Sprouts and microgreens are a rich source of various bioactive compounds. Seeds of lentil, fenugreek, alfalfa, and daikon radish seeds were germinated and the contents of the polyamines agmatine (AGM), putrescine (PUT), cadaverine (CAD), spermidine (SPD), and spermine (SPM) in ungerminated seeds, sprouts, and microgreens were determined. In general, sprouting led to the accumulation of the total polyamine content. The highest levels of AGM (5392 mg/kg) were found in alfalfa microgreens, PUT (1079 mg/kg) and CAD (3563 mg/kg) in fenugreek sprouts, SPD (579 mg/kg) in lentil microgreens, and SPM (922 mg/kg) in fenugreek microgreens. A large increase in CAD content was observed in all three legume sprouts. Conversely, the nutritionally beneficial polyamines AGM, SPD, and SPM were accumulated in microgreens, while their contents of CAD were significantly lower. In contrast, daikon radish sprouts exhibited a nutritionally better profile of polyamines than the microgreens. Freezing and thawing of legume sprouts resulted in significant degradation of CAD, PUT, and AGM by endogenous diamine oxidases. The enzymatic potential of fenugreek sprouts can be used to degrade exogenous PUT, CAD, and tyramine at pH values above 5.

Museums and galleries house increasingly large collections of objects and contemporary art made of plastic materials, many of which undergo rapid material change. The main degradation processes of poly(vinyl chloride) (PVC) are elimination of HCl and plasticizer migration or leaching. This results in visible discolouration, stickiness and cracking. Degradation is known to be a multi-stage process that includes HCl elimination, formation of conjugated polyenes and cross-linking. Elimination of HCl begins due to structural irregularities (allylic and tertiary chlorides) and results in the formation of polyenes. When at least 7 conjugated double bonds are present, discolouration of PVC becomes visible. Non-invasive techniques, such as IR and Raman spectroscopy are used for polymer identification and plasticizer quantification. Plasticizer degradation and particularly the late stages of PVC degradation can be investigated using SEC, GC-MS, TGA and DSC. Studies in heritage collections have revealed that, apart from HCl, PVC objects emit 2-ethylhexanol and other volatile degradation products, however, there is currently no indication that HCl is emitted at usual indoor conditions. There seems to be a general lack of systematic research into PVC degradation at the conditions of storage and display, which could result in the development of dose-response functions and in the development of preventive conservation guidelines for the management of PVC collections.

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.

The suitability of Hansen solubility parameters as descriptors for modelling analyte retention during reversed-phase chromatographic experiments was investigated. A novel theoretical model using Hansen solubility parameters as the basis for a complete mathematical derivation of the model was developed. The theoretical model also includes the cavitation volumes of the analytes, which were calculated using ab initio density functional theory methods. A set of three homologous phthalates was used for experimental data collection and subsequent model construction. The training error and the generalization error of the model were additionally evaluated using a range of chemically diverse analytes. Statistical evaluation of the results revealed that the model is suitable for analyte retention prediction but is limited to the analytes used in the model construction. Therefore, the resulting theoretical model cannot be easily generalized. A retention anomaly attributed to the column temperature and mobile phase composition was experimentally observed and mathematically investigated.