Matija Strlič

Reproducing olfactory experiences remains a major challenge, with implications for heritage science, sensory analysis, and the fragrance industry. This work presents an integrated perfume-engineering framework that combines chemical engineering principles with computational modeling to reproduce a target scent. The methodology quantifies the olfactory profile of a reference fragrance, predicts the evaporation behavior of its constituent compounds, and combines first-principles thermodynamic modeling with machine learning and optimization to identify molecular substitutes and propose a cost-efficient, sustainable reproduction mixture. The framework includes quantitative characterization of the target scent, estimation of gas-phase contributions through evaporation dynamics, generation of candidate molecules, and solution of an optimization problem to determine mixture composition. The approach is tested on a perfume formulation with known composition reported in the literature. Results show that 1,2-dimethoxybenzene can act as an effective and cost-effective substitute for vanillin while closely matching the target olfactory profile. Overall, the study demonstrates the potential of integrated computational methods for rational fragrance design.

External reflectance Fourier transform infrared (ER‐FTIR) spectroscopy offers a contactless, efficient, and cost‐effective method for dating cultural heritage artefacts. This study investigated the application of ER‐FTIR to historic manuscript binding materials, primarily leather, in order to establish a methodology for spectra collection to be used in molecular decay dating. A systematic measurement approach was developed to ensure reliable results through selecting the optimal parameters for data collection. The research revealed that minimizing visible damage and surface contaminants is crucial for obtaining clear spectra of leather book binding samples. Spectral variations were observed between different regions of artefacts, such as spines and covers. These findings highlight the potential of ER‐FTIR in enhancing the preservation and analysis of cultural heritage materials. However, further research and refinement of ER‐FTIR methodologies are necessary to fully realize their capabilities in dating and analyzing historic objects.

Ancient Egyptian mummification was a mortuary practice aimed at preserving the body and soul for the afterlife, achieved through a detailed ritual of embalming using oils, waxes, and balms. While most research on Egyptian mummified bodies has so far been conducted in European collections, our study focuses on the collection of the Egyptian Museum in Cairo. The goal was to evaluate whether contemporary smells reflect the mummification materials and, if so, what information can be of value to collection interpretation and conservation. We combined panel-based sensory analyses with gas chromatography-mass spectrometry-olfactometry (GC-MS-O), microbiological analysis, and historical and conservation research. Apart from differences in odor intensity, the sensory analyses highlighted common olfactory descriptors for all samples: "woody", "spicy", and "sweet". GC-MS-O identified four categories of volatiles based on their origin: (i) original mummification materials; (ii) plant oils used for conservation; (iii) synthetic pesticides; and (iv) microbiological deterioration products. However, the use of insect repellents similar in composition to the original mummification materials makes it challenging to attribute the origin of some compounds. Clusters based on the chemical and olfactory profiles of the smells emerged, suggesting similarities based on the archeological period, conservation treatments, and materiality.

Non-destructive, fast, and accurate methods of dating are highly desirable for many heritage objects. Here, we present and critically evaluate the use of near-infrared (NIR) spectroscopic data combined with three supervised machine learning methods to predict the publication year of paper books dated between 1851 and 2000. These methods provide different accuracies; however, we demonstrate that the underlying processes refer to common spectral features. Regardless of the machine learning method used, the most informative wavelength ranges can be associated with C-H and O-H stretching first overtone, typical of the cellulose structure, and N-H stretching first overtone from amide/protein structures. We find that the expected influence of degradation on the accuracy of prediction is not meaningful. The variance-bias decomposition of the reducible error reveals some differences among the three machine learning methods. Our results show that two out of the three methods allow predictions of publication dates in the period 1851-2000 from NIR spectroscopic data with an unprecedented accuracy of up to 2 years, better than any other non-destructive method applied to a real heritage collection.