Convolutional neural networks for segmentation of FIB-SEM nanotomography data from porous polymer films for controlled drug release

Phase-separated polymer films are commonly used as coatings around pharmaceutical oral dosage forms (tablets or pellets) to facilitate controlled drug release. A typical choice is to use ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. When an EC/HPC film is in contact with water, the leaching out of the water-soluble HPC phase produces an EC film with a porous network through which the drug is transported. The drug release can be tailored by controlling the structure of this porous network. Imaging and characterization of such EC porous films facilitates understanding of how to control and tailor film formation and ultimately drug release. Combined focused ion beam and scanning electron microscope (FIB-SEM) tomography is a well-established technique for high-resolution imaging, and suitable for this application. However, for segmenting image data, in this case to correctly identify the porous network, FIB-SEM is a challenging technique to work with. In this work, we implement convolutional neural networks for segmentation of FIB-SEM image data. The data are acquired from three EC porous films where the HPC phases have been leached out. The three data sets have varying porosities in a range of interest for controlled drug release applications. We demonstrate very good agreement with manual segmentations. In particular, we demonstrate an improvement in comparison to previous work on the same data sets that utilized a random forest classifier trained on Gaussian scale-space features. Finally, we facilitate further development of FIB-SEM segmentation methods by making the data and software used open\ua0access

2D and 3D Simulation of the IIW Round Robin Benchmark

Funktionsstabilite

New features of arabinoxylan ethers revealed by using multivariate analysis

\ua9 2018 Hemicelluloses are a relatively unused renewable resource. One reason is their broad structure variety that makes it hard to understand structure-property relations. In this study arbinoxylan, extracted from barley husk, was chemically modified into hydroxypropyl methyl-, hydroxypropyl- and methyl arabinoxylan. The relationship between structure and phase behavior was investigated by using multivariate analysis. The arabinoxylan ethers were characterized using mid-infrared FTIR spectroscopy and from principal components analysis, PCA, structural or physical variations between samples were visualized. With orthogonal projections to latent structures, OPLS, vibrations specific for arabinoxylan hydroxypropyl and methyl substitutions was assigned. Among the observed differences between chemical derivatives was an intensity change in the water vibration. The differences in hydration were related to clouding phase behavior of the arabinoxylan ethers. This study shows that multivariate analysis methods are useful for finding unexpected and/or hidden features in the polysaccharide structure

An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra

Orthogonal signal correction (OSC) is a technique for pre-processing of, for example, NIR-spectra before they are subjected to a multivariate calibration. With OSC the X-matrix is corrected by a subtraction of variation that is orthogonal to the calibration Y-matrix. This correction can then be applied to new spectra that are going to be used in predictions. The aim of this study is to investigate if the OSC transform makes the spectra less dependent of instrument variation. This may result in easier calibration model transfer between different instruments without creating or re-analysing the whole calibration sample set. OSC was applied to NIR-spectra that were used in a calibration for the water content in a pharmaceutical product. Partial Least Squares calibrations were then compared to other calibration models with uncorrected spectra, models with spectra subjected to multiplicative signal correction, and a number of other transfer methods. The performance of OSC was on the same level as for piece-wise direct standardisation and spectral offset correction for each individual instrument and PLS-models with both instruments included

Chemical images of marine bio-active compounds by surface enhanced Raman spectroscopy and transposed orthogonal partial least squares (T-OPLS)

AbstractSurface enhanced Raman spectroscopy combined with transposed Orthogonal Partial Least Squares (T-OPLS) was shown to produce chemical images of the natural antibacterial surface-active compound 1,1,3,3-tetrabromo-2-heptanone (TBH) on Bonnemaisonia hamifera. The use of gold colloids functionalised with the internal standard 4-mercapto-benzonitrile (MBN) made it possible to create images of the relative concentration of TBH over the surfaces. A gradient of TBH could be mapped over and in the close vicinity of the B. hamifera algal vesicles at the attomol/pixel level.T-OPLS produced a measure of the spectral correlation for each pixel of the hyperspectral images whilst not including spectral variation that was linearly independent of the target spectrum. In this paper we show the possibility to retrieve specific spectral information with a low magnitude in a complex matrix

Surface-enhanced Raman scattering imaging of single living lymphocytes with multivariate evaluation

This paper is aimed to show the possibility to determine individual organic compounds introduced into single living cells with surface-enhanced Raman spectroscopy (SERS). Surface enhancement was achieved with gold colloids that were allowed to diffuse into lymphocytes. An introduced analyte, rhodamine 6G, could be imaged together with for example nucleotides and amino acids of the cell. Multivariate evaluation of surface-enhanced Raman images proved to be a powerful tool for the separation of spectral information of various intracellular components. The principal component analysis (PCA) enabled identification of spectra containing different chemical information and separation of the spectral contribution of rhodamine 6G from the complex cellular matrix

Self-assembled monolayer coating for normalization of surface enhanced Raman spectra

We demonstrate that the use of a self-assembled monolayer, consisting of a thiol derivative of Dabcyl, can be used to normalize surface enhanced Raman signals (SERS) with respect to varying enhancement. Chaotic assemblies of gold nanoparticles exhibit large spatial variation in enhancement. Our work shows that in such a system the signals from the reporting molecules in the SAM co-vary with the signal from the analyte solution. With this knowledge, a normalization procedure was used to increase the precision of the analyte signal by 1 order of magnitude, to 8-13%, fully acceptable for quantitative work