Salt inhibitors have been receiving increasing attention as potential innovative systems to counteract salt damage by preventing crystallisation of the salts within the natural stone structure- and related disruptive action-of built heritage. Especially, we focus on biomass-derived inhibitor systems featuring complete solubility in water or alcohol and intrinsic non-toxicity. Moving from the promising results obtained, the present study aims to develop research concerning the possibility of rationalizing the collected data sets and making them amenable to statistical analysis. This paper reports on an exploratory application of one of the most powerful methods in chemometrics, i.e., principal component analysis (PCA), in this area. It will be seen that this method is a promising tool to extract information from a series of tests to optimize them and to reduce the level of “noise” present in the data collected, i.e., unnecessary information or experimental errors, and to suggest new directions.

Principal component analysis (PCA) combined with naturally occurring crystallization inhibitors: An integrated strategy for a more sustainable control of salt decay in built heritage

Marrocchi A.
2021

Abstract

Salt inhibitors have been receiving increasing attention as potential innovative systems to counteract salt damage by preventing crystallisation of the salts within the natural stone structure- and related disruptive action-of built heritage. Especially, we focus on biomass-derived inhibitor systems featuring complete solubility in water or alcohol and intrinsic non-toxicity. Moving from the promising results obtained, the present study aims to develop research concerning the possibility of rationalizing the collected data sets and making them amenable to statistical analysis. This paper reports on an exploratory application of one of the most powerful methods in chemometrics, i.e., principal component analysis (PCA), in this area. It will be seen that this method is a promising tool to extract information from a series of tests to optimize them and to reduce the level of “noise” present in the data collected, i.e., unnecessary information or experimental errors, and to suggest new directions.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1504812
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