) analysis, instrumental texture analysis profile analysis (TPA), and sensory analysis. Pork thigh mass had a significant effect on the TPA parameters; light samples were harder, chewier, springier, more cohesive and resilient compared to the heavy samples. NaCl content and sensory scores for saltiness were higher in the light Kraški pršut samples than in the heavy ones. Based on the median for hardness in the sensory analysis, the samples were classified into three ranks of texture using linear discriminant analysis (9 variables, 100% correct classification): optimal (median 4.0; 19% of samples), slightly too soft (median 3.5, 72% of samples), and soft (median 3.0; 9% of samples). Kraški pršut mass and hardness were significantly correlated to saltiness, pastiness, aroma, and all TPA parameters (p≤0.05). Positive correlation (p≤0.05) was also observed between NPN and pastiness. These findings indicate the importance of supplier, mass of pork thighs, and production parameters for optimizing the texture of Kraški pršut. [ABSTRACT FROM AUTHOR]

To solve the problem of premature failure of the rubber stator of the screw pump due to wear, reduce the frictional resistance torque of the screw pump to solve the problem of difficulty in starting after the shutdown of the screw pump production system in Daqing Oilfield. The effects of micron dot texture and lubricating medium on the tribological properties of 42CrMo metal and acrylonitrile–butadiene rubber mating surfaces were studied by theoretical analysis and tribological tests. The friction coefficient, wear amount, and rubber wear morphology were used as the measurement indexes of drag reduction, wear resistance, and wear degree, respectively. The results showed that the best combination of texture area ratio and the lubricating medium was 10% silicone grease, the friction coefficient and wear amount were reduced by 11% and 10.7%, respectively, compared with untextured, and the main order of chip storage and oil supply effect was 10% > 5% > 15%. [ABSTRACT FROM AUTHOR]

Water-lubricated rubber bearings are a critical component of the propulsion systems in underwater vehicles. Particularly under conditions of low speed and high load, friction-induced vibration and wear often occur. Surface texturing technology has been proven to improve lubrication performance and reduce friction and wear; however, research on how different texture parameters affect friction-induced vibration and wear mechanisms remains insufficient. In this study, various texture patterns with different area ratios and aspect ratios were designed on the surface of water-lubricated rubber bearings. By combining these designs with an in situ observation system based on computer vision technology, the effects of texture parameters on bearing friction, vibration, and wear were thoroughly investigated. The experimental results show that surface textures play a critical role in improving hydrodynamic effects and stabilizing the lubrication film at the friction interface. Specifically, textures with a high area ratio (15%) and aspect ratio (3:1) exhibited the best vibration suppression effect, primarily due to the reduction in actual contact area. However, excessively high area ratios may lead to increased surface wear. This study concludes that a reasonable selection of texture area and aspect ratios can significantly reduce frictional force fluctuations and vibration amplitude, minimize surface wear, and extend bearing life.

Nowadays, Functional food products as healthy diets have been considered extensively among consumers. Moreover, the use of particular microorganisms, for instance probiotic bacteria, offers probabilities to progress novel foods with appropriate shelf life. In the current research, in order to produce a functional ultrafiltrated (UF)-cheese, demineralized UF-whey powder (DUWP) at the levels of 0, 1 and 2%, and lactulose powder at the levels of 0 and 1% were used. Furthermore, bifidobacterium bifidum was used as probiotic bacteria. The textural characteristics in terms of hardness, cohesiveness, adhesiveness, springiness, gumminess and chewiness and sensorial propertis of the symbiotic cheese samples including odor, color, texture and taste were evaluated during 60 days of storage period at 4 ◦C. Results showed that addition of DUWP and lactulose powders except for adhesiveness and springiness caused significant decrease in all textural parameters (P<0.001). Besides, except for adhesiveness and cohesiveness, the mean values of textural parameters usually enhanced up to middle of the storage time and thereafter decreased up to end of storage period (p<0.05). Sensory results revealed that although addition of both DUWP and lactulose powders resulted in lower sensory scores, no significant differences were found between control (without DUWP and lactulose) and sample containing 1% of each both mentioned powders (P>0.05). Therefore, based on the obtained results, sample having 1% of DUWP and 1% of lactulose determined as the best symbiotic UF-cheese sample. Since the potential contribution of this product in community health promotion and reduction of diseases risk, the production and consumption of this functional cheese is proposed. [ABSTRACT FROM AUTHOR]

Lacto‐fermented foods may have health benefits. Therefore, the consumption of lacto‐fermented fruit and vegetables is important. The objective of this study was to assess the effect of lacto‐fermentation on zucchini flesh after 1 week and 3 months. The zucchini slices were classified using models built based on image textures using different machine learning algorithms. The developed models were characterized by overall accuracies of up to 99.33% for a combined set of textures selected from images in color channels R, G, B, L, a, b, X, Y, and Z (IBk algorithm). Only fresh zucchini slices were correctly classified with an accuracy of 100% and Precision, F‐Measure, Matthews Correlation Coefficient (MCC), Receiver Operating Characteristic (ROC) Area, and Precision‐Recall (PRC) Area of 1.000 for selected models. The greater changes in the zucchini flesh structure were observed after the first week of lacto‐fermentation compared to the fresh slices than between samples lacto‐fermented for 1 week and 3 months. Practical Applications: The obtained results confirmed that the developed procedure combining image analysis and machine learning can be used for the quality assessment of the lacto‐fermented zucchini and the detection of the optimal period to obtain desired changes. The determination of changes in product quality as a result of lengthening the lacto‐fermentation time can be useful for detecting the optimal period of the process. [ABSTRACT FROM AUTHOR]

Cyst nematodes are plant parasitic nematodes infecting crops, causing extensive crop damage and annual losses, and affecting food production. The precise species identification is significant to initiate their control. The repeatable, less expensive, and less laborious distinguishing cyst nematode species using image processing and artificial intelligence can be advantageous. The objective of this study was to distinguish cyst nematodes belonging to the species Globodera pallida, Globodera rostochiensis, and Heterodera schachtii based on image parameters using artificial neural networks (ANN). The application of parameters selected from a set of 2172 textures of images in color channels L, a, b, X, Y, Z, R, G, B, V, U, and S to build classification models using a narrow neural network, medium neural network, wide neural network, trilayered neural network, WiSARD, multilayer perceptron, and RBF network is a great novelty of the present study. Algorithms allowed for distinguishing cyst nematode species with an average accuracy reaching 89.67% for a model developed using WiSARD. The highest correctness was obtained for H. schachtii and this species was distinguished from each other with the highest accuracy of 95–98% depending on the classifier. Whereas the highest number of misclassified cases occurred between G. pallida, G. rostochiensis belonging to the same genus Globodera. The developed procedure involving image parameters and artificial neural networks can be useful for non-destructive and objective distinguishing cyst nematode species. [ABSTRACT FROM AUTHOR]

Black rockfish (Sebastes schlegelii) is a demersal economic species widely distributed in China's coastal regions. In order to determine the differences between wild (wild group) and recirculating aquaculture system (RAS)-farmed (RAS group) black rockfish, several parameters affecting quality were evaluated, such as proximal, texture, amino acids, fatty acids, muscle cellularity, and the expression of muscle growth-related genes. The results showed that the crude lipid, amino acids, polyunsaturated fatty acids (PUFA), and monosaturated fatty acids (MUFA) contents of RAS group were significantly higher (P < 0.05) than of wild group. Hardness, springiness, chewiness, muscle fiber diameter, cross-sectional area, crude protein, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) contents of wild group were significantly higher than of RAS group (P < 0.05). The muscle fiber density showed a positive and significant correlation with hardness, gumminess, springiness, and chewiness, whereas muscle fiber diameter showed a negative correlation. Expression of muscle growth-related genes including myogenic differentiation antigen (MyoD) and myosin heavy chain (MYH) in wild group was significantly higher than in RAS group. Moreover, MyoD and MYH expression was positively correlated with both muscle fiber density and cross-sectional area, and negatively correlated with fiber diameter, which could be a useful indicator for predicting flesh textural properties. It can be concluded that the exogenous and endogenous factors may determine variations of nutrient composition, flesh quality parameters, and muscle growth patterns of wild and RAS-farmed black rockfish. [ABSTRACT FROM AUTHOR]

Monitoring and identifying the freshness levels of meat holds significant importance in the field of food safety as it directly relates to human dietary safety. Traditional packaging methods for lamb meat quality assessment present issues such as cumbersome operations and irreversible damage. This research proposes a quality assessment method for modified atmosphere packaging lamb meat using near-infrared spectroscopy and multi-parameter fusion. Fresh lamb meat quality is taken as the research subject, comparing various physicochemical indicators and near-infrared spectroscopic information under different temperatures (4 °C and 10 °C) and different modified atmosphere packaging combinations. Through precision parameter comparison, rebound and TVB-N values are selected as the modeling parameters. Six spectral preprocessing methods (multi-scatter calibration, MSC; standard normal variate transformation, SNV; normalization; Savitzky–Golay smoothing, SG; Savitzky–Golay 1 derivative, SG-1st; and Savitzky–Golay 2 derivative, SG-2nd), and three feature wavelength selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; and uninformative variable elimination, UVE) are compared. Partial least squares (PLS) and support vector machine (SVM) are used to construct prediction models for chilled fresh lamb meat quality. The results show that when rebound is used as a parameter, the SG-2nd-SPA-PLSR model has the highest accuracy, with a determination coefficient R2p of 0.94 for the prediction set. When TVB-N is used as a parameter, the MSC-UVE-SVM model has the highest accuracy, with an R2p of 0.95 for the prediction set. In conclusion, the use of near-infrared spectroscopic analysis enables rapid and non-destructive prediction and evaluation of lamb meat freshness, including its textural characteristics and TVB-N content under different modified atmosphere packaging. This study provides a theoretical basis and technical support for further encapsulating the models into portable devices and developing portable near-infrared spectrometers to rapidly determine lamb meat freshness. [ABSTRACT FROM AUTHOR]

Different cultivars of seeds may have different properties. Therefore, distinguishing cultivars may be important for seed processing and product quality. This study was aimed at revealing the usefulness of innovative models developed based on selected image textures built using traditional machine algorithms for cultivar classification of quince seeds. The quince seeds belonging to four cultivars 'Uspiech', 'Leskovac', 'Bereczki', and 'Kaszczenko' were considered. In total, 1629 image textures from different color channels for each seed were extracted from color images acquired using a flatbed scanner. Texture parameters were used to build models for a combined set of selected textures from all color channels, sets of selected textures from color spaces RGB, Lab, and XYZ, and individual color channels R, G, B, L, a, b, X, Y, and Z using algorithms from different groups. The most successful models were developed using the Logistic (group of Functions), IBk (Lazy), LogitBoost (Meta), LMT (Trees), and naïve Bayes (Bayes). The classification accuracy reached 98.75% in the case of a model based on a combined set of textures selected from images in all color channels developed using the Logistic algorithm. For most models, the greatest misclassification of cases was observed between seeds 'Bereczki' and 'Kaszczenko'. The developed procedure can be used in practice to distinguish quince seeds in terms of a cultivar and avoid mixing seed cultivars with different properties intended for further processing. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the differences in texture parameters between freeze-dried and fresh carrot slices using image processing and artificial intelligence. Images of fresh and freeze-dried carrot slices were acquired using a digital camera. Texture parameters were extracted from slice images converted to individual color channels L, a, b, R, G, B, X, Y, and Z. A total of 1629 texture parameters, 181 for each of these color channels, were obtained. Models for the classification of freeze-dried and fresh carrot slices were created using various machine learning algorithms, based on attributes selected from a combined set of textures extracted from images in all color channels (L, a, b, R, G, B, X, Y, and Z). Using three different feature selection methods (Genetic Search, Ranker, and Best First), the 20 most effective texture parameters were determined for each method. The models with the highest classification accuracy obtained by applying various machine learning algorithms from Trees, Rules, Meta, Lazy, and Functions groups were determined. The classification successes obtained with the parameters selected from all three different feature selection algorithms were compared. Random Forest, Multi-class Classifier, Logistic and SMO machine learning algorithms achieved 100% accuracy in the classification performed with texture features obtained by each feature selection algorithm. [ABSTRACT FROM AUTHOR]

Buckwheat hulls which are rich in fiber are good ingredients to increase the nutritional value of food products. The aim of this study was to determine the effect of the applied additives in the form of fiber and apple juice on the properties of both potato and corn starch (normal and waxy). In order to characterize the rheological properties of kissel, the pasting characteristic was measured. In the obtained gels, the basic quality parameters were determined. The analysis of texture, color parameters, and also total polyphenol content were determined. Buckwheat hulls, in addition to their high fiber content, are a valuable source of phenolic compounds and can be a great additive in starch desserts. The addition of buckwheat hulls and apple juice improved the nutritional value of the final products but also caused changes in the technological qualities: it increased the initial temperature of potato starch mixtures (by approx. 9 °C); it decreased the viscosity of cold desserts (from 8 to 55%); and increased the hardness of the final product by more than 7 times. In the case of other starches, the recorded changes were much smaller than for potato starch-based products. [ABSTRACT FROM AUTHOR]

By researching the influence of micro-groove texture on the surface tribological properties of the stator and rotor pair of oil production hydraulic motors, this paper aims to reduce the frictional resistance moment of the spiral pair of hydraulic motors, and further solve the problem of the difficult restart of the pump of a certain type of hydraulic-driven screw pump. According to the spiral pair of screw motors, a metal-rubber flat plate reciprocating friction model is established, and rectangular micro-grooves with different texture angles and depths are machined on the surface of the metal specimen. A combination of finite element simulation and tribological tests is used to carry out a study on the influence of different texture parameters on the friction performance of the hydraulic motor spiral pair. The results showed that at a certain texture angle, the friction coefficient of each specimen basically increases with the texture depth. When the texture depth is constant, the friction coefficient increases first and then decreases with the increase of the texture angle. The texture angle is the main factor affecting the friction coefficient. Under the same test conditions, the friction coefficient of the textured specimen can be reduced by 20.2% compared with the untextured specimen. In the metal-rubber contact pair of the stator and rotor of the hydraulic motor, the friction reduction mechanism of the texture mainly transport the lubricating medium through the micro-grooves to improve the lubricating conditions. Samples with a reasonable design of texture parameters can effectively reduce the friction coefficient of the friction pair without reducing the service life of the hydraulic motor, which is conducive to the smooth restart of the oil production system of the hydraulically driven screw pump. [ABSTRACT FROM AUTHOR]

Copyright of Proceedings of the YSU B: Chemical & Biological Sciences / Gitakan Teghekagir. K'imia, Kensabanut'yun is the property of Publishing House of Yerevan State University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, the computer vision area, which represents one of the subfields of artificial intelligence and machine learning, has been widely used to process data in the oil and gas industry. In this context, the detection of specific properties inside carbonate rocks in different datasets from petroleum reservoirs represents a considerable challenge, that consumes enormous resources and time. Therefore, the automatic separation of the lithologies within rocks of reservoirs has attracted the increasing attention of many research groups. The consistent classification of these lithologies is the main factor for the construction of reliable depositional, diagenetic, and reservoir models. This work deals with this last issue by presenting the development of a technique for the automatic classification of carbonate thin sections obtained from plane-polarized and cross-polarized microscopy images corresponding to natural rocks belonging to the Brazilian pre-salt reservoir. Our proposed model transforms the analyzed images into structured data by defining texture parameters (Haralick parameters), and Wavelets transforms. Later, a stacked autoencoder neural network is used to eliminate images with anomalies and/or distortions in order to define relevant characteristics of the data. This stage is followed by supervised classifier called multilayer feed-forward neural network. The definition of the model's hyperparameters is tuned by Bayesian optimization and the Gaussian process. For training and testing of the network, images of 570 thin sections were used (each image obtained with plane-polarized and cross-polarized light) totaling 1140 images. Our model reported an accuracy of 83% for the test samples, confirming the validity of the proposed model in the automatic classification of carbonate rocks. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess the influence of storage under different storage conditions on black currant quality in a non-destructive and inexpensive manner using image processing and artificial intelligence. Black currants were stored at a room temperature of 20 ± 1 °C and a temperature of 3 °C (refrigerator). The images of black currants directly after harvest and fruit stored for one and two weeks were obtained using a digital camera. Then, texture parameters were computed from the images converted to color channels R (red), G (green), B (blue), L (lightness component from black to white), a (green for negative and red for positive values), b (blue for negative and yellow for positive values), X (component with color information), Y (lightness), and Z (component with color information). Models for the classification of black currants were built using various machine learning algorithms based on selected textures for RGB, Lab, and XYZ color spaces. Models built using the IBk, multilayer perceptron, and multiclass classifier for textures from RGB color space, and the IBk algorithm for textures from Lab color space distinguished unstored black currants and samples stored in the room for one and two weeks with an average accuracy of 100%, and the kappa statistic and weighted averages of precision, recall, Matthews correlation coefficient (MCC), receiver operating characteristic (ROC) area, and precision–recall (PRC) area equal to 1.000. This indicated a very distinct change in the external structure of the fruit after the first week and more and more visible changes in quality with increasing storage time. A classification accuracy reaching 98.67% (multilayer perceptron, Lab color space) for the samples stored in the refrigerator may indicate smaller quality changes caused by storage at a low temperature. The approach combining image textures and artificial intelligence turned out to be promising to monitor the quality changes in black currants during storage. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Institute of Food Science & Technology / Zhongguo Shipin Xuebao is the property of Journal of Chinese Institute of Food Science & Technology Periodical Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Food processing allows for maintaining the quality of perishable products and extending their shelf life. Nondestructive procedures combining image analysis and machine learning can be used to control the quality of processed foods. This study was aimed at developing an innovative approach to distinguishing fresh and lacto-fermented red bell pepper samples involving selected image textures and machine learning algorithms. Before processing, the pieces of fresh pepper and samples subjected to spontaneous lacto-fermentation were imaged using a digital camera. The texture parameters were extracted from images converted to different color channels L, a, b, R, G, B, X, Y, and Z. The textures after selection were used to build models for the classification of fresh and lacto-fermented samples using algorithms from the groups of Lazy, Functions, Trees, Bayes, Meta, and Rules. The highest average accuracy of classification reached 99% for the models developed based on sets of selected textures for color space Lab using the IBk (instance-based K-nearest learner) algorithm from the group of Lazy, color space RGB using SMO (sequential minimal optimization) from Functions, and color space XYZ and color channel X using IBk (Lazy) and SMO (Functions). The results confirmed the differences in image features of fresh and lacto-fermented red bell pepper and revealed the effectiveness of models built based on textures using machine learning algorithms for the evaluation of the changes in the pepper flesh structure caused by processing. [ABSTRACT FROM AUTHOR]

Cyst nematodes are plant parasitic nematodes infecting crops, causing extensive crop damage and annual losses, and affecting food production. The precise species identification is significant to initiate their control. The repeatable, less expensive, and less laborious distinguishing cyst nematode species using image processing and artificial intelligence can be advantageous. The objective of this study was to distinguish cyst nematodes belonging to the species Globodera pallida, Globodera rostochiensis, and Heterodera schachtii based on image parameters using artificial neural networks (ANN). The application of parameters selected from a set of 2172 textures of images in color channels L, a, b, X, Y, Z, R, G, B, V, U, and S to build classification models using a narrow neural network, medium neural network, wide neural network, trilayered neural network, WiSARD, multilayer perceptron, and RBF network is a great novelty of the present study. Algorithms allowed for distinguishing cyst nematode species with an average accuracy reaching 89.67% for a model developed using WiSARD. The highest correctness was obtained for H. schachtii and this species was distinguished from each other with the highest accuracy of 95–98% depending on the classifier. Whereas the highest number of misclassified cases occurred between G. pallida, G. rostochiensis belonging to the same genus Globodera. The developed procedure involving image parameters and artificial neural networks can be useful for non-destructive and objective distinguishing cyst nematode species.

Monitoring and identifying the freshness levels of meat holds significant importance in the field of food safety as it directly relates to human dietary safety. Traditional packaging methods for lamb meat quality assessment present issues such as cumbersome operations and irreversible damage. This research proposes a quality assessment method for modified atmosphere packaging lamb meat using near-infrared spectroscopy and multi-parameter fusion. Fresh lamb meat quality is taken as the research subject, comparing various physicochemical indicators and near-infrared spectroscopic information under different temperatures (4 °C and 10 °C) and different modified atmosphere packaging combinations. Through precision parameter comparison, rebound and TVB-N values are selected as the modeling parameters. Six spectral preprocessing methods (multi-scatter calibration, MSC; standard normal variate transformation, SNV; normalization; Savitzky–Golay smoothing, SG; Savitzky–Golay 1 derivative, SG-1st; and Savitzky–Golay 2 derivative, SG-2nd), and three feature wavelength selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; and uninformative variable elimination, UVE) are compared. Partial least squares (PLS) and support vector machine (SVM) are used to construct prediction models for chilled fresh lamb meat quality. The results show that when rebound is used as a parameter, the SG-2nd-SPA-PLSR model has the highest accuracy, with a determination coefficient R2p of 0.94 for the prediction set. When TVB-N is used as a parameter, the MSC-UVE-SVM model has the highest accuracy, with an R2p of 0.95 for the prediction set. In conclusion, the use of near-infrared spectroscopic analysis enables rapid and non-destructive prediction and evaluation of lamb meat freshness, including its textural characteristics and TVB-N content under different modified atmosphere packaging. This study provides a theoretical basis and technical support for further encapsulating the models into portable devices and developing portable near-infrared spectrometers to rapidly determine lamb meat freshness.

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Different cultivars of seeds may have different properties. Therefore, distinguishing cultivars may be important for seed processing and product quality. This study was aimed at revealing the usefulness of innovative models developed based on selected image textures built using traditional machine algorithms for cultivar classification of quince seeds. The quince seeds belonging to four cultivars ‘Uspiech’, ‘Leskovac’, ‘Bereczki’, and ‘Kaszczenko’ were considered. In total, 1629 image textures from different color channels for each seed were extracted from color images acquired using a flatbed scanner. Texture parameters were used to build models for a combined set of selected textures from all color channels, sets of selected textures from color spaces RGB, Lab, and XYZ, and individual color channels R, G, B, L, a, b, X, Y, and Z using algorithms from different groups. The most successful models were developed using the Logistic (group of Functions), IBk (Lazy), LogitBoost (Meta), LMT (Trees), and naïve Bayes (Bayes). The classification accuracy reached 98.75% in the case of a model based on a combined set of textures selected from images in all color channels developed using the Logistic algorithm. For most models, the greatest misclassification of cases was observed between seeds ‘Bereczki’ and ‘Kaszczenko’. The developed procedure can be used in practice to distinguish quince seeds in terms of a cultivar and avoid mixing seed cultivars with different properties intended for further processing.

This study was aimed at assessing the effect of microwave drying at 100, 200, or 300 W on the quality of Cavendish banana slices without pretreatment and with pretreatment using 5% ascorbic acid solution, 5% citric acid solution, 5% gum arabic solution, and ultrasound. Banana slices were imaged using a digital single-lens reflex (SLR) camera. The acquired images were processed to extract texture parameters. The classification models were developed based on image texture parameters selected from a big dataset of 2172 textures of images in different color channels using artificial neural networks. Wide Neural Network, Bilayered Neural Network, Medium Neural Network and three classifiers from the group of function, such as RBF (Radial basis function) Network, Multilayer Perceptron, and WiSARD were applied. Banana slices belonging to 15 classes with different combinations of pretreatment and microwave drying were distinguished with an average accuracy of up to 97.2% for a model built using Multilayer Perceptron. For most models, banana samples microwave-dried at 200 W without pretreatment were classified with the highest correctness. The performed study revealed that the objective, non-destructive, correct, and robust quality assessment of pretreated and microwave-dried banana slices may be performed using image processing and artificial intelligence. • Banana slices were subjected to different combinations of pretreatment and microwave drying. • Samples were classified using image processing and artificial neural networks. • The obtained classification accuracy reached 97.2%. • The most successful model was developed using Multilayer Perceptron. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the effects of the dry-aging method on the sensory properties, chemical composition, and profile parameters of the texture of beef obtained from local farms. The qualitative characteristics of the beef were investigated for five samples, respectively, fresh meat, and dry-aged beef for 14, 21, 28, and 35 days, in aging rooms with controlled parameters: temperature (1 ± 1 °C), relative humidity (80 ± 5%), and air circulation speed (0.5–2 m/s). During the dry-aging period, there was a decrease in humidity by about 6.5% in the first 21 days, which allowed the concentration of fat, protein, and total collagen content. The dry-aging process considerably influenced the pH value of the meat, which, in the second part of the dry-aging process (14–35 days), increased from 5.49 to 5.66. These values favored the increase by 37.33% of the water retention capacity and the activation of the meat's own enzymes (calpain, cathepsin, collagenase). This influenced the solubilization process of proteins and collagen, thus contributing to the improvement of the texture profile. Because variations in organoleptic and physicochemical parameters occurred simultaneously during dry-aging and storage, the method of analyzing the information was applied. Mutual information on the influence of physicochemical indicators on the texture profile parameters was followed, a factor of major importance in the consumer's perception. The degree of influence of soluble proteins, sarcoplasmic and myofibrillar proteins, fats, and soluble collagen content on the texture profile parameters (hardness, cohesiveness, springiness, gumminess, and chewiness) of the dry-aged beef for 35 days was established. These investigations allowed the optimization of the beef dry-aging technological process in order to obtain a product with a sensory profile preferred by the consumer. [ABSTRACT FROM AUTHOR]

The different cultivars of apricot seeds may differ in their properties. To ensure economical and efficient seed processing, knowledge of the cultivars' composition and physical properties may be necessary. Therefore, the correct identification of the cultivar of the apricot seeds may be very important. The objective of this study was to develop models based on selected textures of apricot seed images to distinguish different cultivars. The images of four cultivars of apricot seeds were acquired using a flatbed scanner. For each seed, approximately 1600 textures from the image, converted to the different color channels R, G, B, L, a, b, X, Y, and Z, were calculated. The models were built separately for the individual color channels; the color spaces Lab, RGB, XYZ; and all color channels combined based on selected texture parameters using different classifiers. The average accuracy of the classification of apricot seeds reached 99% (with an accuracy of 100% for the seeds of the cultivars 'Early Orange', 'Bella', and 'Harcot', and 96% for 'Taja') in the case of the set of textures selected from the color space Lab for the model built using the Multilayer Perceptron classifier. The same classifier produced high average accuracies for the color spaces RGB (90%) and XYZ (86%). For the set of textures selected from all color channels, i.e., R, G, B, L, a, b, X, Y, and Z, the average accuracy reached 96% (Multilayer Perceptron and Random Forest classifiers). In the case of individual color channels, the highest average accuracy was up to 91% for the models built based on a set of textures selected from color channel b (Multilayer Perceptron). The results proved the possibility of distinguishing apricot seed cultivars with a high probability using a non-destructive, inexpensive, and objective procedure involving image analysis. [ABSTRACT FROM AUTHOR]

This study was aimed at evaluating the effect of freeze-drying and lacto-fermentation on the texture parameters of images and sensory attributes of beetroots. The samples were imaged using a flatbed scanner, and textures from images converted to color channels L, a, b, R, G, B, X, Y, Z were computed. The discrimination of raw and processed beetroots was performed using models based on textures selected for each color channel. The sensory quality of processed samples was determined using the attributes related to smell, color, texture and taste. The highest discrimination accuracy of 97.25% was obtained for the model built for color channel b. The accuracies for other channels were equal to 96.25% for channel a, 95.25% for channel R, 95% for channel Y, 94.75% for channel B, 94.5% for channel X, 94% for channel L, 92.5% for channel G, 88.25% for channel Z. In the case of some models, the raw and lacto-fermented beetroots were discriminated with 100% correctness. The freeze-dried and freeze-dried lacto-fermented samples were also the most similar in terms of sensory attributes, such as off-odor, attractiveness color, beetroot color, crunchiness, hardness, bitter taste, overall quality. The results indicated that the image parameters and sensory attributes may be related. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess the influence of storage under different storage conditions on black currant quality in a non-destructive and inexpensive manner using image processing and artificial intelligence. Black currants were stored at a room temperature of 20 ± 1 °C and a temperature of 3 °C (refrigerator). The images of black currants directly after harvest and fruit stored for one and two weeks were obtained using a digital camera. Then, texture parameters were computed from the images converted to color channels R (red), G (green), B (blue), L (lightness component from black to white), a (green for negative and red for positive values), b (blue for negative and yellow for positive values), X (component with color information), Y (lightness), and Z (component with color information). Models for the classification of black currants were built using various machine learning algorithms based on selected textures for RGB, Lab, and XYZ color spaces. Models built using the IBk, multilayer perceptron, and multiclass classifier for textures from RGB color space, and the IBk algorithm for textures from Lab color space distinguished unstored black currants and samples stored in the room for one and two weeks with an average accuracy of 100%, and the kappa statistic and weighted averages of precision, recall, Matthews correlation coefficient (MCC), receiver operating characteristic (ROC) area, and precision–recall (PRC) area equal to 1.000. This indicated a very distinct change in the external structure of the fruit after the first week and more and more visible changes in quality with increasing storage time. A classification accuracy reaching 98.67% (multilayer perceptron, Lab color space) for the samples stored in the refrigerator may indicate smaller quality changes caused by storage at a low temperature. The approach combining image textures and artificial intelligence turned out to be promising to monitor the quality changes in black currants during storage.

Food processing allows for maintaining the quality of perishable products and extending their shelf life. Nondestructive procedures combining image analysis and machine learning can be used to control the quality of processed foods. This study was aimed at developing an innovative approach to distinguishing fresh and lacto-fermented red bell pepper samples involving selected image textures and machine learning algorithms. Before processing, the pieces of fresh pepper and samples subjected to spontaneous lacto-fermentation were imaged using a digital camera. The texture parameters were extracted from images converted to different color channels L, a, b, R, G, B, X, Y, and Z. The textures after selection were used to build models for the classification of fresh and lacto-fermented samples using algorithms from the groups of Lazy, Functions, Trees, Bayes, Meta, and Rules. The highest average accuracy of classification reached 99% for the models developed based on sets of selected textures for color space Lab using the IBk (instance-based K-nearest learner) algorithm from the group of Lazy, color space RGB using SMO (sequential minimal optimization) from Functions, and color space XYZ and color channel X using IBk (Lazy) and SMO (Functions). The results confirmed the differences in image features of fresh and lacto-fermented red bell pepper and revealed the effectiveness of models built based on textures using machine learning algorithms for the evaluation of the changes in the pepper flesh structure caused by processing.

The different cultivars of apricot seeds may differ in their properties. To ensure economical and efficient seed processing, knowledge of the cultivars’ composition and physical properties may be necessary. Therefore, the correct identification of the cultivar of the apricot seeds may be very important. The objective of this study was to develop models based on selected textures of apricot seed images to distinguish different cultivars. The images of four cultivars of apricot seeds were acquired using a flatbed scanner. For each seed, approximately 1600 textures from the image, converted to the different color channels R, G, B, L, a, b, X, Y, and Z, were calculated. The models were built separately for the individual color channels; the color spaces Lab, RGB, XYZ; and all color channels combined based on selected texture parameters using different classifiers. The average accuracy of the classification of apricot seeds reached 99% (with an accuracy of 100% for the seeds of the cultivars ‘Early Orange’, ‘Bella’, and ‘Harcot’, and 96% for ‘Taja’) in the case of the set of textures selected from the color space Lab for the model built using the Multilayer Perceptron classifier. The same classifier produced high average accuracies for the color spaces RGB (90%) and XYZ (86%). For the set of textures selected from all color channels, i.e., R, G, B, L, a, b, X, Y, and Z, the average accuracy reached 96% (Multilayer Perceptron and Random Forest classifiers). In the case of individual color channels, the highest average accuracy was up to 91% for the models built based on a set of textures selected from color channel b (Multilayer Perceptron). The results proved the possibility of distinguishing apricot seed cultivars with a high probability using a non-destructive, inexpensive, and objective procedure involving image analysis.

The aim of this study was to investigate the effects of the dry-aging method on the sensory properties, chemical composition, and profile parameters of the texture of beef obtained from local farms. The qualitative characteristics of the beef were investigated for five samples, respectively, fresh meat, and dry-aged beef for 14, 21, 28, and 35 days, in aging rooms with controlled parameters: temperature (1 ± 1 °C), relative humidity (80 ± 5%), and air circulation speed (0.5–2 m/s). During the dry-aging period, there was a decrease in humidity by about 6.5% in the first 21 days, which allowed the concentration of fat, protein, and total collagen content. The dry-aging process considerably influenced the pH value of the meat, which, in the second part of the dry-aging process (14–35 days), increased from 5.49 to 5.66. These values favored the increase by 37.33% of the water retention capacity and the activation of the meat’s own enzymes (calpain, cathepsin, collagenase). This influenced the solubilization process of proteins and collagen, thus contributing to the improvement of the texture profile. Because variations in organoleptic and physicochemical parameters occurred simultaneously during dry-aging and storage, the method of analyzing the information was applied. Mutual information on the influence of physicochemical indicators on the texture profile parameters was followed, a factor of major importance in the consumer’s perception. The degree of influence of soluble proteins, sarcoplasmic and myofibrillar proteins, fats, and soluble collagen content on the texture profile parameters (hardness, cohesiveness, springiness, gumminess, and chewiness) of the dry-aged beef for 35 days was established. These investigations allowed the optimization of the beef dry-aging technological process in order to obtain a product with a sensory profile preferred by the consumer.

The present study aims to develop recipe compositions and technology for producing sponge cakes from wholemeal flour, partially replaced with a functional plant component dry blossom flour of Sambucus nigra L. Three designs of sponge cakes with 5, 10, and 15% content of flour of Sambucus nigra L. corrected up to 100% with whole-grain oat flour were studied. Their characteristics were compared with sponge cakes of 100% wheat flour/control. The obtained new products were characterized by reduced carbohydrates, increased content of dietary fiber, and preserved volume compared to the control. The physicochemical parameters of sponge cake and marshmallows with different concentrations of dry flowers of Sambucus nigra L. included in them differed from the control with lower water absorption, pH, and moisture, while having a higher relative mass and ash content and retaining the original size. Pathogenic microorganisms such as Escherichia coli, Salmonella sp., and Staphylococcus aureus, and common coliforms were not detected in the control and experimental samples when determining the microbiological parameters. Therefore, the developed formulations are an excellent alternative to wheat flour, significantly improving some nutritional characteristics such as smell, taste, dietary fiber, and lower carbohydrate content.

Plum fruit and kernels offer bioactive material for industrial production. The promising procedure for distinguishing plum kernel cultivars used in this study comprised two stages: image analysis to compute the texture parameters of plum kernels belonging to three cultivars ‘Emper’, ‘Kalipso’, and ‘Polinka’, and discriminant analysis using machine learning algorithms to classify plum kernel cultivars based on selected textures with the highest discriminative power. The discriminative models built separately for sets of textures selected from all color channels L, a, b, R, G, B, U, V, S, X, Y, Z, color space Lab and color channel b using the KStar (Lazy), PART (Rules), and LMT (Trees) classifiers provided the highest average accuracies reaching 98% in the case of the color space Lab and the KStar classifier. In this case, individual cultivars were discriminated with the accuracies of 97% for ‘Emper’ and ‘Kalipso’ to 99% for ‘Polinka’. The values of other performance metrics were also satisfactory, higher than 0.95. The ROC curves were quite smooth and steady with the most satisfactory curve for the ‘Kalipso’ kernels. The present study sheds light on an objective, non-destructive, and inexpensive procedure for cultivar discrimination of plum kernels.