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Abstract Confirmed since the twelth century, the Saxon community in Transylvania developed over the years in a rigorous powerful society, with its own lifestyle, social, economic, cultural and artistic standards. Together with research in historic documents, this society is now revealed by material studies of eighteenth– twentieth century objects in the Emil Sigerius collection, preserved in the ASTRA Museum, Sibiu, Romania. Embroideries made in Saxon households as well as representative Saxon coats manufactured in workshops, and dated between 1892 and 1908 (years embroidered on objects), were studied in terms of dye analysis, in order to understand if adoption of new materials goes together with the stylistic improvements. Dye analysis were performed by liquid chromatography with UV–vis and mass spectrometric detection (LC-DAD-MS), nowadays the most appropriate technique for the characterization and identification of colour components in heritage textiles. MS detection, with a triple quadrupole mass analyser, was used to achieve clear identification of each dye in a complex matrix. Construction of suitable spectral libraries through studies on standard dyes and dyed textiles definitely improved the ability to detect natural and early synthetic with greater certainty. Identification of fuchsine (1856), methyl violet (1861), orange II (1876) brilliant green (1879), quinoline yellow (1881) and rhodamine B (1887) in textiles dated about 20 years after the dyes first synthesis prove that transition from natural to synthetic dyes in the Saxon community was very fast, especially for textiles made in specialized workshops. The analytical configurations used prove their competence in textile studies, based on natural and early synthetic dyes research, and promotes the use of advanced analytical techniques in heritage science applications.

In oenology, statistical analyses are used for descriptive purposes, mostly with separate sensory and chemistry data sets. Cases that combine them are mostly supervised, usually seeking to optimize discrimination, classification, or prediction power. Unsupervised methods are used as preliminary steps to achieving success in supervised models. However, there is potential for unsupervised methods to combine different data sets into comprehensive, information-rich models. This study detailed stepwise strategies for creating data fusion models using unsupervised techniques at different levels. Principal component analysis (PCA) and multiple factor analysis (MFA) were used to combine five data blocks (four chemistry and one sensory). The model efficiency and configurational similarity were evaluated using eigenvalues and regression vector (RV) coefficients, respectively. The MFA models were less efficient than PCA, having gradual distributions of eigenvalues across model dimensions. The MFA models were more representative than PCA, as indicated by high RV coefficients between MFA and each individual block. Therefore, MFA approaches were better suited for multi-modal data than PCA. This work approached data fusion systematically and showed the type of decisions that must be made and how to evaluate their consequences. Proper integration of data sets, instead of concatenation, is an important aspect to consider in multi-modal data fusion.