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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.

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Non-destructive spectroscopic analysis combined with machine learning rapidly provides information on the identity and content of plasticizers in PVC objects of heritage value. For the first time, a large and diverse collection of more than 100 PVC objects in different degradation stages and of diverse chemical compositions was analysed by chromatographic and spectroscopic techniques to create a dataset used to construct classification and regression models. Accounting for this variety makes the model more robust and reliable for the analysis of objects in museum collections. Six different machine learning classification algorithms were compared to determine the algorithm with the highest classification accuracy of the most common plasticizers, based solely on the spectroscopic data. A classification model capable of the identification of di(2-ethylhexyl) phthalate, di(2-ethylhexyl) terephthalate, diisononyl phthalate, diisodecyl phthalate, a mixture of diisononyl phthalate and diisodecyl phthalate, and unplasticized PVC was constructed. Additionally, regression models for quantification of di(2-ethylhexyl) phthalate and di(2-ethylhexyl) terephthalate in PVC were built. This study of real-life objects demonstrates that classification and quantification of plasticizers in a general collection of degraded PVC objects is possible, providing valuable data to collection managers.

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In this study, a method was developed for the determination of five neonicotinoid pesticides (acetamiprid, clothianidin, imidacloprid, thiacloprid, and thiamethoxam) in propolis. Two sample preparation methods were tested: solid-phase extraction and the quick, easy, cheap, effective, rugged, and safe (QuEChERS) method. The identities of analytes were confirmed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in the selected reaction monitoring mode. Solid-phase extraction resulted in cleaner extracts; therefore, the SPE-LC-MS/MS method was validated according to the SANTE protocol in triplicate at two spiking levels (10 ng/g and 50 ng/g). The average recoveries of analytes ranged from 61% to 101%, except for clothianidin (10-20%). The LOD ranged from 0.2 ng/g to 4.4 ng/g, whereas the LOQ was in the range of 0.8 ng/g-14.7 ng/g. In order to compensate for the matrix effect, matrix-matched calibration was used. Good accuracy (relative error: 1.9-10.4%) and good linearity (R2 > 0.991) were obtained for all compounds. The optimised method was applied to 30 samples: 18 raw propolis and 12 ethanol tinctures. Acetamiprid, imidacloprid, and thiacloprid were detectable in seven samples but were still below the LOQ. This study is the first to report the determination of several neonicotinoid residues in propolis.

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Nine purine and pyrimidine bases were separated and determined simultaneously using reversed phase (RP) high performance liquid chromatography (HPLC) in some food samples and biological fluids. Chromatographic behavior of these ionizable compounds highly depends on the interactions with the solvent as confirmed experimentally and by calculation of distribution of this species as a function of pH. Chromatograms show the optimal separation of five purine (uric acid, hypoxanthine, xanthine, adenine, and guanosine), and four pyrimidine (cytosine, uracil, cytidine and tymine) bases at pH around four. Accordingly, acetate buffer was selected due to high buffer capacity in this region. By variation of pH, concentration of buffer and volume ratio between buffer and methanol, we found that a mixture of 50 mM acetate buffer of pH 4.0 ± 0.1 with 3 % of methanol ensures reproducibility, complete separation in less than 15 minutes and compatibility with MS detection. Developed method was validated and applied for the analysis of complex clinical and beverage samples.

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