Simple, Fast and Convenient Magnetic Bead-Based Sample Preparation for Detecting Viruses via Raman-Spectroscopy.

We introduce a magnetic bead-based sample preparation scheme for enabling the Raman spectroscopic differentiation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)-positive and -negative samples. The beads were functionalized with the angiotensin-converting enzyme 2 (ACE2) receptor protein, which is used as a recognition element to selectively enrich SARS-CoV-2 on the surface of the magnetic beads. The subsequent Raman measurements directly enable discriminating SARS-CoV-2-positive and -negative samples. The proposed approach is also applicable for other virus species when the specific recognition element is exchanged. A series of Raman spectra were measured on three types of samples, namely SARS-CoV-2, Influenza A H1N1 virus and a negative control. For each sample type, eight independent replicates were considered. All of the spectra are dominated by the magnetic bead substrate and no obvious differences between the sample types are apparent. In order to address the subtle differences in the spectra, we calculated different correlation coefficients, namely the Pearson coefficient and the Normalized cross correlation coefficient. By comparing the correlation with the negative control, differentiating between SARS-CoV-2 and Influenza A virus is possible. This study provides a first step towards the detection and potential classification of different viruses with the use of conventional Raman spectroscopy.

Correcting systematic errors by hybrid 2D correlation loss functions in nonlinear inverse modelling.

Recently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results.

Dispersion related coupling effects in IR spectra on the example of water and Amide I bands.

We discuss coupling effects in infrared spectra which are caused by dispersion and local field effects. The first effect is instigated by changes of the refractive index due to absorption which have an impact on the strength of adjacent absorptions. The second effect is a consequence of the light-induced polarization of one molecule affecting neighboring ones. These coupling effects do not only effect band positions, but also influence relative intensities. They are particularly strong in case of overlapping bands and complicate their deconvolution by band fitting. We investigated the corresponding challenges for the HO-stretching vibrations in water and the Amide I band in proteins. Our findings show that the effects are significant and of high interest for protein and water structure determination. Especially, for the water stretching vibrations we conclude that it is of utmost importance to consider such coupling effects in quantum mechanical calculations of water spectra. Otherwise, progress in understanding band positions and profiles is likely to be hampered. Also, in case of the Amide I band we found a distinct impact of such coupling effects. Accordingly, we strongly recommend consideration of dispersion and local field effects to ensure the possibility of an accurate, quantitative determination of α-helix and ß-sheet structures.

Smart Error Sum Based on Hybrid Two-Trace Two-Dimensional (2T2D) Correlation Analysis.

Based on hybrid 2D correlation analysis, we recently derived and introduced a "smart error sum," a sophisticated loss function that can be used for solving nonlinear inverse problems like the determination of optical constants and oscillator parameters from a series of optical spectra in the infrared spectral region. The advantage of the smart error sum compared to the conventional sum of squared errors lies in its ability to marginalize multiplicative systematic errors such as, for example, reflectance values above unity in transflection spectra. This is enabled by a transformation, which allows fits to not exclusively focus on forcing fit spectra to agree with experimental spectra at every wavenumber point by all means, but also to take correlations such as spectral similarities and their changes with certain perturbations into account. In this work, we extend our approach to accommodate the treatment of individual spectra, instead of only series, based on hybrid two-trace two-dimensional (2T2D) correlation analysis. We evaluate and prove the value of our approach by individually analyzing experimental transflection spectra of polymethyl methacrylate (PMMA) layers on gold substrates. The comparison of the results with those obtained by the original smart error sum based on the whole set of spectra as well as those resulting from conventional fitting of series and individual spectra (using the conventional sum of squared errors) confirms the validity and soundness of the 2T2D smart error sum.

Hybrid 2D Correlation-Based Loss Function for the Correction of Systematic Errors.

We present the derivation of a new kind of loss function from the symmetry rules of synchronous and asynchronous two-dimensional correlation maps. This loss function, which takes into account correlations that are based on causal relations among the members of a series of spectra, can be employed to solve non-linear inverse problems that are plagued by systematic multiplicative errors. This possibility results from the correlation-based loss function being practically insensitive to such systematic errors, which often arise in spectroscopy because sample spectra are usually ratioed against reference spectra. Using dispersion analysis, a sophisticated method of band fitting, of the spectra of poly(methyl methacrylate) films deposited on gold, we demonstrate the applicability and validity of the new loss function. If gold is used as a substrate, experimental spectra are often unphysical, that is, they display reflectance values larger than unity. In such cases, our correlation-based loss function not only helps to achieve accurate fits but also provides corrections to obtain physically meaningful spectra, which leads to results that are superior to conventional correction methods. The validity of the results is checked and proved with help of the results of dispersion analysis of spectra of films of poly(methyl methacrylate) on calcium fluoride (CaF2) and silicon (Si), which do not suffer from the systematic errors.

Isolation of pathogenic bacteria from sputum samples using a 3D-printed cartridge system.

Within this contribution we introduce a 3D-printed cartridge system enabling the convenient and cost-efficient sample preparation from sputum for subsequent PCR based detection schemes. The developed fluidic system operates on pneumatic actuations. The closed system ensures a very low probability for contamination during sample processing, which is crucial when using a highly sensitive detection method such as PCR. The enrichment of the bacterial cells is achieved using different types of amine-functionalized particles. Our particle-based sample preparation approach yields intact and viable bacterial cells. Accordingly, not only PCR-based detection schemes can be employed, but also spectroscopic methods and biochemical tests, which require cultivation steps, are possible. The cartridge design in principle is compatible with magnetic and non-magnetic particle types. We investigated both variants and found that the performance of expanded glass beads is superior over the magnetic particles within the cartridge. Owing to the rather large size of the expanded glass beads, the dimensions of the channels can be enlarged, leading to lower hydrodynamic resistances, which is beneficial when processing viscous samples such as sputum. We verified the performance of our system using both artificial and real sputum samples containing Escherichia coli and Moraxella catarrhalis.

Recent technological and scientific developments concerning the use of infrared spectroscopy for point-of-care applications.

In this contribution we review selected point-of-care applications of infrared spectroscopy and the technological innovations they are based on. After a short introduction summarizing the general idea behind point-of-care applications we introduce the reader to important infrared spectroscopy sensing principles on a very basic level. We discuss the role of optical components like quantum cascade lasers, supercontinuum sources, waveguides and how they are potentially going to revolutionize point-of-care applications. First, we focus on the technological solutions of some principal problems like increasing the pathlength in a transmission cell to enhance the sensitivity for solutes in aqueous solutions and discuss indirect methods which circumvent the problem of low transmittance. In the second part we show how the technological progress of the last decades enabled scientific progress leading to selected concrete and outstanding point-of-care solutions and applications based on infrared spectroscopy. These include the detection and quantification of malaria parasitemia, early recognition of Alzheimer's disease long before the onset of clinical symptoms and a non-invasive method for testing the blood glucose content. The selected examples demonstrate and showcase that infrared spectroscopy is on the way to become an indispensable technique for point-of-care applications.

The Bouguer-Beer-Lambert Law: Shining Light on the Obscure.

The Beer-Lambert law is unquestionably the most important law in optical spectroscopy and indispensable for the qualitative and quantitative interpretation of spectroscopic data. As such, every spectroscopist should know its limits and potential pitfalls, arising from its application, by heart. It is the goal of this work to review these limits and pitfalls, as well as to provide solutions and explanations to guide the reader. This guidance will allow a deeper understanding of spectral features, which cannot be explained by the Beer-Lambert law, because they arise from electromagnetic effects/the wave nature of light. Those features include band shifts and intensity changes based exclusively upon optical conditions, i. e. the method chosen to record the spectra, the substrate and the form of the sample. As such, the review will be an essential tool towards a full understanding of optical spectra and their quantitative interpretation based not only on oscillator positions, but also on their strengths and damping constants.

Deep learning for 'artefact' removal in infrared spectroscopy.

It has been well recognized that infrared spectra of microscopically heterogeneous media do not merely reflect the absorption of the sample but are influenced also by geometric factors and the wave nature of light causing scattering, reflection, interference, etc. These phenomena often occur simultaneously in complex samples like tissues and manifest themselves as intense baseline profiles, fringes, band distortion and band intensity changes in a measured IR spectrum. The information on the molecular level contained in IR spectra is thus entangled with the geometric structure of a sample and the optical model behind it, which largely hinders the data interpretation and in many cases renders the Beer-Lambert law invalid. It is required to recover the pure absorption (i.e., absorbance) of the sample from the measurement (i.e., apparent absorbance), that is, to remove the 'artefacts' caused merely by optical influences. To do so, we propose an artefact removal approach based on a deep convolutional neural network (CNN), specifically a 1-dimensional U-shape convolutional neural network (1D U-Net), and based our study on poly(methyl methacrylate) (PMMA) as materials. To start, a simulated dataset composed of apparent absorbance and absorbance pairs was generated according to the Mie-theory for PMMA spheres. After a data augmentation procedure, this dataset was utilized to train the 1D U-Net aiming to transform the input apparent absorbance into the corrected absorbance. The performance of the artefact removal was evaluated by the hit-quality-index (HQI) between the corrected and the true absorbance. Based on the prediction and the HQI of two experimental and one simulated independent testing datasets, we could demonstrate that the network was able to retrieve the absorbance very well, even in cases where the absorbance is completely overwhelmed by extremely large 'artefacts'. As the testing datasets bear different patterns of absorbance and 'artefacts' to the training data, the promising correction also indicated a good generalization performance of the 1D U-Net. Finally, the reliability and computational mechanism of the trained network were illustrated via two interpretation approaches including a direct visualization of layer-wise outputs as well as a saliency-based method.

CaF2: An Ideal Substrate Material for Infrared Spectroscopy?

CaF2 seems to be the ideal substrate material for the infrared spectroscopy of organic and biological layers, since its refractive index is very similar to that of these materials. As a consequence of this similarity, the baseline, i.e., the signal strength in nonabsorbing regions, is nearly flat and does not show notable interference fringes. Nevertheless, as absorption is always accompanied by changes of the refractive index, the refractive indices of substrate and layer can substantially deviate around absorption bands. As a consequence, changes in band intensity, shape, and position result, which aggravate a correct interpretation of the spectra. For layers with thicknesses between 1 and 2 µm, we show experimentally, that deviations from the Beer-Lambert law of up to ±10% occur. Calculations reveal that for thinner layers these deviations are even higher. These results suggest the application of a wave-optics based formalism to correct the deviations. We introduce such a formalism and prove that it is able to remove the errors. In addition, it also corrects band shape and position changes.

Removing interference-based effects from infrared spectra - interference fringes re-revisited.

Substantial refractive index mismatches between substrate and layers lead to undulating baselines, which are known as interference fringes. These fringes can be attributed to multiple reflections inside the layers. For thin and plane parallel layers, these multiple reflections result in wave interference and electric field intensities which strongly depend on the location within the layer and wavenumber. In particular, the average electric field intensity is increased in spectral regions where the reflectance is reduced. Therefore, the most important precondition for the Beer-Lambert law to hold, absorption as the single reason for electric field intensity changes, is no longer valid and, since absorption is proportional to the electric field intensity, considerable deviations from the Beer-Lambert law result. Fringe removal is consequently synonymous with correcting deviations from the Beer-Lambert law in the spectra. Within this contribution, we introduce an appropriate formalism based on wave optics, which allows a particularly fast and simple correction of any interference based effects. We applied our approach for correcting transmittance spectra of Poly(methyl methacrylate) layers on silicon substrates. The interference effects were successfully removed and correct baselines, in good agreement with the calculated spectra, were obtained. Due to its sound theoretical foundation, our formalism can be used as benchmark to test the performance of other methods for interference fringe removal.

Rapid detection of the aspergillosis biomarker triacetylfusarinine C using interference-enhanced Raman spectroscopy.

Triacetylfusarinine C (TAFC) is a siderophore produced by certain fungal species and might serve as a highly useful biomarker for the fast diagnosis of invasive aspergillosis. Due to its renal elimination, the biomarker is found in urine samples of patients suffering from Aspergillus infections. Accordingly, non-invasive diagnosis from this easily obtainable body fluid is possible. Within our contribution, we demonstrate how Raman microspectroscopy enables a sensitive and specific detection of TAFC. We characterized the TAFC iron complex and its iron-free form using conventional and interference-enhanced Raman spectroscopy (IERS) and compared the spectra with the related compound ferrioxamine B, which is produced by bacterial species. Even though IERS only offers a moderate enhancement of the Raman signal, the employment of respective substrates allowed lowering the detection limit to reach the clinically relevant range. The achieved limit of detection using IERS was 0.5 ng of TAFC, which is already well within the clinically relevant range. By using an extraction protocol, we were able to detect 1.4 µg/mL TAFC via IERS from urine within less than 3 h including sample preparation and data analysis. We could further show that TAFC and ferrioxamine B can be clearly distinguished by means of their Raman spectra even in very low concentrations.

Rapid Isolation and Identification of Pneumonia-Associated Pathogens from Sputum Samples Combining an Innovative Sample Preparation Strategy and Array-Based Detection.

With this study, an innovative and convenient enrichment and detection strategy for eight clinically relevant pneumonia pathogens, namely, Acinetobacter baumannii, Escherichia coli, Haemophilus influenzae, Klebsiella pneumoniae, Moraxella catarrhalis, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae is introduced. Bacteria were isolated from sputum samples with amine-modified particles exploiting pH-dependent electrostatic interactions between bacteria and the functionalized particle surface. Following this, an asymmetric polymerase chain reaction as well as subsequent stringent array-based hybridization with specific complementary capture probes were performed. Finally, results were visualized by an enzyme-induced silver nanoparticle deposition, providing stable endpoint signals and consequently an easy detection possibility. The assay was optimized using spiked samples of artificial sputum with different strains of the abovementioned bacterial species. Furthermore, actual patient sputum samples with S. pneumoniae were successfully analyzed. The presented approach offers great potential for the urgent need of a fast, specific, and reliable isolation and identification platform for important pneumonia pathogens, covering the complete process chain from sample preparation up to array-based detection within only 4 h.

Interference-Enhanced Raman Spectroscopy as a Promising Tool for the Detection of Biomolecules on Raman-Compatible Surfaces.

Raman spectroscopy in combination with appropriate sample preparation strategies, for example, enrichment of bacteria on metal surfaces, has been proven to be a promising approach for rapidly diagnosing infectious diseases. Unfortunately, the fabrication of the required chip substrates is usually very challenging due to the lack of feasible instruments that can be used for quality control in the surface modification process. The intrinsically weak Raman signal of the biomolecules, employed for the enrichment of the micro-organisms on the chip surface, does not allow for monitoring of the successful immobilization by means of a Raman spectroscopic approach. Within this contribution, we demonstrate how a simple modification of a plain aluminum surface enables enhancement (or a decrease, if desired) of the Raman signal of molecules deposited on that surface. The manipulation of the Raman signal strength is achieved via exploiting interference effects that occur, if the highly reflective aluminum surface is modified with thin layers of transparent dielectrics like aluminum oxide. The thicknesses of these layers were determined by theoretical considerations and calculations. For the first time, it is shown that the interference effects can be used for the detection of biomolecules as well by investigating the siderophore ferrioxamine B. The observed degree of enhancement was approximately 1 order of magnitude. Moreover, the employed aluminum/aluminum oxide layers have been thoroughly characterized using atomic force and scanning electron microscopy as well as X-ray reflectometry and UV-Vis measurements.

Removing interference-based effects from the infrared transflectance spectra of thin films on metallic substrates: a fast and wave optics conform solution.

A hybrid formalism combining elements from Kramers-Kronig based analyses and dispersion analysis was developed, which allows removing interference-based effects in the infrared spectra of layers on highly reflecting substrates. In order to enable a highly convenient application, the correction procedure is fully automatized and usually requires less than a minute with non-optimized software on a typical office PC. The formalism was tested with both synthetic and experimental spectra of poly(methyl methacrylate) on gold. The results confirmed the usefulness of the formalism: apparent peak ratios as well as the interference fringes in the original spectra were successfully corrected. Accordingly, the introduced formalism makes it possible to use inexpensive and robust highly reflecting substrates for routine infrared spectroscopic investigations of layers or films the thickness of which is limited by the imperative that reflectance absorbance must be smaller than about 1. For thicker films the formalism is still useful, but requires estimates for the optical constants.

Rapid Identification of Pseudomonas spp. via Raman Spectroscopy Using Pyoverdine as Capture Probe.

Pyoverdine is a substance which is excreted by fluorescent pseudomonads in order to scavenge iron from their environment. Due to specific receptors of the bacterial cell wall, the iron loaded pyoverdine molecules are recognized and transported into the cell. This process can be exploited for developing efficient isolation and enrichment strategies for members of the Pseudomonas genus, which are capable of colonizing various environments and also include human pathogens like P. aeruginosa and the less virulent P. fluorescens. A significant advantage over antibody based systems is the fact that siderophores like pyoverdine can be considered as "immutable ligands," since the probability for mutations within the siderophore uptake systems of bacteria is very low. While each species of Pseudomonas usually produces structurally unique pyoverdines, which can be utilized only by the producer strain, cross reactivity does occur. In order to achieve a reliable identification of the captured pathogens, further investigations of the isolated cells are necessary. In this proof of concept study, we combine the advantages of an isolation strategy relying on "immutable ligands" with the high specificity and speed of Raman microspectroscopy. In order to isolate the bacterial cells, pyoverdine was immobilized covalently on planar aluminum chip substrates. After capturing, single cell Raman spectra of the isolated species were acquired. Due to the specific spectroscopic fingerprint of each species, the bacteria can be identified. This approach allows a very rapid detection of potential pathogens, since time-consuming culturing steps are unnecessary. We could prove that pyoverdine based isolation of bacteria is fully Raman compatible and further investigated the capability of this approach by isolating and identifying P. aeruginosa and P. fluorescens from tap water samples, which are both opportunistic pathogens and can pose a threat for immunocompromised patients.

Isolation and identification of bacteria by means of Raman spectroscopy.

Bacterial detection is a highly topical research area, because various fields of application will benefit from the progress being made. Consequently, new and innovative strategies which enable the investigation of complex samples, like body fluids or food stuff, and improvements regarding the limit of detection are of general interest. Within this review the prospects of Raman spectroscopy as a reliable tool for identifying bacteria in complex samples are discussed. The main emphasis of this work is on important aspects of applying Raman spectroscopy for the detection of bacteria like sample preparation and the identification process. Several approaches for a Raman compatible isolation of bacterial cells have been developed and applied to different matrices. Here, an overview of the limitations and possibilities of these methods is provided. Furthermore, the utilization of Raman spectroscopy for diagnostic purposes, food safety and environmental issues is discussed under a critical view.

Convenient detection of E. coli in Ringer's solution.

An easy and inexpensive detection method for DNA hybridization assays combining magnetic beads and enzymatically generated silver nanoparticles is introduced. The main advantage of this approach is the possibility to distinguish between positive and negative test results with the naked eye. In the case of complementary DNA sequences the sample will turn black within a few minutes, allowing readout without any hardware. In order to illustrate the applicability of the assay genomic DNA isolated from E. coli contaminated Ringer's solution was used for testing the sensitivity as well as specificity.