A new analytical approach based on high-performance liquid chromatography with diode array detector (HPLC-DAD) and multivariate data analysis was applied and assessed for analyzing the red dye extracted from cochineal insects, used in precious historical textiles. The most widely used method of analysis involves quantification of specific minor compounds (markers), using HPLC-DAD. However, variation in the cochineal markers concentration, use of aggressive dye extraction methods and poor resolution of HPLC chromatograms can compromise the identification of the precise insect species used in the textiles. In this study, a soft extraction method combined with a new dye recovery treatment was developed, capable of yielding HPLC chromatograms with good resolution, for the first time, for historical cochineal-dyed textiles. After principal components analysis (PCA) and mass spectrometry (MS), it was possible to identify the cochineal species used in these textiles, in contrast to the accepted method of analysis. In order to compare both methodologies, 7 cochineal species and 63 historical cochineal insect specimens were analyzed using the two approaches, and then compared with the results for 15 historical textiles in order to assess their applicability to real complex samples. The methodology developed here was shown to provide more accurate and consistent information than the traditional method. Almost all of the historical textiles were dyed with Porphyrophora sp. insects. These results emphasize the importance of adopting the proposed methodology for future research on cochineal (and related red dyes). Mild extraction methods and HPLC-DAD/MS(n) analysis yield distinctive profiles, which, in combination with a PCA reference database, are a powerful tool for identifying red insect dyes.

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The colorant behaviour of cochineal and kermes insect dyes in 141 experimentally-dyed and 28 artificially-aged samples of silk and wool was investigated using ultra-high performance liquid chromatography coupled to photodiode array detector (UHPLC-PDA), liquid chromatography electrospray ionisation mass spectrometry (LC-ESI-MS) and image scanning electron microscopy - energy dispersive X-ray spectroscopy (SEM-EDX). Partial-least squares discriminant analysis (PLS-DA) was then used to model the acquired UHPLC-PDA data and assess the possibility of discriminating cochineal insect species, as well as their correspondent dyed and aged reference fibres. The resulting models helped to characterize a set of 117 red samples from 95 historical textiles, in which UHPLC-PDA analyses have reported the presence of cochineal and kermes insect dyes. Analytical investigation of the experimentally-dyed and artificially-aged fibres has demonstrated that the ratio of compounds in the insects dye composition can change, depending on the dyeing conditions applied and the type of fibres used. Similarities were observed when comparing the UHPLC-MS and SEM-EDX results from the dyed and aged references with the historical samples. This was verified with PLS-DA models of the chromatographic data, facilitating the classification of the cochineal species present in the historical samples. The majority of these samples were identified to contain American cochineal, which is in agreement with historical and dye identification literature that describe the impact of this dyestuff into European and Asian dyeing practices, after the Iberian Expansion in the 16th century. The analytical results emphasize the importance of using statistical data interpretation for the discrimination of cochineal dyes, besides qualitative and quantitative evaluation of chromatograms. Hence, the combination of UHPLC-PDA with a statistical classification method, such as PLS-DA, has been demonstrated to be an advisable approach in future investigations to assess closely related species of natural dyes in historical textile samples. This is particularly important when aiming to achieve more accurate interpretations about the history of works of art, or the application of natural dyes in old textile production.