English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Partial Least Squares regression (PLS) was used to elaborate the prediction models of the different chemical families i.e. wt% paraffins, naphthenes, and wt% and mol/100 g monoaromatics, diaromatics+ and total aromatics from Near InfraRed spectra (NIR) of mid-distillates covering a large range of chemical compositions. Cluster analysis was used to reveal the chemical similarities between samples and to organize in clusters the calibration data base. The relationships between NIR spectra and modeled properties were well adapted for most of the prediction models in twice the interval of confidence at 95% of the reference methods after clustering of the data base into three clusters. Cluster analysis was necessary to improve the prediction quality of the PLS models.

Partial Least Square Modeling for the Control of Refining Processes on Mid-Distillates by Near Infrared Spectroscopy