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The goal of the Vilhelm Hammershøi Digital Archive project of the National Gallery of Denmark is to understand the Danish painter Vilhelm Hammershøi's painting methods by compiling a comprehensive amount of data on his use of materials and working methods through visual and technical examination of a large number of his paintings, and to make this information available to researchers and the public in an open access digital resource. A clear understanding of the full suite of pigments across the paintings requires determination of which materials comprise the palettes of the ground and paint layers. Scanning electron microscopy/energy‐dispersive x‐ray spectroscopy and macro x‐ray fluorescence spectroscopy were selected as the key analytical techniques due to their ability to yield chemical information at the elemental level. This article presents a method that combines unsupervised machine learning and cluster analysis techniques, to automatically reduce the large x‐ray spectral data to sets of distinct clusters that share similar spectra, making it possible to identify materials more precisely. The proposed method allowed the grouping of materials by chemical composition, which enabled an optimal understanding of the pigments used in the ground layers sampled from a large number of paintings as well as in the paint layer examined at the surface of one selected painting. The method performed well when compared with other well‐established data mining techniques, and it helped reduce the time necessary for the interpretation of the analytical results significantly. Through this approach, a basis for a more nuanced view of Hammershøi's artistic idea and technical development will be generated. [ABSTRACT FROM AUTHOR]

A methodology for characterizing vegetal taxonomic groups from the voltammetric fingerprints of polyphenolic components of seeds is described. It is based on recording the voltammetric response of microparticulate films deposited on glassy carbon electrodes from seed extracts using different organic solvents. The obtained responses in contact with aqueous electrolytes provided characteristic voltammetric profiles at the level of genera/subgenera and/or families using bivariant and multivariant chemometric methods. The voltammograms of 14 species from 5 different families provided family-characteristic patterns. Analysis of voltammetric responses for a set of 20 species of the Asparagus genus from four continents permits to discriminate the genetic lines represented by the Asparagus, Protasparagus and Myrsiphyllum subgenera. [ABSTRACT FROM AUTHOR]

The cover picture shows an image of an Asparagus densiflorus (Kunth) Jessop from Botanical Garden of the University of Valencia, Spain. The image is based on the article titled “Access to Phylogeny from Voltammetric Fingerprints of Seeds: the Asparagus Case”, by A. Doménech‐Carbó et al. DOI: 10.1002/elan.201600588 [ABSTRACT FROM AUTHOR]