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We compare the application of different modeling strategies in order to predict physical properties of five different industrial pectin formulations based on near‐infrared spectral data. Methods from the chemometric toolbox, such as partial least squares regression (PLS1 and PLS2) and ridge regression, were employed and compared to the performance of a 1‐D convolutional neural network (CNN). The pectin formulations were modeled in two major scenarios, individually using local models, and jointly using global models, which resulted in better prediction performance of the 1‐D CNN. [ABSTRACT FROM AUTHOR]

Connections between crystal chemistry and critical temperature T c have been in the focus of superconductivity, one of the most widely studied phenomena in physics, chemistry, and materials science alike. In most Fe-based superconductors, materials chemistry and physics conspire so that T c correlates with the average anion height above the Fe plane, i.e., with the geometry of the FeAs 4 or FeCh 4 (Ch = Te, Se, or S) tetrahedron. By synthesizing Fe 1- y Se 1 -x S x (0 ≤ x ≤ 1; y ≤ 0.1), we find that in alloyed crystals T c is not correlated with the anion height like it is for most other Fe superconductors. Instead, changes in T c ( x ) and tetragonal-to-orthorhombic (nematic) transition T s ( x ) upon cooling are correlated with disorder in Fe vibrations in the direction orthogonal to Fe planes, along the crystallographic c -axis. The disorder stems from the random nature of S substitution, causing deformed Fe(Se,S) 4 tetrahedra with different Fe-Se and Fe-S bond distances. Our results provide evidence of T c and T s suppression by disorder in anion height. The connection to local crystal chemistry may be exploited in computational prediction of new superconducting materials with FeSe/S building blocks.