###### Abstract  
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. machine learning; artificial neural networks; pork muscle; papain; microwave-vacuum drying; puffing; texture analysis; color analysis +
Footnote †: journal: Medical Centre, Adam Mickiewicz University, ul. Wszechnicy Plastowskiej 3, 61-614 Poznan, Poland; barp@amu. Many examples today show that ML may provide innovative insight into existing research problems [3; 10; 11]. ML is defined as the approach to creating data patterns with learning algorithms for creating models, which in turn may be used to solve actual problems [12]. Using ML helps set new standards of speed, flexibility and optimization with respect to products, which translates into cost savings, error reduction and quality decision making. The dominant method of representation learning born out of ML is artificial neural networks (ANNs) [13]. Neural models are a network of artificial neurons, interconnected so as to exchange various information, including using numerical data [8], images [2; 14] or sound [6]. To use the simplest description, a neural model consists of input, hidden and output layers. Each layer is a set of neurons, where a neuron from each layer generates a signal constituting one of the parameters for each neuron in the next layer. The method used to present the signal when designing the network determines the type of the artificial neural network, e.g., feedforward, recurrent or convolutional [9]. Given the above, assisting in the processes of drying, mass transfer or assessing the quality of meat (such as pork, mutton, beef, etc.) becomes an important part of ensuring that food products contain a sufficient amount of protein [15]. No literature exists on the use of artificial intelligence (AI) to assess the quality of pork meat snacks. Meat snacks are an important part of our diet and one of the fastest developing segments in the food market. The rapid development of meat snacks coinciding with an increased demand for food products tailored to the needs of consumers may enable the introduction of innovative meat snacks on the market, opening new possibilities before representatives of the meat industry, consumers and professionals. The meat snack industry wants to improve the quality and stability of traditional meat snacks or introduce new products with better nutritional values, functional qualities, more convenient packaging and improved sensory properties, such as taste and aroma, on the market. Matters related to the preservation of products are equally important [16]. Dehydration is one of the oldest methods of preserving meat [17]. Every kind of meat--beef, pork, game meat, poultry and fish--can be dried. Jerky made of beef is a widely sold meat snack, sold mostly in the USA. These are products available in large supermarkets, petrol stations, and in shops selling accessories connected, for example, with hunting and angina. The popularity of traditional meat snacks can reflect the changes in meat consumption. Drying has numerous applications in the food industry, mostly because of the variety of techniques available, long-life of dried products and economic reasons.