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.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献