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Salivary gland tumors (SGTs) are a relevant, highly diverse subgroup of head and neck tumors whose entity determination can be difficult. Confocal Raman imaging in combination with multivariate data analysis may possibly support their correct classification. For the analysis of the translational potential of Raman imaging in SGT determination, a multi-stage evaluation process is necessary. By measuring a sample set of Warthin tumor, pleomorphic adenoma and non-tumor salivary gland tissue, Raman data were obtained and a thorough Raman band analysis was performed. This evaluation revealed highly overlapping Raman patterns with only minor spectral differences. Consequently, a principal component analysis (PCA) was calculated and further combined with a discriminant analysis (DA) to enable the best possible distinction. The PCA-DA model was characterized by accuracy, sensitivity, selectivity and precision values above 90% and validated by predicting model-unknown Raman spectra, of which 93% were classified correctly. Thus, we state our PCA-DA to be suitable for parotid tumor and non-salivary salivary gland tissue discrimination and prediction. For evaluation of the translational potential, further validation steps are necessary.
Cotton contamination by honeydew is considered one of the significant problems for quality in textiles as it causes stickiness during manufacturing. Therefore, millions of dollars in losses are attributed to honeydew contamination each year. This work presents the use of UV hyperspectral imaging (225–300 nm) to characterize honeydew contamination on raw cotton samples. As reference samples, cotton samples were soaked in solutions containing sugar and proteins at different concentrations to mimic honeydew. Multivariate techniques such as a principal component analysis (PCA) and partial least squares regression (PLS-R) were used to predict and classify the amount of honeydew at each pixel of a hyperspectral image of raw cotton samples. The results show that the PCA model was able to differentiate cotton samples based on their sugar concentrations. The first two principal components (PCs) explain nearly 91.0% of the total variance. A PLS-R model was built, showing a performance with a coefficient of determination for the validation (R2cv) = 0.91 and root mean square error of cross-validation (RMSECV) = 0.036 g. This PLS-R model was able to predict the honeydew content in grams on raw cotton samples for each pixel. In conclusion, UV hyperspectral imaging, in combination with multivariate data analysis, shows high potential for quality control in textiles.
UV hyperspectral imaging (225 nm–410 nm) was used to identify and quantify the honey- dew content of real cotton samples. Honeydew contamination causes losses of millions of dollars annually. This study presents the implementation and application of UV hyperspectral imaging as a non-destructive, high-resolution, and fast imaging modality. For this novel approach, a reference sample set, which consists of sugar and protein solutions that were adapted to honeydew, was set-up. In total, 21 samples with different amounts of added sugars/proteins were measured to calculate multivariate models at each pixel of a hyperspectral image to predict and classify the amount of sugar and honeydew. The principal component analysis models (PCA) enabled a general differentiation between different concentrations of sugar and honeydew. A partial least squares regression (PLS-R) model was built based on the cotton samples soaked in different sugar and protein concentrations. The result showed a reliable performance with R2cv = 0.80 and low RMSECV = 0.01 g for the valida- tion. The PLS-R reference model was able to predict the honeydew content laterally resolved in grams on real cotton samples for each pixel with light, strong, and very strong honeydew contaminations. Therefore, inline UV hyperspectral imaging combined with chemometric models can be an effective tool in the future for the quality control of industrial processing of cotton fibers.
Due to the wide variety of benign and malignant salivary gland tumors, classification and malignant behavior determination based on histomorphological criteria can be difficult and sometimes impossible. Spectroscopical procedures can acquire molecular biological information without destroying the tissue within the measurement processes. Since several tissue preparation procedures exist, our study investigated the impact of these preparations on the chemical composition of healthy and tumorous salivary gland tissue by Fourier-transform infrared (FTIR) microspectroscopy. Sequential tissue cross-sections were prepared from native, formalin-fixed and formalin-fixed paraffin-embedded (FFPE) tissue and analyzed. The FFPE cross-sections were dewaxed and remeasured. By using principal component analysis (PCA) combined with a discriminant analysis (DA), robust models for the distinction of sample preparations were built individually for each parotid tissue type. As a result, the PCA-DA model evaluation showed a high similarity between native and formalin-fixed tissues based on their chemical composition. Thus, formalin-fixed tissues are highly representative of the native samples and facilitate a transfer from scientific laboratory analysis into the clinical routine due to their robust nature. Furthermore, the dewaxing of the cross-sections entails the loss of molecular information. Our study successfully demonstrated how FTIR microspectroscopy can be used as a powerful tool within existing clinical workflows.
Surface-enhanced Raman spectroscopy (SERS) provides a strong enhancement to an inherently weak Raman signal, which strongly depends on the material, design, and fabrication of the substrate. Here, we present a facile method of fabricating a non-uniform SERS substrate based on an annealed thin gold (Au) film that offers multiple resonances and gap sizes within the same sample. It is not only chemically stable, but also shows reproducible trends in terms of geometry and plasmonic response. Scanning electron microscopy (SEM) reveals particle-like and island-like morphology with different gap sizes at different lateral positions of the substrate. Extinction spectra show that the plasmonic resonance of the nanoparticles/metal islands can be continuously tuned across the substrate. We observed that for the analytes 1,2-bis(4-pyridyl) ethylene (BPE) and methylene blue (MB), the maximum SERS enhancement is achieved at different lateral positions, and the shape of the extinction spectra allows for the correlation of SERS enhancement with surface morphology. Such non-uniform SERS substrates with multiple nanoparticle sizes, shapes, and interparticle distances can be used for fast screening of analytes due to the lateral variation of the resonances within the same sample.
Monodisperse porous poly(glycidyl methacrylate-co-ethylene glycol dimethacrylate) particles are widely applied in different fields, as their pore properties can be influenced and functionalization of the epoxy group is versatile. However, the adjustment of parameters which control morphology and pore properties such as pore volume, pore size and specific surface area is scarcely available. In this work, the effects of the process factors monomer:porogen ratio, GMA:EDMA ratio and composition of the porogen mixture on the response variables pore volume, pore size and specific surface area are investigated using a face centered central composite design. Non-linear effects of the process factors and second order interaction effects between them were identified. Despite the complex interplay of the process factors, targeted control of the pore properties was possible. For each response a response surface model was derived with high predictive power (all R2 predicted > 0.85). All models were tested by four external validation experiments and their validity and predictive power was demonstrated.
Metalworking fluids (MWFs) are widely used to cool and lubricate metal workpieces during processing to reduce heat and friction. Extending a MWF’s service life is of importance from both economical and ecological points of view. Knowledge about the effects of processing conditions on the aging behavior and reliable analytical procedures are required to properly characterize the aging phenomena. While so far no quantitative estimations of ageing effects on MWFs have been described in the literature other than univariate ones based on single parameter measurements, in the present study we present a simple spectroscopy-based set-up for the simultaneous monitoring of three quality parameters of MWF and a mathematical model relating them to the most influential process factors relevant during use. For this purpose, the effects of MWF concentration, pH and nitrite concentration on the droplet size during aging were investigated by means of a response surface modelling approach. Systematically varied model MWF fluids were characterized using simultaneous measurements of absorption coefficients µa and effective scattering coefficients µ’s. Droplet size was determined via dynamic light scattering (DLS) measurements. Droplet size showed non-linear dependence on MWF concentration and pH, but the nitrite concentration had no significant effect. pH and MWF concentration showed a strong synergistic effect, which indicates that MWF aging is a rather complex process. The observed effects were similar for the DLS and the µ’s values, which shows the comparability of the methodologies. The correlations of the methods were R2c = 0.928 and R2P = 0.927, as calculated by a partial least squares regression (PLS-R) model. Furthermore, using µa, it was possible to generate a predictive PLS-R model for MWF concentration (R2c = 0.890, R2P = 0.924). Simultaneous determination of the pH based on the µ’s is possible with good accuracy (R²c = 0.803, R²P = 0.732). With prior knowledge of the MWF concentration using the µa-PLS-R model, the predictive capability of the µ’s-PLS-R model for pH was refined (10 wt%: R²c = 0.998, R²p = 0.997). This highlights the relevance of the combined measurement of µa and µ’s. Recognizing the synergistic nature of the effects of MWF concentration and pH on the droplet size is an important prerequisite for extending the service life of an MWF in the metalworking industry. The presented method can be applied as an in-process analytical tool that allows one to compensate for ageing effects during use of the MWF by taking appropriate corrective measures, such as pH correction or adjustment of concentration.
Hyperspectral imaging and reflectance spectroscopy in the range from 200–380 nm were used to rapidly detect and characterize copper oxidation states and their layer thicknesses on direct bonded copper in a non-destructive way. Single-point UV reflectance spectroscopy, as a well-established method, was utilized to compare the quality of the hyperspectral imaging results. For the laterally resolved measurements of the copper surfaces an UV hyperspectral imaging setup based on a pushbroom imager was used. Six different types of direct bonded copper were studied. Each type had a different oxide layer thickness and was analyzed by depth profiling using X-ray photoelectron spectroscopy. In total, 28 samples were measured to develop multivariate models to characterize and predict the oxide layer thicknesses. The principal component analysis models (PCA) enabled a general differentiation between the sample types on the first two PCs with 100.0% and 96% explained variance for UV spectroscopy and hyperspectral imaging, respectively. Partial least squares regression (PLS-R) models showed reliable performance with R2c = 0.94 and 0.94 and RMSEC = 1.64 nm and 1.76 nm, respectively. The developed in-line prototype system combined with multivariate data modeling shows high potential for further development of this technique towards real large-scale processes.
A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application.
Monodisperse polystyrene spheres are functional materials with interesting properties, such as high cohesion strength, strong adsorptivity, and surface reactivity. They have shown a high application value in biomedicine, information engineering, chromatographic fillers, supercapacitor electrode materials, and other fields. To fully understand and tailor particle synthesis, the methods for characterization of their complex 3D morphological features need to be further explored. Here we present a chemical imaging study based on three-dimensional confocal Raman microscopy (3D-CRM), scanning electron microscopy (SEM), focused ion beam (FIB), diffuse reflectance infrared Fourier transform (DRIFT), and nuclear magnetic resonance (NMR) spectroscopy for individual porous swollen polystyrene/poly (glycidyl methacrylate-co-ethylene di-methacrylate) particles. Polystyrene particles were synthesized with different co-existing chemical entities, which could be identified and assigned to distinct regions of the same particle. The porosity was studied by a combination of SEM and FIB. Images of milled particles indicated a comparable porosity on the surface and in the bulk. The combination of standard analytical techniques such as DRIFT and NMR spectroscopies yielded new insights into the inner structure and chemical composition of these particles. This knowledge supports the further development of particle synthesis and the design of new strategies to prepare particles with complex hierarchical architectures.