Filters
Fermentation Kinetics, Microbiological and Physical Properties of Fermented Soy Beverage with Acai Powder
In this study, the effects of the fermentation kinetics, determination of the number of lactic acid bacteria, texture, water holding capacity, and color of fermented soy beverages with acai powder (3 and 6% w/v) were investigated. The addition of acai powder significantly influenced the fermentation kinetics based on changes in pH, accelerating fermentation in the initial period. The results showed that the acai additive did not affect the enumeration of Lactobacillus acidophilus and Bifidobacterium animalis subsp. lactis. The presence of acai inhibited the proliferation of Streptococcus thermophilus compared to the soy beverage without acai powder added. However, the higher the acai additive, the more Streptococcus thermophilus bacteria were detected: 4.39 CFU/g for 6% acai powder sample and 3.40 CFU/g for 3% acai powder sample. The addition of acai to the soy beverage reduced its firmness, consistency, cohesiveness, and viscosity index after fermentation. A slight difference was observed in the lightness and whiteness of fermented soy beverages with 3% and 6% acai powder.
Fresh White Cheeses from Buttermilk with Polymerized Whey Protein: Texture, Color, Gloss, Cheese Yield, and Peptonization
Buttermilk and whey, despite their documented health and technological potential, are still not sufficiently utilized for the development of new products. In this research, the texture, color, gloss, cheese yield, and peptonization of fresh white cheeses made from buttermilk with the addition of whey proteins after heat treatment were analyzed. Additionally, the influence of the polymerization process on cheese yield and composition was examined. Four fresh white cheese samples were prepared: without a whey protein concentrate (FWC); with a whey protein concentrate (FWC/WPC); with single-heated polymerized whey proteins (FWC/SPWP); and with double-heated polymerized whey proteins (FWC/DPWP). The introduction of whey proteins in buttermilk cheese production increased the cheese yield by over 2-fold. There were no differences in color and gloss between the FWC/SPWP and FWC/DPWP samples. The cheese became glassy and transparent during melting. The content of uncrushed curd that remained white ranged from 27% in FWC/DPWP to 74% in FWC/SPWP.
Determinants of the attitudes of proinnovative dairy consumers and a model simulating consumer behavior regarding increasing calcium intake
Designing the Properties of Probiotic Kefir with Increased Whey Protein Content
This research unveiled new insights on the impact of incorporating whey proteins into kefir produced using three different methods. This aims to improve its quality and health benefits, primarily as a result of optimal proliferation of probiotic bacteria. In the initial part of the experiment, samples were prepared using three different methods (methods 1, 2, and 3) to examine the impact of introducing whey protein on bacterial count, the content of L(+)-lactic acid, lactase activity, and the lactic acid and ethanol levels. The methods differed primarily in the sequence of the inoculation milk with probiotic bacteria stage in the production cycle, as well as incubation time and temperature. No significant differences were found in the number of yeasts and bacteria between samples with and without whey proteins. However, it was revealed that the 5% addition of whey proteins enhanced the number of probiotic bacteria in kefir produced with method 2 (from 4.86 to 5.52 log cfu/mL) and method 3 (from 3.68 to 4.01 log cfu/mL). The second part of the research investigated the impact of whey proteins on firmness, consistency, cohesiveness, viscosity, color, and water activity of kefir. This part focused on testing samples with lower whey protein contents (1 and 3%, w/v). We found that the addition of 1% and 3% whey proteins resulted in decreased firmness, consistency, cohesiveness, and viscosity compared to the control kefir. On the other hand, the addition of 5% whey proteins resulted in increased firmness and consistency compared to the addition of 1% and 3% whey proteins. The addition of whey protein decreased the white index WI of the kefir samples. Overall, our results revealed that incorporating whey protein concentrate (WPC) in the production of probiotic kefir can enhance its health benefits while maintaining its rheological properties and overall quality.
Effects of Heat Treatment Duration on the Electrical Properties, Texture and Color of Polymerized Whey Protein
In this research effects of heat treatment duration on the electrical properties (zeta potential and conductivity), texture and color of polymerized whey protein (PWP) were analyzed. Whey protein solutions were heated for 30 min to obtain single-heated polymerized whey protein (SPWP). After cooling to room temperature, the process was repeated to obtain double-heated polymerized whey protein (DPWP). The largest agglomeration was demonstrated after 10 min of single-heating (zeta potential recorded as −13.3 mV). Single-heating decreased conductivity by 68% and the next heating cycle by 54%. As the heating time increased, there was a significant increase in the firmness of the heated solutions. Zeta potential of the polymerized whey protein correlated with firmness, consistency, and index of viscosity, the latter of which was higher when the zeta potential (r = 0.544) and particle size (r = 0.567) increased. However, there was no correlation between zeta potential and color. This research has implications for future use of PWP in the dairy industry to improve the syneretic, textural, and sensory properties of dairy products.
The influence of the texture and color of goat’s salad cheese on the emotional reactions of consumers compared to cow’s milk cheese and Feta cheese
AbstractIn this study, the sensory and mechanical aspects of the texture of goat’s milk salad cheese were correlated with the emotional profiles of consumers. Using descriptive sensory analysis and instrumental assessment, the texture profile of goat’s milk salad cheese was compared to cow’s milk salad cheese and Feta cheese. Texture measurements confirmed that goat’s cheese compared to cow’s cheese had more softness and less hardness, and Feta cheese had the highest whiteness index compared to the other cheeses. Goat’s milk salad cheese was much less acceptable to consumers compared to cow’s milk cheese and Feta cheese. Consumers also indicated that the hardness of goat’s cheese was lower than that of cow’s cheese and Feta cheese. A reduction in “stickiness” in comparison with cow’s cheese was also reported; however, it was much higher than that for Feta cheese. The “fracturability” and “graininess” of goat’s cheese was similar to cow’s cheese. Emotional profile analysis showed that goat’s cheese evokes mainly negative emotions. Consumers indicated only one positive emotion in the case of this cheese, which was “healthy”. The most frequently mentioned emotions after the consumption of goat’s cheese were “upset”, “disgusted” and “worried”. Many consumers also indicated “disappointed” and “angry”, which did not occur after the consumption of cow’s cheese. This research shows how important it is to combine several analyses and techniques when evaluating dairy products, including salad cheeses. It is also important that consumer research is enriched by emotional profiling. Graphical abstract
Application of Machine Learning to Assess the Quality of Food Products - Case Study: Coffee Bean
Modern machine learning methods were used to automate and improve the determination of an effective quality index for coffee beans. Machine learning algorithms can effectively recognize various anomalies, among others factors, occurring in a food product. The procedure for preparing the machine learning algorithm depends on the correct preparation and preprocessing of the learning set. The set contained coded information (i.e., selected quality coefficients) based on digital photos (input data) and a specific class of coffee bean (output data). Because of training and data tuning, an adequate convolutional neural network (CNN) was obtained, which was characterized by a high recognition rate of these coffee beans at the level of 0.81 for the test set. Statistical analysis was performed on the color data in the RGB color space model, which made it possible to accurately distinguish three distinct categories of coffee beans. However, using the Lab* color model, it became apparent that distinguishing between the quality categories of under-roasted and properly roasted coffee beans was a major challenge. Nevertheless, the Lab* model successfully distinguished the category of over-roasted coffee beans.