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The exploration and analyses of chemical components in (extra‐)terrestrial geological materials (such as asteroids and meteorites) are insightful in modern research. Laser‐induced breakdown spectroscopy (LiBS) is a popular method for analyzing the chemical attributes of geologic samples—which scientists use to study and understand planetary bodies and their complex histories. In the literature, several machine learning models that produce high‐accuracy predictions have been proposed. In our work, we compared the performances of such models in predicting elemental abundances on a certain spectroscopic dataset. Models included partial least squares (PLS), extreme gradient boost machines (XGB), neural networks, and linear models. In our results, we showed how PLS and XGB are superior in terms of high predictive power, their ability to generalize, and their reasonably efficient runtimes. In addition, we proposed Ensemble models that aggregate predictions of top‐tier models and observed that they can be desirable. We intend to gain better understanding of how these models perform in predicting elemental compositions on specific spectrum (LIBS) datasets. In this paper, we compare several machine learning models to identify those who performed best in a specific spectroscopic datasets. The models investigated include partial least squares, Lasso regression, support vector regression, artificial and convolutional neural networks, and extreme gradient boosting machines. We then propose an Ensemble model aggregating the predictions of the top‐tier, models and we have found that it performs significantly well. [ABSTRACT FROM AUTHOR]

Transfer learning (TL), the sub‐discipline of machine learning devoted to learning from different domains, has gained increasing attention over the past decade. With the current contribution, we aim at giving a concise overview on theory, concepts, and applications of TL from a chemometrician's perspective and draw some connections to previous work on calibration model updating/adaptation and calibration transfer. Furthermore, we provide a demonstration of the application of TL in analytical chemistry and discuss the benefits and challenges associate with its use for real‐world problems. We conclude the paper by discussing some open problems and by contemplating on future research directions. [ABSTRACT FROM AUTHOR]

Edmund R. Malinowski is well known for his factor analysis‐based work; he is clearly less well known for his chemical model‐based analyses of chemical data. In this contribution, we discuss the, at the time innovative, idea of subjecting the primary model‐free analysis results to a secondary quantitative model‐based evaluation. Two examples from his research serve as illustrations: the complexation of Cu2+ with cyclodextrin and the dimerisation and trimerisation of methylene blue. In both examples, the spectrophotometric titration data are first analysed by window factor analysis, WFA, resulting in the concentration profiles. These primary, model‐free results allowed Malinowski to gain qualitative insight in the chemical processes. Additional quantitative analyses of the concentration profiles, based on the previously obtained reaction model, resulted in the numerical values of the underlying equilibrium constants. This contribution relates and compares this methodology with alternative ways of analysing the same data. Model‐free analyses result in the shapes of concentration profiles and spectra. Secondary analysis of the concentration profiles is possible and can result in quantitative information, for example, rate or equilibrium constants. It is advantageous to avoid this 2‐step process by executing a straight model‐based analysis. [ABSTRACT FROM AUTHOR]