ASCA

Using chemometrics to characterise and unravel the near infra-red spectral changes induced in aubergine fruit by chilling injury as influenced by storage time and temperature

The early non-destructive detection of chilling injury (CI) in aubergine fruit was investigated using spectroscopy. CI is a physiological disorder that occurs when the fruit is subjected to temperatures lower than 12 °C. Reference measurements of CI were acquired by visual appearance analysis, measuring electrolyte leakage (EL), mass loss and firmness evaluations which demonstrated that even before three days of storage at 2 °C, the CI process was initiated.

Predicting Outcomes in Pediatric Crohn's Disease for Management Optimization: Systematic Review and Consensus Statements From the Pediatric Inflammatory Bowel Disease–Ahead Program

Background & Aims: A better understanding of prognostic factors within the heterogeneous spectrum of pediatric Crohn's disease (CD) should improve patient management and reduce complications. We aimed to identify evidence-based predictors of outcomes with the goal of optimizing individual patient management. Methods: A survey of 202 experts in pediatric CD identified and prioritized adverse outcomes to be avoided. A systematic review of the literature with meta-analysis, when possible, was performed to identify clinical studies that investigated predictors of these outcomes.

Confidence ellipsoids for ASCA models based on multivariate regression theory

In analysis of variance simultaneous component analysis, permutation testing is the standard way of assessing uncertainty of effect level estimates. This article introduces an analytical solution to the assessment of uncertainty through classical multivariate regression theory. We visualize the uncertainty as ellipsoids, contrasting these to data ellipsoids. This is further extended to multiple testing of effect level differences. Confirmatory and intuitive results are observed when applying the theory to previously published data and simulations.

Milk renneting: study of process factor Influences by FT-NIR spectroscopy and chemometrics

The dairy industry is continuously developing new strategies to obtain healthier dairy products preserving expected properties. However, when modifying a food process, the reassessment of each parameters and their interaction should be considered as highly influencing the final quality. Among others, rennet process features are fundamental for both sensory properties and typical characteristics of a cheese. In this contest, the research addresses the development of a FT-NIR spectroscopic method, coupled with chemometrics, for the study of the effect of process variables on milk renneting.

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