  The reverse engineering of a complex mixture, regardless of its nature, has
become significant today. Being able to quickly assess the potential toxicity
of new commercial products in relation to the environment presents a genuine
analytical challenge. The development of digital tools (databases,
chemometrics, machine learning, etc.) and analytical techniques (Raman
spectroscopy, NIR spectroscopy, mass spectrometry, etc.) will allow for the
identification of potential toxic molecules. In this article, we use the
example of detergent products, whose composition can prove dangerous to humans
or the environment, necessitating precise identification and quantification for
quality control and regulation purposes. The combination of various digital
tools (spectral database, mixture database, experimental design, Chemometrics /
Machine Learning algorithm{\ldots}) together with different sample preparation
methods (raw sample, or several concentrated / diluted samples) Raman
spectroscopy, has enabled the identification of the mixture's constituents and
an estimation of its composition. Implementing such strategies across different
analytical tools can result in time savings for pollutant identification and
contamination assessment in various matrices. This strategy is also applicable
in the industrial sector for product or raw material control, as well as for
quality control purposes.
