title: Sample treatment considerations in the analysis of organic colorants by surface-enhanced Raman scattering.
abstract: The introduction of surface-enhanced Raman spectroscopy (SERS) in the field of cultural heritage has significantly improved the analysis of the organic dyes and their complexes that have been used as textile dyes and pigments in paintings and other polychrome works of art since antiquity. Over the last five years, a number of different procedures have been developed by various research groups. In this Article, we evaluate the effect of pretreating samples by exposing them to hydrofluoric acid (HF) vapor prior to SERS analysis, a step designed to hydrolyze the dye-metal complexes and increase analyte adsorption on the nanosized metallic support, thus enhancing the SERS signal. Materials studied include pure colorants, commercial lake pigments, and fibers from dyed textiles, as well as actual aged samples, such as microscopic fragments of lakes on paper and ancient pigments and glazes from several works of art, covering a wide range of time, from the second century B.C. to the early 20th century. In each case, SERS spectra obtained with or without HF hydrolysis were critically evaluated. The pretreatment with HF vapor resulted in faster analysis and increased sensitivity in most cases, with the exception of dyed silk fibers, where silk protein hydrolyzates were found to interfere with SERS analysis. As a final point, a two-step procedure including SERS on untreated and treated samples is proposed as a standard approach: by analyzing a sample first without hydrolysis, and then, following removal of the colloid, upon HF treatment, the best and most reliable results for a great number of dyes and substrates are assured.
authors.affiliation: Department of Scientific Research, The Metropolitan Museum of Art, New York, United States.
authors.forename: Federica
authors.initials: F
authors.lastname: Pozzi
authors.forename: John R
authors.initials: JR
authors.lastname: Lombardi
authors.forename: Silvia
authors.initials: S
authors.lastname: Bruni
authors.forename: Marco
authors.initials: M
authors.lastname: Leona