Matthias Otto

Matthias Otto is an analytical chemistry researcher identified in the full title-page byline of the 2004 IUPAC report on multispecies calibration, alongside Klaus Danzer and Lloyd A. Currie. That primary page gives his affiliation as the analytical chemistry department at TU Bergakademie Freiberg. The university's own directory independently lists Matthias Otto among the institute's former staff.^[ref][ref]

The report concerns multivariate calibration: relating several chemical species and their quantities to several measured signals, with uncertainty and model validation treated explicitly. Its sections distinguish classical multivariate calibration, inverse calibration and error diagnosis. This is a coauthored methodological contribution; the byline alone does not assign separate individual roles.^[ref]

An original two-species model illustrates the scientific problem. Let concentration values $c_1,c_2$ and two instrument signals $s_1,s_2$ be expressed in chosen calibrated units, withThe signals $(5,8)$ imply $c_2=s_2-s_1=3$ and $c_1=2s_1-s_2=2$. If the second channel were instead exactly twice the first, giving response matrix $\left(\begin{array}{cc}1& 1\\ 2& 2\end{array}\right)$, readings $(5,10)$ would determine only $c_1+c_2=5$. Merely recording two channels would not make the two species separately identifiable.

This distinction is why calibration mixtures must supply independent information about the species responses and why successful interpolation does not replace validation. Near-dependent response columns can also amplify measurement noise even when a formal inverse exists. Singular value decomposition and the actual calibration design help diagnose such limitations; a regression coefficient is not automatically an independently established chemical effect.

A measurement program can vary mixture composition, collect the same channels under controlled conditions, and test predictions on mixtures not used to fit the response. The example is an ideal calculation, not experimental data from the report. It explains why chemometrics combines chemical knowledge, experimental design and statistical assessment rather than treating a fitted number as a complete analytical result.