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.

翻译：复杂混合物（无论其性质如何）的逆向工程在当今具有重要意义。快速评估新型商业产品对环境的潜在毒性是一项真正的分析挑战。数字工具（数据库、化学计量学、机器学习等）与分析技术（拉曼光谱、近红外光谱、质谱法等）的发展将有助于识别潜在的有毒分子。本文以洗涤剂产品为例，其成分可能对人类或环境造成危害，因此需要精确识别和定量以进行质量控制和法规监管。通过结合多种数字工具（光谱数据库、混合物数据库、实验设计、化学计量学/机器学习算法等）与不同样品制备方法（原始样品或若干浓缩/稀释样品）的拉曼光谱，我们成功识别了混合物的成分并估算了其组成比例。在不同分析工具中实施此类策略，可节省在各种基质中进行污染物识别和污染评估的时间。该策略同样适用于工业领域的产品或原材料控制以及质量控制。