Herein, we examined the possibility of producing probiotic yogurt with the addition of polymerized whey protein (PWP). It was determined that the yogurt was stable in terms of syneresis, texture, and sensory features. No spontaneous whey syneresis (SWS) was found in PWP yogurt during 21 days of refrigerated storage at 3 ± 0.5 °C. PWP yogurt had a 5.3% higher water retention capacity (WHC) than yogurt with whey protein concentrate (WPC). Compared with yogurt with unpolymerized protein, PWP yogurt had a higher absolute cohesiveness and viscosity index. The addition of whey protein concentrates to native and polymerized form resulted in longer maintenance of the original yogurt coherence than the control yogurt during storage. PWP yogurt had the same color saturation as the control yogurt. The polymerization of whey proteins resulted in a vanilla pudding aftertaste in yogurt and increased butter flavor 2.5-fold.

Functional foods are defined as foods and ingredients that exhibit health benefits beyond their nutritional value. Research on functional foods is increasing rapidly as they may help prevent and manage some non-communicable diseases. Whey proteins are recognized as a high-quality nutrient source and known to contain some bioactive components. They are rich in essential amino acids such as cysteine, branched-chain amino acids such as leucine, valine, and isoleucine, and bioactive peptides. Whey proteins look promising as a potential functional food, given its antioxidant, anti-inflammatory, blood pressure lowering, anti-obesity, and appetite suppressing effects that is discussed in the literature. Whey proteins also show functional properties that play an essential role in food processing as an emulsifier, fat-replacer, gelling and encapsulating agent and are known to improve sensory and textural characteristics of food. This review focuses on the functional food aspects of whey proteins, associated health effects, and current food applications.

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.

Color is important for the consumer when making a purchase decision. Mare’s milk and, thus, fermented mare’s milk is little known to consumers. Thus, it is worth presenting research showing the extent of color change during the production and storage of mare’s milk. Herein, we examined the range of color changes in mare’s milk and cow’s milks adapted to mare’s milk composition. These samples were further fermented and stored for 3 weeks at 5 ± 1 °C. Starter cultures containing Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus were used for fermentation. Mare’s milk reached the required pH of 4.5 during fermentation faster (255 min) than cow’s milk (300 min). After fermentation, mare’s milk compared to cow’s milk and adapted cow’s milk had lower titratable acidity (0.75%) and firmness (145. 6 |(g∙s)|). The water holding capacity (95.6%) and number of Lactobacillus (7.71 log CFU/mL) and Streptocococcus (7.20 log CFU/mL) in mare’s and other’s milks were the same. Mare’s milk was furthest from the ideal white (WI) color, with its chrome (C*) being 1.5-times larger than cow’s milk. However, fermented mare’s milk was darker than the fermented adapted milk and cow’s milk by 36% and 58%, respectively. Storage caused a decrease in the WI, C*, and yellowness index (YI). The fermented mare’s milk color stability during production and storage was less than that of fermented cow’s milk. After 3 weeks storage, it was observed that the titratable acidity increased to 1.05%, and the pH decreased to 4.3 in fermented mare’s milk. The water holding capacity decreased but was still higher compared to fermented cow’s milk.