Polymerase chain reaction for soybean detection in heat processed meat products.

Since vegetable proteins are considerably cheaper than muscle proteins, they are frequently used as meat extenders in order to reduce the cost of the final product. Due to several interesting characteristics, soybean is reported to be the most widely used vegetable protein in the meat industry. Nevertheless, soybean is included in the group of 12 ingredients potentially allergenic, which should therefore be labelled according to the Codex Alimentarius FAO/WHO and the European Commission (Directive 2003/89/EC). In fact, it has been described that amounts of soy bellow 0.1% and 1% (w/w) can lead to allergic reactions in sensitive consumers (1)

Empowering Deaf-Hearing Communication: Exploring Synergies between Predictive and Generative AI-Based Strategies towards (<i>Portuguese</i>) Sign Language Interpretation

Communication between Deaf and hearing individuals remains a persistent challenge requiring attention to foster inclusivity. Despite notable efforts in the development of digital solutions for sign language recognition (SLR), several issues persist, such as cross-platform interoperability and strategies for tokenizing signs to enable continuous conversations and coherent sentence construction. To address such issues, this paper proposes a non-invasive Portuguese Sign Language (Língua Gestual Portuguesa or LGP) interpretation system-as-a-service, leveraging skeletal posture sequence inference powered by long-short term memory (LSTM) architectures. To address the scarcity of examples during machine learning (ML) model training, dataset augmentation strategies are explored. Additionally, a buffer-based interaction technique is introduced to facilitate LGP terms tokenization. This technique provides real-time feedback to users, allowing them to gauge the time remaining to complete a sign, which aids in the construction of grammatically coherent sentences based on inferred terms/words. To support human-like conditioning rules for interpretation, a large language model (LLM) service is integrated. Experiments reveal that LSTM-based neural networks, trained with 50 LGP terms and subjected to data augmentation, achieved accuracy levels ranging from 80% to 95.6%. Users unanimously reported a high level of intuition when using the buffer-based interaction strategy for terms/words tokenization. Furthermore, tests with an LLM—specifically ChatGPT—demonstrated promising semantic correlation rates in generated sentences, comparable to expected sentences