Search

The flavor of Hunan fuzhuan brick tea (HFBT), as a typical post-fermented dark tea, is one of the crucial properties reflecting its quality and influenced by phytochemical components. However, the key components related to the quality of HFBT remain unclear. Thus, this study investigated the relationship between 6 sensory attributes and 56 phytochemical components of 27 HFBT, and identified key substances using multivariate statistical analysis. Results showed that the quality characteristics of HFBT can be categorized as bitter-astringency, mellow, and umami-sweet type. Among these, bitterness, astringency, and sweetness were dominant. Further, a total of 32 phytochemical components were identified as key contributors to the quality characteristic of HFBT through the partial least squares regression (PLSR) analysis and dose over threshold (Dot) factor, including 8 flavonol glycosides, 7 overall indicators, 5 phenolic acids, 5 amino acids, 4 catechin components, 2 organic acids, and 1 alkaloid. Among them, 19 phytochemical components together influence bitterness, astringency, and sweetness. In conclusion, this study provided a reference base for improving the quality of HFBT, and the subsequent experiment will further explore key components related to sourness, umami, and overall taste especially the isomerism and multivariate taste characteristics of amino acids in dark tea. [Display omitted] • The quality of Hunan fuzhuan brick tea was classified 3 types based on QDA. • The phenolics importantly contribute to the taste of Hunan fuzhuan brick tea. • Flavonol glycosides had low threshold and high astringency contribution. • Amino acids may exist as isomers in tea process, thereby affecting umami expression. [ABSTRACT FROM AUTHOR]

EEG signals exhibit commonality and variability across subjects, sessions, and tasks. But most existing EEG studies focus on mean group effects (commonality) by averaging signals over trials and subjects. The substantial intra- and inter-subject variability of EEG have often been overlooked. The recently significant technological advances in machine learning, especially deep learning, have brought technological innovations to EEG signal application in many aspects, but there are still great challenges in cross-session, cross-task, and cross-subject EEG decoding. In this work, an EEG-based biometric competition based on a large-scale M3CV (A Multi-subject, Multi-session, and Multi-task Database for investigation of EEG Commonality and Variability) database was launched to better characterize and harness the intra- and inter-subject variability and promote the development of machine learning algorithm in this field. In the M3CV database, EEG signals were recorded from 106 subjects, of which 95 subjects repeated two sessions of the experiments on different days. The whole experiment consisted of 6 paradigms, including resting-state, transient-state sensory, steady-state sensory, cognitive oddball, motor execution, and steady-state sensory with selective attention with 14 types of EEG signals, 120000 epochs. Two learning tasks (identification and verification), performance metrics, and baseline methods were introduced in the competition. In general, the proposed M3CV dataset and the EEG-based biometric competition aim to provide the opportunity to develop advanced machine learning algorithms for achieving an in-depth understanding of the commonality and variability of EEG signals across subjects, sessions, and tasks. [ABSTRACT FROM AUTHOR]