Liquid chromatography (LC) coupled to mass spectrometry (MS) is a powerful and versatile technique with several applications in analytical chemistry such as the untargeted analysis of complex mixtures of plant food bioactive compounds. In this framework, identification of compounds mainly relies on mass and fragmentation patterns derived from MS spectra of available databases. However, to develop automated tools for a rapid compound identification, prior knowledge of retention times (RTs) has been demonstrated to be a valid support to MS data to reduce the number of possible candidate structures. Unlike experimental methods, which are time-consuming and limited to a few classes of compounds, data-driven computational approaches can predict retention times for a wide range of compounds using only their molecular structures and physicochemical properties. In this research, Genetic Algorithms (GAs) coupled to Multiple Linear Regression (MLR) were applied to select the most relevant molecular descriptors to establish quantitative structure-retention relationships (QSRRs) aimed at predicting the retention times of plant food bioactive compounds across three different LC chromatographic systems. The statistical parameters showed model robustness and satisfactory predictive ability. Particular attention was paid to measuring the uncertainty of predictions and assessing their reliability based on the model applicability domain. Interpretation of the selected molecular descriptors provided valuable insight into the separation mechanism. Finally, the developed models were applied to predict the unknown retention times, for the three studied LC chromatographic systems, of a large library of plant food bioactive compounds, which were made freely available to further assist the research in the field of natural products.

One of the most challenging preprocessing steps in Raman spectroscopy is the removal of cosmic spikes that interfere with genuine Raman modes. Software packages offer built-in methods for their detection and removal, but these are not entirely effective and/or well-documented. In this work, we propose a new algorithm called ARCHER for cosmic spike correction, specifically designed for Hyperspectral Raman Imaging, that combines different steps and factorizations for effective and automatic cosmic spike detection. ARCHER can also detect saturated pixels, which must be necessarily removed after addressing any further multivariate analysis. The algorithm was optimized using D-optimal design to set its internal parameters. The validation was double-checked by application on different artificial images, facing several degrees of complexity to identify potential weaknesses. Finally, the algorithm was tested against real hyperspectral images in the presence of saturated and noisy pixels.

Although pyrite (FeS₂) is highly reactive in oxygen-rich environments and is expected to be largely consumed through the weathering zone, observations from Taiwan indicate that pyrite grains can survive and be transported by fluvial systems. This rapidly uplifting island exhibits exceptionally high erosion rates driven by frequent earthquakes, typhoons, and pervasive landsliding.In this study, we examine the distribution and preservation of detrital pyrite in river sediments from southeastern Taiwan. Using petrographic, heavy-mineral, Raman spectroscopy and SEM-EDS data, we assessed pyrite abundance, grain morphology, and oxidation state across catchments spanning a range of erosion regimes. The results reveal that pyrite abundance and persistence scale with erosion rates: catchments eroding faster than 1 mm/yr export up to ~30% of the exhumed pyrite, accounting for more than 1% of the total sediment load, primarily as fresh fragments, whereas lower-relief catchments are dominated by oxidized pyrite grains. Fresh pyrite is particularly abundant in sands sourced from the Central Range, where erosion rates are highest and landsliding triggered by typhoons is widespread. These observations indicate that rapid erosion, rather than mineralogical resistance or external geochemical controls, is the primary factor governing pyrite survival in Taiwan’s river systems. Detrital pyrite can therefore bypass oxidative weathering in fast-eroding orogenic settings, with important implications for sulfur and carbon cycling and for the preservation of pyrite in the sedimentary record.