Effect of Packaging and Portioning on the Dynamics of Water–Fat Serum Release from Fresh Pasta Filata Soft Cheese

Innovative Covering Liquids Stabilising Water-Fat Leachate from Fresh Mozzarella Cheese Used as Pizza Topping

This study analyses the possibility of changing the composition of the covering liquid in which mozzarella cheese is stored. The characterisation of mozzarella cheese consumed fresh and during later culinary use as a pizza topping was carried out. Mozzarella cheese from cow's milk and reconstituted sheep's milk were used for this study. The cheese was stored in whey-based covering liquid to which single or double amounts of lactose and/or citric acid (w/w) were added. The results obtained during laboratory analysis showed that the addition of lactose and/or citric acid to the covering liquid significantly impacted the mass of the cheese and the changes that can occur during later culinary use. The observed changes in the cheese during storage in the covering liquid were confirmed by the characteristics of the liquid itself. The smallest mass changes were related to cheeses stored in a covering liquid with double the amount of lactose and a single amount of citric acid. This cheese also displayed positive changes in all assessed descriptors (texture, melt, and colour). The amount of leachate from the cheese was small and occurred relatively late after unpacking and quartering. Based on the results of the study, changes made to the composition of the covering liquids can positively affect the characteristics of mozzarella cheese. Additionally, this data allows for the creation of specialised mozzarella cheeses that can pique the consumer's interest.

Application of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork Snacks

The objective of the study was to create artificial neural networks (ANN) capable of highly efficient recognition of modified and unmodified puffed pork snacks for the purposes of obtaining an optimal final product. The study involved meat snacks produced from unmodified and papain modified raw pork (Psoas major) by means of microwave-vacuum puffing (MVP) under specified conditions. The snacks were then analyzed using various instruments in order to determine their basic chemical composition, color and texture. As a result of the MVP process, the moisture-to-protein ratio (MPR) was reduced to 0.11. A darker color and reduction in hardness of approx. 25% was observed in the enzymatically modified products. Multi-layer perceptron networks (MLPN) were then developed using color and texture descriptor training sets (machine learning), which is undoubtedly an innovative solution in this area.