Diverse Phytochemicals and Bioactivities in the Ancient Fruit and Modern Functional Food Pomegranate (Punica granatum).

Having served as a symbolic fruit since ancient times, pomegranate (Punica granatum) has also gained considerable recognition as a functional food in the modern era. A large body of literature has linked pomegranate polyphenols, particularly anthocyanins (ATs) and hydrolyzable tannins (HTs), to the health-promoting activities of pomegranate juice and fruit extracts. However, it remains unclear as to how, and to what extent, the numerous phytochemicals in pomegranate may interact and exert cooperative activities in humans. In this review, we examine the structural and analytical information of the diverse phytochemicals that have been identified in different pomegranate tissues, to establish a knowledge base for characterization of metabolite profiles, discovery of novel phytochemicals, and investigation of phytochemical interactions in pomegranate. We also assess recent findings on the function and molecular mechanism of ATs as well as urolithins, the intestinal microbial derivatives of pomegranate HTs, on human nutrition and health. A better understanding of the structural diversity of pomegranate phytochemicals as well as their bioconversions and bioactivities in humans will facilitate the interrogation of their synergistic/antagonistic interactions and accelerate their applications in dietary-based cancer chemoprevention and treatment in the future

A data-based approach for multivariate model predictive control performance monitoring

An intelligent statistical approach is proposed for monitoring the performance of multivariate model predictive control (MPC) controller, which systematically integrates both the assessment and diagnosis procedures. Model predictive error is included into the monitored variable set and a 2-norm based covariance benchmark is presented. By comparing the data of a monitored operational period with the "golden" user-predefined one, this method can properly evaluate the performance of an MPC controller at the monitored operational stage. Characteristic direction information is mined from the operating data and the corresponding classes are built. The eigenvector angle is defined to describe the similarity between the current data set and the established classes, and an angle-based classifier is introduced to identify the root cause of MPC performance degradation when a poor performance is detected. The effectiveness of the proposed methodology is demonstrated in a case study of the Wood–Berry distillation column system

Performance monitoring of MPC based on dynamic principal component analysis

A unified framework based on the dynamic principal component analysis (PCA) is proposed for performance monitoring of constrained multi-variable model predictive control (MPC) systems. In the proposed performance monitoring framework, the dynamic PCA based performance benchmark is adopted for performance assessment, while performance diagnosis is carried out using a unified weighted dynamic PCA similarity measure. Simulation results obtained from the case study of the Shell process demonstrate that the use of the dynamic PCA performance benchmark can detect the performance deterioration more quickly compared with the traditional PCA method, and the proposed unified weighted dynamic PCA similarity measure can correctly locate the root cause for poor performance of MPC controller