A Multivariate Analysis-Driven Workflow to Tackle Uncertainties in Miniaturized NIR Data.

This study focuses on exploring and understanding measurement errors in analytical procedures involving miniaturized near-infrared instruments. Despite recent spreading in different application fields, there remains a lack of emphasis on the accuracy and reliability of these devices, which is a critical concern for accurate scientific outcomes. The study investigates multivariate measurement errors, revealing their complex nature and the influence that preprocessing techniques can have.

On the Importance of Investigating Data Structure in Miniaturized NIR Spectroscopy Measurements of Food: The Case Study of Sugar.

Alongside the increasing proofs of efficacy of miniaturized NIR instruments in food-related scenarios, it is progressively growing the number of end-users, even incentivized by the low-cost of the sensors. While attention is paid to the analytical protocol-from sampling to data collection, up to the data processing, the importance of error investigation in raw data is generally underestimated. Understanding the sources and the structure of uncertainty related to the raw data improves the quality of measurements and suggests the correct planning of the experiments, as well as helps in chemometric model development.

Local versus central assessment of venographies in a multicenter trial on the prevention of deep vein thrombosis in neurosurgery.

Venography is the diagnostic method of choice for end-point measurement in multicenter trials on the prevention of postoperative deep vein thrombosis (DVT). The aim of the study was to determine the inter-observer agreement between the local and central assessment of venographies in a multicenter trial comparing enoxaparin and placebo in the prevention of DVT after elective neurosurgery. The study was run in seven centers experienced in venography trials on DVT prevention.