Natural polyphenols are playing an increasingly important role as renewable biomass in the substitution of fossil-based resources. Applications ranging from the materials sector to the biomedical and phytosanitary contexts lead to an increased demand and an increased need of a detained understanding of structural key features. The first aspect leads to an increase in production of tannins and lignins, the main representatives of natural polyphenols, and the presence of various technical lignins and tannins of questionable quality in terms of compositional and structural purity. The second aspect becomes important when applications and procedures face approval by the competent authorities. Obviously, both contexts are connected in terms of quality control aspects and the certification of origins and applied extraction processes. In order to facilitate both structural characterization, otherwise cumbersome, and to introduce a quality control means, an analytical method is developed to perform a characterization (fingerprinting) of natural polyphenols using near-infrared spectroscopy (NIR). Preliminary tests performed on models of various complexity were pursued to determine the optimal conditions for the instrumental acquisition of the spectroscopic signals, in order to minimize the signal noise and maximize the reproducibility of the measurements. Samples of natural polyphenols in the form of both technical lignins and tannins, as well as mixtures of them, also in the presence of common impurities and process remains were analyzed for the subsequent fingerprinting-phase based on the correlation between the spectroscopic signals and structural properties of the polyphenols that were determined independently in house. Differentiating between classes of polyphenols is possible as well as the identification of mixtures. It was further possible to identify the blending of natural polyphenols with artificial polyphenol-based antioxidants.

Valentino, D., Cruz Muñoz, E., Termopoli, V., Orlandi, M., Lange, H., Ballabio, D. (2025). Near-Infrared Spectroscopy-Based Fingerprinting of Natural Polyphenols and Their Origin Using Machine-Learning. Intervento presentato a: NIR2025 – XXII International Conference on Near Infrared Spectroscopy, Roma, Italia.

Near-Infrared Spectroscopy-Based Fingerprinting of Natural Polyphenols and Their Origin Using Machine-Learning

Enmanuel Cruz Muñoz;Veronica Termopoli;Marco Orlandi;Heiko Lange
;
Davide Ballabio
2025

Abstract

Natural polyphenols are playing an increasingly important role as renewable biomass in the substitution of fossil-based resources. Applications ranging from the materials sector to the biomedical and phytosanitary contexts lead to an increased demand and an increased need of a detained understanding of structural key features. The first aspect leads to an increase in production of tannins and lignins, the main representatives of natural polyphenols, and the presence of various technical lignins and tannins of questionable quality in terms of compositional and structural purity. The second aspect becomes important when applications and procedures face approval by the competent authorities. Obviously, both contexts are connected in terms of quality control aspects and the certification of origins and applied extraction processes. In order to facilitate both structural characterization, otherwise cumbersome, and to introduce a quality control means, an analytical method is developed to perform a characterization (fingerprinting) of natural polyphenols using near-infrared spectroscopy (NIR). Preliminary tests performed on models of various complexity were pursued to determine the optimal conditions for the instrumental acquisition of the spectroscopic signals, in order to minimize the signal noise and maximize the reproducibility of the measurements. Samples of natural polyphenols in the form of both technical lignins and tannins, as well as mixtures of them, also in the presence of common impurities and process remains were analyzed for the subsequent fingerprinting-phase based on the correlation between the spectroscopic signals and structural properties of the polyphenols that were determined independently in house. Differentiating between classes of polyphenols is possible as well as the identification of mixtures. It was further possible to identify the blending of natural polyphenols with artificial polyphenol-based antioxidants.
abstract + poster
natural polyphenols; near-infrared; machine-learning; fingerprinting; quality control
English
NIR2025 – XXII International Conference on Near Infrared Spectroscopy
2025
2025
reserved
Valentino, D., Cruz Muñoz, E., Termopoli, V., Orlandi, M., Lange, H., Ballabio, D. (2025). Near-Infrared Spectroscopy-Based Fingerprinting of Natural Polyphenols and Their Origin Using Machine-Learning. Intervento presentato a: NIR2025 – XXII International Conference on Near Infrared Spectroscopy, Roma, Italia.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/559012
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