Multiresponse and multiobjective latent variable optimization of modern analytical instrumentation for the quantification of chemically related families of compounds: Case study—Solid‐phase microextraction (SPME) applied to the quantification of analytes with impact on wine aroma

The optimized operation of modern analytical instrumentation is a critical but complex task. It involves the simultaneous consideration of a large number of factors, both qualitative and quantitative, where multiple responses should be quantified and several goals need to be adequately pondered, such as global quantification performance, selectivity, and cost. Furthermore, the problem is highly case specific, depending on the type of instrument, target analytes, and media where they are dispersed. Therefore, an optimization procedure should be conducted frequently, which implies that it should be efficient (requiring a low number of experiments), as simple as possible (from experimental design to data analysis) and informative (interpretable and conclusive). The success of this task is fundamental for achieving the scientific goals and to justify, in the long run, the high economic investments made and significant costs of operation. In this article, we present a systematic optimization procedure for the prevalent class of situations where multiple responses are available regarding a family of chemical compounds (instead of a single analyte). This class of problems conducts to responses exhibiting mutual correlations, for which, furthermore, several goals need to be simultaneously considered. Our approach explores the latent variable structure of the responses created by the chemical affinities of the compounds under analysis and the orthogonality of the interpretable extracted components to conduct their simultaneous optimization with respect to different analysis goals. The proposed methodology was applied to a real case study involving the quantification of a family of analytes with impact on wine aroma.