In the present study, the recycled post-consumption polyethylene terephthalate (PET) flakes were investigated as possible raw materials for the production of food packaging. After heating at 220 °C for 1 h, a steaming stage was conducted as a control test to assess the quality of the product. Different samples were characterized by H-NMR, FT-IR, DSC/TGA analysis, viscosity index (VI), and trace metals analysis. The results showed that the recycled post-consumed PET flakes' properties were generally conform to the standard norms of PET except the color of some flakes turned to yellow. Subsequently, a complementary study was undertaken to assess whether the material could be possibly reused for food packaging. For this purpose, rheological, thermal, and mechanical characterizations were performed. The results of the comparative study between the virgin and the recycled PET flakes concluded that the PET recycling affected the rheological properties but did not have any significant effect on their thermal and mechanical characteristics. Hence, it was deduced that the post-consumed PET flakes could be reused as a packaging material except food products.

Artificial Neural Networks (ANNs) are machine learning algorithms inspired by the structure and function of the human brain. Their popularity has increased in recent years due to their ability to learn and improve through experience, making them suitable for a wide range of applications. ANNs are often used as part of deep learning, which enables them to learn, transfer knowledge, make predictions, and take action. This paper aims to provide a comprehensive understanding of ANNs and explore potential directions for future research. To achieve this, the paper analyzes 10,661 articles and 35,973 keywords from various journals using a text-mining approach. The results of the analysis show that there is a high level of interest in topics related to machine learning, deep learning, and ANNs and that research in this field is increasingly focusing on areas such as optimization techniques, feature extraction and selection, and clustering. The study presented in this paper is motivated by the need for a framework to guide the continued study and development of ANNs. By providing insights into the current state of research on ANNs, this paper aims to promote a deeper understanding of ANNs and to facilitate the development of new techniques and applications for ANNs in the future.

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