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Some widely used optical measurement systems require a scan in wavelength or in one spatial dimension to measure the topography in all three dimensions. Novel hyperspectral sensors based on an extended Bayer pattern have a high potential to solve this issue as they can measure three dimensions in a single shot. This paper presents a detailed examination of a hyperspectral sensor including a description of the measurement setup. The evaluated sensor (Ximea MQ022HG-IM-SM5X5-NIR) offers 25 channels based on Fabry–Pérot filters. The setup illuminates the sensor with discrete wavelengths under a specified angle of incidence. This allows characterization of the spatial and angular response of every channel of each macropixel of the tested sensor on the illumination. The results of the characterization form the basis for a spectral reconstruction of the signal, which is essential to obtain an accurate spectral image. It turned out that irregularities of the signal response for the individual filters are present across the whole sensor.
Characterization of brain tumours requires neuropathological expertise and is generally performed by histological evaluation and molecular analysis. One emerging technique to assist pathologists in future tumour diagnostics is multimodal optical spectroscopy. In the current clinical routine, tissue preprocessing with formalin is widely established and suitable for spectroscopic investigations since degradation processes impede the measurement of native tissue. However, formalin fixation results in alterations of the tissue chemistry and morphology for example by protein cross-linking. As optical spectroscopy is sensitive to these variations, we evaluate the effects of formalin fixation on multimodal brain tumour data in this proof-of-concept study. Nonfixed and formalin-fixed cross sections of different common human brain tumours were subjected to analysis of chemical variations using ultraviolet and Fourier-transform infrared microspectroscopy. Morphological changes were assessed by elastic light scattering microspectroscopy in the visible wavelength range. Data were analysed with multivariate data analysis and compared with histopathology. Tissue type classifications deduced by optical spectroscopy are highly comparable and independent from the preparation and the fixation protocol. However, formalin fixation leads to slightly better classification models due to improved stability of the tissue. As a consequence, spectroscopic methods represent an appropriate additional contrast for chemical and morphological information in neuropathological diagnosis and should be investigated to a greater extent. Furthermore, they can be included in the clinical workflow even after formalin fixation.
The critical process parameters cell density and viability during mammalian cell cultivation are assessed by UV/VIS spectroscopy in combination with multivariate data analytical methods. This direct optical detection technique uses a commercial optical probe to acquire spectra in a label-free way without signal enhancement. For the cultivation, an inverse cultivation protocol is applied, which simulates the exponential growth phase by exponentially replacing cells and metabolites of a growing Chinese hamster ovary cell batch with fresh medium. For the simulation of the death phase, a batch of growing cells is progressively replaced by a batch with completely starved cells. Thus, the most important parts of an industrial batch cultivation are easily imitated. The cell viability was determined by the well-established method partial least squares regression (PLS). To further improve process knowledge, the viability has been determined from the spectra based on a multivariate curve resolution (MCR) model. With this approach, the progress of the cultivations can be continuously monitored solely based on an UV/VIS sensor. Thus, the monitoring of critical process parameters is possible inline within a mammalian cell cultivation process, especially the viable cell density. In addition, the beginning of cell death can be detected by this method which allows us to determine the cell viability with acceptable error. The combination of inline UV/VIS spectroscopy with multivariate curve resolution generates additional process knowledge complementary to PLS and is considered a suitable process analytical tool for monitoring industrial cultivation processes.
Hyperspectral imaging opens a wide field of applications. It is a well established technique in agriculture, medicine, mineralogy and many other fields. Most commercial hyperspectral sensors are able to record spectral information along one spatial dimension in a single acquisition. For the second spatial dimension a scan is required. Beside those systems there is a novel technique allowing to sense a two dimensional scene and its spectral information within one shot. This increases the speed of hyperspectral imaging, which is interesting for metrology tasks under rough environmental conditions. In this article we present a detailed characterization of such a snapshot sensor for later use in a snapshot full field chromatic confocal system. The sensor (Ximea MQ022HG-IM-SM5X5-NIR) is based on the so called snapshot mosaic technique, which offers 25 bands mapped to one so called macro pixel. The different bands are realized by a spatially repeating pattern of Fabry-Pèrot flters. Those filters are monolithically fabricated on the camera chip.
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.
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.
The early detection of head and neck cancer is a prolonged challenging task. It requires a precise and accurate identification of tissue alterations as well as a distinct discrimination of cancerous from healthy tissue areas. A novel approach for this purpose uses microspectroscopic techniques with special focus on hyperspectral imaging (HSI) methods. Our proof-of-principle study presents the implementation and application of darkfield elastic light scattering spectroscopy (DF ELSS) as a non-destructive, high-resolution, and fast imaging modality to distinguish lingual healthy from altered tissue regions in a mouse model. The main aspect of our study deals with the comparison of two varying HSI detection principles, which are a point-by-point and line scanning imaging, and whether one might be more appropriate in differentiating several tissue types. Statistical models are formed by deploying a principal component analysis (PCA) with the Bayesian discriminant analysis (DA) on the elastic light scattering (ELS) spectra. Overall accuracy, sensitivity, and precision values of 98% are achieved for both models whereas the overall specificity results in 99%. An additional classification of model-unknown ELS spectra is performed. The predictions are verified with histopathological evaluations of identical HE-stained tissue areas to prove the model’s capability of tissue distinction. In the context of our proof-of-principle study, we assess the Pushbroom PCA-DA model to be more suitable for tissue type differentiations and thus tissue classification. In addition to the HE-examination in head and neck cancer diagnosis, the usage of HSI-based statistical models might be conceivable in a daily clinical routine.
Commercially available homogenized cow- and plant-based milks were investigated by optical spectroscopy in the range of 400–1360 nm. Absorbance spectra, the effective scattering coefficient μs′, and the spectral absorption coefficient μa were recorded for 23 milk varieties and analyzed by multivariate data analysis. Cow- and plant-based milks were compared and discriminated using principal component analysis combined with a quadratic discriminant analysis. Furthermore, it was possible to discriminate the origin of plant-based milk by μa and the fat content in cow-based milk by μs′. Partial least squares regression models were developed to determine the fat content in cow-based milk. The model for μs′ proved to be the most efficient for this task with R2 = 0.98 and RMSEP = 0.19 g/100 mL for the external validation. Thus, optical spectroscopy together with multivariate data analysis is suitable for routine laboratory analysis or quality monitoring in the dairy production.
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.