Anna de Juan

Hyperspectral imaging (HSI) is a very complete analytical measurement that encloses rich spatial and chemical information. This double side enables HSI to outperform classical spectroscopic measurements and vision systems based only on color information. However, HSI requires powerful data analysis tools for interpretation and to facilitate its implementation in process analytical technology (PAT) contexts. This work is a brief perspective that shows a diversity of PAT challenges that can be solved with the combined use of HSI and dedicated chemometric procedures.

Nanofiltration (NF) membranes are essential in wastewater treatment, battery industries, and brine management for selectively removing multivalent ions. However, fouling reduces their lifespan and necessitates harsh cleaning. The layer-by-layer (LBL) technique addresses this by modifying surface properties, enhancing rejection of divalent cations, such as magnesium and calcium, and minimizing fouling. This study evaluated a semiaromatic-based polyamide NF membrane (Fortilife-XN) modified using a LBL technique. Surface properties such as contact angle (CA), roughness, morphology, uniformity, and thickness were analyzed before and after modification. FTIR characterization revealed the membrane’s structure, comprising a polyethylene terephthalate (PET) support, a polysulfone (PS) substrate, and a polyamide (PA) active surface. Poly­(sodium 4-styrenesulfonate) (PSS) was used as the polyanion, while poly­(diallyldimethylammonium chloride) (PDADMAC) and poly­(allylamine hydrochloride) (PAH) served as strong and weak polycations, respectively. Modifications in varying bilayers (1.5–6.5 BLs) with a positive terminal half-layer introduced peaks at 1034 and 923 cm^–1, corresponding to sulfonate and C–N bonds. CA and roughness analysis showed that (PDADMAC/PSS)­4.5 (CA 20°, roughness 77 nm) and (PAH/PSS)­1.5 (CA 61°, roughness 79 nm) achieved superior wettability and roughness, confirmed by initial NF testing with a permeability of around 10 L/(m² h bar). Ellipsometry, using Cauchy and Sellmeier models, measured multilayer thickness, estimating bilayers at ∼2 nm. Raman imaging visualized cross-sectional modifications, distinguishing raw and modified layers. Surface imaging revealed more uniform PAH deposition, while PDADMAC showed higher swelling with increased layers due to stronger affinity. These combined analytical techniques provided insights into the impact of LBL modification on membrane morphology and properties, aiding performance optimization. All membranes were preliminarily tested with a mixed salt solution (NaCl, CaSO₄, and MgSO₄). The average selectivity of Na/Ca for the bare membrane was 2.6 ± 0.4, increasing by 108% for (PDADMAC/PSS)­5.5 and 134% for PAH 6.5. For Na/Mg, the selectivity was 4.5 ± 0.3, rising by 49% for (PDADMAC/PSS)­5.5 and 131% for PAH 6.5.

Multiscale and multimodal image fusion is a challenge derived from the diversity of chemical and spatial information provided by the current hyperspectral image platforms. Efficient image fusion approaches are essential to exploit the complementary chemical information across different zoom scales. Most current image fusion algorithms tend to work by equalizing the spatial characteristics of the platforms to be combined, i.e., downsampling pixel size and cropping noncommon scanned sample areas if required. In this work, a new image unmixing algorithm based on a flexible mathematical framework is proposed to enable working with all available image information while preserving the original spatial properties of every imaging measurement. The algorithm is tested on a challenging image fusion scenario of fluorescence and Raman images collected on labeled HeLa cells. The system is relevant from an analytical point of view, since smart fluorescence labeling allows profiting from the excellent morphological information without causing interferences in the rich chemical information furnished by Raman. From a data handling perspective, it offers a challenging multiscale problem, where the fast fluorescence imaging acquisition allows recording full cell images, and the slower Raman image acquisition is focused on scanning only relevant small regions of the cells analyzed. By applying the image fusion algorithm proposed, an improved morphological and chemical characterization of cell constituents in the full cell area is obtained despite the different spatial scales used in the original imaging measurements.

Multiscale and multimodal image fusion is a challenge derived from the diversity of chemical and spatial information provided by the current hyperspectral image platforms. Efficient image fusion approaches are essential to exploit the complementary chemical information across different zoom scales. Most current image fusion algorithms tend to work by equalizing the spatial characteristics of the platforms to be combined, i.e., downsampling pixel size and cropping non-common scanned sample areas if required. In this work, a new image unmixing algorithm based on a flexible mathematical framework is proposed to enable working with all available image information while preserving the original spatial properties of every imaging measurement. The algorithm is tested on a challenging image fusion scenario of fluorescence and Raman images collected on labelled HeLa cells. The system is relevant from an analytical point of view, since smart fluorescence labelling allows profiting from the excellent morphological information without causing interferences in the rich chemical information furnished by Raman. From a data handling perspective, it offers a challenging multiscale problem, where the fast fluorescence imaging acquisition allows recording full cell images, and the slower Raman image acquisition is focused on scanning only relevant small regions of the cells analyzed. By applying the image fusion algorithm proposed, an improved morphological and chemical characterization of cell constituents in the full cell area is obtained despite the different spatial scales used in the original imaging measurements.

The goal of native mass spectrometry is to obtain information on noncovalent interactions in solution through mass spectrometry measurements in the gas phase. Characterizing intramolecular folding requires using structural probing techniques such as ion mobility spectrometry. However, inferring solution structures of nucleic acids is difficult because the low-charge state ions produced from aqueous solutions at physiological ionic strength get compacted during electrospray. Here we explored whether native supercharging could produce higher charge states that would better reflect solution folding, and whether the voltage required for collision-induced unfolding (CIU) could reflect preserved intramolecular hydrogen bonds. We studied pH-responsive i-motif structures with different loops, and unstructured controls. We also implemented a multivariate curve resolution procedure to extract physically meaningful pure components from the CIU data and reconstruct unfolding curves. We found that the relative unfolding voltages reflect to some extent, but not always unambiguously, the number of intramolecular hydrogen bonds that were present in solution. Reaching phosphate charging densities over 0.25 makes it easier to discriminate between structures, and the use of native supercharging agents is thus essential. We also uncovered several caveats in data interpretation: (1) when different structures (for example the i-motif with and without hairpin) unfold via different pathways, the unfolding voltages do not necessarily reflect the number of hydrogen bonds, (2) unstructured controls also undergo unfolding, and the base composition influences the unfolding voltage, (3) changing the solution pH also unexpectedly changed the unfolding voltage, and (4) the ion mobility patterns become more complicated when two structures are present simultaneously, such as an i-motif and a harpin, because of opposite effects on the collision cross section upon activation.

The goal of native mass spectrometry is to obtain information on non-covalent interactions in solution through mass spectrometry measurements in the gas phase. Characterizing intramolecular folding re-quires using structural probing techniques such as ion mobility spectrometry. However, inferring solu-tion structures of nucleic acids is difficult because the low-charge state ions produced from aqueous solutions at physiological ionic strength get compacted during electrospray. Here we explored whether native supercharging could produce higher charge states that would better reflect solution folding, and whether the voltage required for collision-induced unfolding (CIU) could reflect preserved intramolecu-lar hydrogen bonds. We studied pH-responsive i-motif structures with different loops, and unstructured controls. We also implemented a multivariate curve resolution procedure to extract physically meaning-ful pure components from the CIU data and reconstruct unfolding curves. We found that the relative unfolding voltages reflect to some extent, but not always unambiguously, the number of intramolecular hydrogen bonds that were present in solution. Reaching phosphate charging densities over 0.25 makes it easier to discriminate between structures, and the use of native supercharging agents is thus essential. We also uncovered several caveats in data interpretation: (1) when different structures (for example the i-motif with and without hairpin) unfold via different pathways, the unfolding voltages do not necessari-ly reflect the number of hydrogen bonds, (2) unstructured controls also undergo unfolding, and the base composition influences the unfolding voltage, (3) changing the solution pH also unexpectedly changed the unfolding voltage, and (4) the ion mobility patterns become more complicated when two structures are present simultaneously, such as an i-motif and a harpin, because of opposite effects on the collision cross section upon activation.

This research focuses on analyzing wool samples dyed with synthetic dyes from the early 20th century. A methodology to identify and distinguish wool fibers dyed with azo, triphenylmethane, and xanthene dyes, which are no longer in use, using the ATR-FTIR spectra, is presented. Firstly, the dataset was subjected to PCA, which revealed the similarities and differences among the samples, illustrating a distribution pattern based on dye classes. MCR-ALS was employed to extract the spectral profiles of the dyed fibers, thereby enhancing the efficacy of the analytical techniques and extracting the comprehensive information from a single instrument. The combination of ATR-FTIR spectroscopy with chemometric methods, such as PCA and MCR-ALS, has proven to be an effective strategy for identifying and differentiating wool fibers dyed with early azo, triphenylmethane, and xanthene dyes. This approach has demonstrated particular effectiveness in enabling rapid analysis without requiring sampling or pretreatment. Moreover, the analysis is supported by thorough bibliographic research on these no longer used colorants. In order to maximize the potential of non-destructive spectroscopic techniques, such as ATR-FTIR, the approach used has proven to be crucial. This study underscores how chemometric techniques expand the capabilities of spectroscopy, extracting extensive information from a single instrument and aligning with the goals of cultural heritage analysis.