Component spectra extraction and quantitative analysis for preservative mixtures by combining terahertz spectroscopy and machine learning

Preservatives are universally used in synergistic combination to enhance antimicrobial effect. Identify compositions and quantify components of preservatives are crucial steps in quality monitoring to guarantee merchandise safety. In the work, three most common preservatives, sorbic acid, potassium sorbate and sodium benzoate, are deliberately mixed in pairs with different mass ratios, which are supposed to be the unknown multicomponent systems and measured by terahertz (THz) time-domain spectroscopy. Subsequently, three major challenges have been accomplished by machine learning methods in this work. The singular value decomposition (SVD) effectively obtains the number of components in mixed preservatives. Then, the component spectra are successfully extracted by non-negative matrix factorization (NMF) and self-modeling mixture analysis (SMMA), which match well with the measured THz spectra of pure reagents. Moreover, the support vector machine for regression (SVR) designed an underlying model to the target components and simultaneously identify contents of each individual component in validation mixtures with decision coefficient R-2 = 0.989. By taking advantages of the fingerprint-based THz technique and machine learning methods, our approach has been demonstrated the great potential to be served as a useful strategy for detecting preservative mixtures in practical applications. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study utilizes machine learning methods to identify and quantify the components of preservatives, successfully addressing challenges such as component identification and content recognition of mixed preservatives through terahertz time-domain spectroscopy. The application of fingerprint-based terahertz technology and machine learning methods demonstrates great potential in detecting preservative mixtures in practical applications.