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Baseline Correction with Asymmetric Least Squares Smoothing

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Abstract

Most baseline problems in instrumental methods are characterized by a smooth baseline and a superimposed signal that carries the analytical information: a se-ries of peaks that are either all positive or all negative. We combine a smoother with asymmetric weighting of deviations from the (smooth) trend get an effective baseline estimator. It is easy to use, fast and keeps the analytical peak signal in-tact. No prior information about peak shapes or baseline (polynomial) is needed by the method. The performance is illustrated by simulation and applications to real data.

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... 176 Here, the spectra were baseline corrected using asymmetric least square (AsLS) smoothing, as proposed by Eilers. 177,178 The AsLS algorithm is an extension of the Whittaker smoothing suggested in 1922. 179 It has been shown, that the algorithm works well on different types of data 178,[180][181][182][183][184][185] and, therefore, AsLS background correction is applied to all kinds of spectral data shown in this thesis. ...
... 177,178 The AsLS algorithm is an extension of the Whittaker smoothing suggested in 1922. 179 It has been shown, that the algorithm works well on different types of data 178,[180][181][182][183][184][185] and, therefore, AsLS background correction is applied to all kinds of spectral data shown in this thesis. ...
... 55 The spectra were interpolated using the in-built interp1-function in Matlab with a distance of m/z 2, in order to reduce the data set size. Afterwards, the spectra were baseline-corrected using asymmetric least square correction as proposed by Eilers 177,178 and vector normalized before further data analysis. ...
Thesis
Die Vorverarbeitung und Analyse von spektrometrischen und spektroskopischen Daten von Pflanzengewebe sind in den unterschiedlichsten Forschungsbereichen wie der Pflanzenbiologie, Agrarwissenschaften und Klimaforschung von großer Bedeutung. Der Schwerpunkt dieser Arbeit liegt auf der optimierten Nutzung von Daten von Pflanzengeweben, insbesondere der Daten gewonnen durch Matrix–Assistierte Laser–Desorption–Ionisierung Massenspektrometrie, Raman-Spektroskopie und Fourier-Transform-Infrarotspektroskopie. Die Klassifizierungsfähigkeit mit diesen Methoden wird insbesondere nach Kombination der Daten untereinander und mit zusätzlichen chemischen und biologischen Informationen verglichen. Die diskutierten Beispiele befassen sich mit der Untersuchung und Einordnung innerhalb einer bestimmten Pflanzenart, beispielsweise der Unterscheidung von Proben aus unterschiedlichen Populationen, Wachstumsbedingungen oder Gewebeunterstrukturen. Die Daten wurden mit sowohl mit explorativen Werkzeugen wie der Hauptkomponentenanalyse und der hierarchischen Clusteranalyse, als auch mit Methoden des maschinellen Lernens wie die Diskriminanzanalyse oder künstliche neuronale Netzwerke umfassten. Konkret zeigen die Ergebnisse, dass die Kombination der Methoden mit zusätzlichen pflanzenbezogenen Informationen in einer Konsensus-Hauptkomponentenanalyse zu einer umfassenden Charakterisierung der Proben führt. Es werden verschiedene Strategien zur Datenvorbehandlung diskutiert, um nicht relevante spektrale Information zu reduzieren, z.B. aus Karten von Pflanzengeweben oder eingebetteten Pollenkörnern. Die Ergebnisse dieser Arbeit weisen auf die Relevanz der gezielten Nutzung spektrometrischer und spektroskopischer Daten hin und lassen sich nicht nur auf pflanzenbezogene Themen, sondern auch auf andere analytische Klassifizierungsprobleme übertragen.
... This method iteratively compares a spectrum with a version of itself that has been blurred via a sliding window average, using a least squares fit. Each wavenumber is then progressively down weighted using separate weights for intensities above and below the fitted curve, with a bias against peaks remaining above the blurred baseline 93 . This approach is typically computationally faster and sim pler than polynomial fitting, while producing similar results, and thus may be better suited to studies with a large number of spectra 93 . ...
... Each wavenumber is then progressively down weighted using separate weights for intensities above and below the fitted curve, with a bias against peaks remaining above the blurred baseline 93 . This approach is typically computationally faster and sim pler than polynomial fitting, while producing similar results, and thus may be better suited to studies with a large number of spectra 93 . ...
Article
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Raman microspectroscopy offers microbiologists a rapid and non-destructive technique to assess the chemical composition of individual live microorganisms in near real time. In this Primer, we outline the methodology and potential for its application to microbiology. We describe the technical aspects of Raman analyses and practical approaches to apply this method to microbiological questions. We discuss recent and potential future applications to determine the composition and distribution of microbial metabolites down to subcellular scale; to investigate the host–microorganism, cell–cell and cell–environment molecular exchanges that underlie the structure of microbial ecosystems from the ocean to the human gut microbiomes; and to interrogate the microbial diversity of functional roles in environmental and industrial processes — key themes in modern microbiology. We describe the current technical limitations of Raman microspectroscopy for investigation of microorganisms and approaches to minimize or address them. Recent technological innovations in Raman microspectroscopy will further reinforce the power and capacity of this method for broader adoptions in microbiology, allowing microbiologists to deepen their understanding of the microbial ecology of complex communities at nearly any scale of interest.
... This method iteratively compares a spectrum with a version of itself that has been blurred via a sliding window average, using a least squares fit. Each wavenumber is then progressively down weighted using separate weights for intensities above and below the fitted curve, with a bias against peaks remaining above the blurred baseline 93 . This approach is typically computationally faster and sim pler than polynomial fitting, while producing similar results, and thus may be better suited to studies with a large number of spectra 93 . ...
... Each wavenumber is then progressively down weighted using separate weights for intensities above and below the fitted curve, with a bias against peaks remaining above the blurred baseline 93 . This approach is typically computationally faster and sim pler than polynomial fitting, while producing similar results, and thus may be better suited to studies with a large number of spectra 93 . ...
Article
Raman microspectroscopy offers microbiologists a rapid and non-destructive technique to assess the chemical composition of individual live microorganisms in near real time. In this Primer, we outline the methodology and potential for its application to microbiology. We describe the technical aspects of Raman analyses and practical approaches to apply this method to microbiological questions. We discuss recent and potential future applications to determine the composition and distribution of microbial metabolites down to subcellular scale; to investigate the host–microorganism, cell–cell and cell–environment molecular exchanges that underlie the structure of microbial ecosystems from the ocean to the human gut microbiomes; and to interrogate the microbial diversity of functional roles in environmental and industrial processes — key themes in modern microbiology. We describe the current technical limitations of Raman microspectroscopy for investigation of microorganisms and approaches to minimize or address them. Recent technological innovations in Raman microspectroscopy will further reinforce the power and capacity of this method for broader adoptions in microbiology, allowing microbiologists to deepen their understanding of the microbial ecology of complex communities at nearly any scale of interest.
... The spectra are converted to reflectance factor using equation (1) (Fig. 15, D). To see the relative depths of the absorption lines, a continuum removal techniquehere Eilers and Boelens (2005) is applied to flatten the continuum (Fig. 15, E); however this removes broader surface features from the spectra. ...
... To use the dataset presented in this work for atmospheric retrievals, such as for fitting to H 2 O or CO lines, a continuum removal method e.g. (Eilers and Boelens, 2005) should be used to remove the effects of the surface and dust/aerosols from the spectra. The method of Cruz-Mermy et al. (2021), (this issue) uses a continuum removal method as part of the calibration procedure, therefore broad spectral shapes such as surface features are already flattened. ...
Article
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The Nadir and Occultation for MArs Discovery (NOMAD) instrument is a 3-channel spectrometer suite on the ESA ExoMars Trace Gas Orbiter. Since April 2018, when the nominal science mission began, it has been measuring the constituents of the Martian atmosphere. NOMAD contains three separate spectrometers, two of which operate in the infrared: the Solar Occultation (SO) channel makes only solar occultation observations, and therefore has the best resolving power (∼20,000) and a wider spectral region covering 2.2–4.3 μm. The Limb, Nadir and Occultation (LNO) channel covers the 2.2–3.8 μm spectral region and can operate in limb, nadir or solar occultation pointing modes. The Ultraviolet–VISible (UVIS) channel operates in the UV–visible region, from 200 to 650 nm, and can measure in limb, nadir or solar occultation modes like LNO. The LNO channel has a lower resolving power (∼10,000) than the SO channel, but is still typically an order of magnitude better than previous instruments orbiting Mars. The channel primarily operates in nadir-viewing mode, pointing directly down to the surface to measure the narrow atmospheric molecular absorption lines, clouds and surface features in the reflected sunlight. From the depth and position of the observed atmospheric absorption lines, the constituents of the Martian atmosphere and their column densities can be derived, leading to new insights into the processes that govern their distribution and transport. Surface properties can also be derived from nadir observations by observing the shape of the spectral continuum. Many calibration measurements were made prior to launch, on the voyage to Mars, and continue to be made in-flight during the science phase of the mission. This work, part 2, addresses the aspects of the LNO channel calibration that are not covered elsewhere, namely: the LNO ground calibration setup, the LNO occultation and nadir boresight pointing vectors, LNO detector characterisation and nadir/limb illumination pattern, instrument temperature effects, and finally the radiometric calibration of the LNO channel. An accompanying paper, part 1 (Thomas et al., 2021, this issue), addresses similar aspects for SO, the other infrared channel in NOMAD. A further accompanying paper (Cruz-Mermy et al., 2021, this issue) investigated the LNO radiometric calibration in more detail, approaching the work from a theoretical perspective. The two calibrations agree with each other to within 3%, validating each calibration method.
... Baseline correction: Fits a baseline curve to a spectrum based on the Asymmetric Least Squares smoothing method (AsLS) developed by Eilers and Boelens. [23,24] While there are multiple approaches for baseline correction of spectra [25], which could also readily be implemented as part of PRISMA's codebase, we prefer the AsLS method due to its relative simplicity, fast computation performance and excellent baseline estimation. [24] Furthermore, unlike traditional approaches, the user does not need to manually indicate anchor points in the spectrum to estimate its baseline, thus minimizing user intervention and facilitating the automation of the correction process. ...
... [23,24] While there are multiple approaches for baseline correction of spectra [25], which could also readily be implemented as part of PRISMA's codebase, we prefer the AsLS method due to its relative simplicity, fast computation performance and excellent baseline estimation. [24] Furthermore, unlike traditional approaches, the user does not need to manually indicate anchor points in the spectrum to estimate its baseline, thus minimizing user intervention and facilitating the automation of the correction process. The method finds a curve being i) smooth, ii) faithful to the spectrum, and iii) asymmetrically penalizing positive residuals, where the analytical peaks are found. ...
Preprint
Full-text available
The popularization of high-throughput spectroscopies to characterize functional materials requires the simultaneous development of new analysis tools to efficiently process large numbers of measurements into scientifically meaningful observables. Here we introduce PRISMA, an open-source tool to rapidly analyze hundreds of spectra in a semi-automated way. PRISMA follows a human-in-the-loop workflow, where the user interacts with an intuitive graphical user interface (GUI) to control multiple steps in the spectrum analysis process: trimming, baseline correction, and peak fitting. The user tunes the analysis in real-time and applies the optimal parameters to all spectra, outputting processed results in an easy-to-read csv format within seconds. Crucially, the tuned parameters are stored to guarantee the full reproducibility of the analysis. We describe the functionalities implemented in PRISMA and test its capabilities with three experimental cases relevant to the study of electrochemical energy storage and conversion devices: temperature-dependent Raman measurement of phase transitions, a linear Raman mapping of a graphite composite electrode, and an operando X-ray diffraction experiment of LiNiO2 Li-ion electrode. Even if X-ray diffraction is not a spectroscopic technique, diffraction patterns are represented as one-dimensional arrays of counts equally suitable for analysis with PRISMA. The case studies demonstrate the robustness of the app and its ability to unearth insightful trends in peak parameters, which are easier to represent, interpret and further analyze with more advanced techniques.
... Baseline correction: Fits a baseline curve to a spectrum based on the Asymmetric Least Squares smoothing method (AsLS) developed by Eilers and Boelens. [22,23] While there are multiple approaches for baseline correction of spectra [24], which could also readily be implemented as part of PRISMA's codebase, we prefer the AsLS method due to its relative simplicity, fast computation performance and excellent baseline estimation. [23] Furthermore, unlike traditional approaches, the user does not need to manually indicate anchor points in the spectrum to estimate its baseline, thus minimizing user intervention and facilitating the automation of the correction process. ...
... [22,23] While there are multiple approaches for baseline correction of spectra [24], which could also readily be implemented as part of PRISMA's codebase, we prefer the AsLS method due to its relative simplicity, fast computation performance and excellent baseline estimation. [23] Furthermore, unlike traditional approaches, the user does not need to manually indicate anchor points in the spectrum to estimate its baseline, thus minimizing user intervention and facilitating the automation of the correction process. The method finds a curve being i) smooth, ii) faithful to the spectrum, and iii) asymmetrically penalizing positive residuals, where the analytical peaks are found. ...
Preprint
Full-text available
The popularization of high-throughput spectroscopies to characterize functional materials requires the simultaneous development of new analysis tools to efficiently process large numbers of measurements into scientifically meaningful observables. Here we introduce PRISMA, an open-source tool to rapidly analyze hundreds of spectra in a semi-automated way. PRISMA follows a human-in-the-loop workflow, where the user interacts with an intuitive graphical user interface (GUI) to control multiple steps in the spectrum analysis process: trimming, baseline correction, and peak fitting. The user tunes the analysis in real-time and applies the optimal parameters to all spectra, outputting processed results in an easy-to-read csv format within seconds. Crucially, the tuned parameters are stored to guarantee the full reproducibility of the analysis. We describe the functionalities implemented in PRISMA and test its capabilities with three experimental cases relevant to the study of electrochemical energy storage and conversion devices: temperature-dependent Raman measurement of phase transitions, a linear Raman mapping of a graphite composite electrode, and an operando X-ray diffraction experiment of LiNiO2 Li-ion electrode. Even if X-ray diffraction is not a spectroscopic technique, diffraction patterns are represented as one-dimensional arrays of counts equally suitable for analysis with PRISMA. The case studies demonstrate the robustness of the app and its ability to unearth insightful trends in peak parameters, which are easier to represent, interpret and further analyze with more advanced techniques.
... In all, approximately 22,000 intensity values (corresponding to 22,000 m/Z) composing a spectrum were submitted to a three-step preprocessing. First, the baseline was subtracted using the asymmetric least squares smoothing method 16 . Second, a Fourier transformation was applied to smooth the spectra to avoid small variations in intensity. ...
... Convolutional neural networks are powerful discriminative models that are only now starting to be used in the medical domain, mostly in image recognition in radiology applied to oncology 16,17 . The method is potentially more acute than anything a human brain could accomplish, as it is able to assess and remember a larger number of data with small variations; hence, our desire to test it with the differentiation of mass spectra very similar to one another, all identified as belonging to the same species. ...
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The spread of fungal clones is hard to detect in the daily routines in clinical laboratories, and there is a need for new tools that can facilitate clone detection within a set of strains. Currently, Matrix Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry is extensively used to identify microbial isolates at the species level. Since most of clinical laboratories are equipped with this technology, there is a question of whether this equipment can sort a particular clone from a population of various isolates of the same species. We performed an experiment in which 19 clonal isolates of Aspergillus flavus initially collected on contaminated surgical masks were included in a set of 55 A. flavus isolates of various origins. A simple convolutional neural network (CNN) was trained to detect the isolates belonging to the clone. In this experiment, the training and testing sets were totally independent, and different MALDI-TOF devices (Microflex) were used for the training and testing phases. The CNN was used to correctly sort a large portion of the isolates, with excellent (> 93%) accuracy for two of the three devices used and with less accuracy for the third device (69%), which was older and needed to have the laser replaced.
... This method iteratively compares a spectrum with a version of itself that has been blurred via a sliding window average, using a least squares fit. Each wavenumber is then progressively down weighted using separate weights for intensities above and below the fitted curve, with a bias against peaks remaining above the blurred baseline 93 . This approach is typically computationally faster and sim pler than polynomial fitting, while producing similar results, and thus may be better suited to studies with a large number of spectra 93 . ...
... Each wavenumber is then progressively down weighted using separate weights for intensities above and below the fitted curve, with a bias against peaks remaining above the blurred baseline 93 . This approach is typically computationally faster and sim pler than polynomial fitting, while producing similar results, and thus may be better suited to studies with a large number of spectra 93 . ...
Article
Raman microspectroscopy offers microbiologists a rapid and non-destructive technique to assess the chemical composition of individual live microorganisms in near real time. In this Primer, we outline the methodology and potential for its application to microbiology. We describe the technical aspects of Raman analyses and practical approaches to apply this method to microbiological questions. We discuss recent and potential future applications to determine the composition and distribution of microbial metabolites down to subcellular scale; to investigate the host–microorganism, cell–cell and cell–environment molecular exchanges that underlie the structure of microbial ecosystems from the ocean to the human gut microbiome; and to interrogate the microbial diversity of functional roles in environmental and industrial processes — key themes in modern microbiology. We describe the current technical limitations of Raman microspectroscopy for investigation of microorganisms and approaches to minimize or address them. Recent technological innovations in Raman microspectroscopy will further reinforce the power and capacity of this method for broader adoptions in microbiology, allowing microbiologists to deepen their understanding of the microbial ecology of complex communities at nearly any scale of interest.
... It is clear that the background is dominated by fluorescence, which may itself contain valuable information and will be subject to future investigations. We applied a common asymmetric least square smoothing algorithm [26,27] that fits a curve to the bottom of fluctuating data. ...
Article
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Raman spectroscopy has shown to be a promising method for the examination of biomedical samples. However, until now, its efficacy has not been established in clinical diagnostics. In this study, Raman spectroscopy’s potential application in medical laboratories is evaluated for a large variety (38) of biomarkers. Given 234 serum samples from a cohort of patients with different stages of liver disease, we performed Raman spectroscopy at 780nm excitation wavelength. The Raman spectra were analyzed in combination with the results of routine diagnostics using specifically developed complex mathematical algorithms, including fluorescence filtering, frequency subset selection and several overfitting circumventing strategies, such as independent validation. With the results of this cohort, which were validated in 328 independent samples, a significant proof-of-concept study was completed. This study highlights the need to prevent overfitting and to use independent data for validation. The results reveal that Raman spectroscopy has high potential for use in medical laboratory diagnostics to simultaneously quantify multiple biomarkers.
... For this use of baseline correction methods is in practice. For this asymmetric least squares (ALS) 25 is considered. ALS is based on least squares algorithm which weights explanatory variables with positive differences. ...
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Chemotherapy appeared to be a significant advancement in cancer research, with fewer side effects. Methotrexate (MTX) is a widely used anticancer drug with strong activity but serious side effects. Several MTX derivatives have been reported, with modifications at various sites to reduce side effects and increase efficacy. The current study uses FTIR spectroscopy to predict the survival fraction of human malignant glioma U87 (MG-U87) cell lines against MTX derivatives. Together with Parent MTX several aldehydes viz. Benzaldehyde, Chlorobenzaldehyde, 2-Chlorobenzaldehyde, 3-Nitrobenzaldehyde, 5-Chloro-2-hydroxybenz-aldehyde, 2-Hydroxy-5-Nitrobenzaldehyde, 2-Thiocarboxyaldehyde, Trans-2-pentenal, and Glutaraldehyde are treated with MTX to obtain MTX derivatives. The prediction of survival fraction of malignant glioma cells is carried out by Lasso, Elastic net and Soft PLS at different concentration levels of synthesized derivatives, including 400 μM, 200 μM, 100 μM, 50 μM, 25 μM and 12.5 μM. The cross-validated prediction error is minimised to optimise spectral wavelength selection and model parameters. It appears that the RMSE computed from test data is significantly varying with the change of models (p = 0.012), with the change of concentrations levels (p ≤0.001) and with the change of combination of models and concentration level (p ≤0.001). StPLS outperforms in predicting survival fraction of glioma cells at the concentration level 50 μM, 100 μM and 400 μM respectively with relative RMSE = 0.1,0.14 and 0.55. Lasso outperforms at the concentration level 12.5 μM, and 200 μM respectively with relative RMSE = 0.4 and 0.14. Elastic net outperforms at the concentration level 25 μM with relative RMSE = 0.8. Consistently appeared influential wavelength identifies the influential functional compounds which best predicts the survival fraction. Hence FTIR appears potential candidate for estimating survival fraction of MTX derivatives.
... Changes in fluorescence of responsive regions over time were baseline corrected using asymmetric least squares smoothing (Eilers and Boelens 2005) and response tuning profiles were determined by normalizing each individual fluorescence intensity timeseries by its maximum intensity value (baseline: 0, maximum: 1). We then defined a threshold below which to ignore amplitude peaks in the expected peak response intervals (12.5%). ...
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During metamorphosis, the olfactory system of anuran tadpoles undergoes substantial restructuring. The main olfactory epithelium in the principal nasal cavity of Xenopus laevis tadpoles is associated with aquatic olfaction and transformed into the adult air-nose, while a new adult water-nose emerges in the middle cavity. Impacts of this metamorphic remodeling on odor processing, behavior, and network structure are still unexplored. Here, we used neuronal tracings, calcium imaging, and behavioral experiments to examine the functional connectivity between the epithelium and the main olfactory bulb during metamorphosis. In tadpoles, olfactory receptor neurons in the principal cavity project axons to glomeruli in the ventral main olfactory bulb. These projections are gradually replaced by receptor neuron axons from the newly forming middle cavity epithelium. Despite this reorganization in the ventral bulb, two spatially segregated odor processing streams remain undisrupted and behavioral responses to waterborne odorants are unchanged. Contemporaneously, new receptor neurons in the remodeling principal cavity innervate the emerging dorsal part of the bulb, which displays distinct wiring features. Glomeruli around its midline are innervated from the left and right nasal epithelia. Additionally, postsynaptic projection neurons in the dorsal bulb predominantly connect to multiple glomeruli, while half of projection neurons in the ventral bulb are uni-glomerular. Our results show that the “water system” remains functional despite metamorphic reconstruction. The network differences between the dorsal and ventral olfactory bulb imply a higher degree of odor integration in the dorsal main olfactory bulb. This is possibly connected with the processing of different odorants, airborne vs. waterborne.
... A Raman spectra processing script was written in the Python programming language to smooth and flatten the raw data ( Figure S8). A Savitzky-Golay (SG) filter [31] was applied to smooth each spectra, and an asymmetric least squares (ALS) smoothing algorithm [32] was used to approximate the baseline. The processing parameters were selected for the data on hand; typical values were a window size of 5 and an order of 3 for the SG filter and a smoothness of 1000 and an asymmetry of 0.01 for the ALS smoothing. ...
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Gold nanoparticles (AuNPs) were used experimentally for non-invasive in vivo Raman monitoring because they show a strong absorbance in the phototherapeutic window (650–850 nm), a feature that is accompanied by a particle size in excess of 100 nm. However, these AuNPs cannot be used clinically because they are likely to persist in mammalian systems and resist excretion. In this work, clustered ultrasmall (sub-5 nm) AuNP constructs for in vivo Raman diagnostic monitoring, which are also suitable for mammalian excretion, were synthesized and characterized. Sub-5 nm octadecyl amine (ODA)-coated AuNPs were clustered using a labile dithiol linker: ethylene glycol bis-mercaptoacetate (EGBMA). Upon clustering via a controlled reaction and finally coating with a polymeric amphiphile, a strong absorbance in the phototherapeutic window was demonstrated, thus showing the potential suitability of the construct for non-invasive in vivo detection and monitoring. The clusters, when labelled with a biphenyl-4-thiol (BPT) Raman tag, were shown to elicit a specific Raman response in plasma and to disaggregate back to sub-5 nm particles under physiological conditions (37 °C, 0.8 mM glutathione, pH 7.4). These data demonstrate the potential of these new AuNP clusters (Raman NanoTheranostics—RaNT) for in vivo applications while being in the excretable size window.
... The _counts array is given by the prompt gamma entries in the bin after applying a Savitzky-Golay smoothing filter. The baseline array was obtained after applying the non-linear iterative peak (SNIP) technique 33,34 for background subtraction. The bins array contains the energy information after the time integration of the CeBr 3 peaks in every trace (for a maximum of 3 peaks). ...
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The number of radiotherapy patients treated with protons has increased from less than 60,000 in 2007 to more than 220,000 in 2019. However, the considerable uncertainty in the positioning of the Bragg peak deeper in the patient raised new challenges in the proton therapy of prostate cancer (PCPT). Here, we describe and share a dataset where 43 single-spot anterior beams with defined proton energies were delivered to a prostate phantom with an inserted endorectal balloon (ERB) filled either with water only or with a silicon-water mixture. The nuclear reactions between the protons and the silicon yield a distinct prompt gamma energy line of 1.78 MeV. Such energy peak could be identified by means of prompt gamma spectroscopy (PGS) for the protons hitting the ERB with a three-sigma threshold. The application of a background-suppression technique showed an increased rejection capability for protons hitting the prostate and the ERB with water only. We describe each dataset, document the full processing chain, and provide the scripts for the statistical analysis. Measurement(s)single-spot proton ranges • surface of prostateTechnology Type(s)prompt gamma spectroscopyFactor Type(s)campaign • beam energy • processing Measurement(s) single-spot proton ranges • surface of prostate Technology Type(s) prompt gamma spectroscopy Factor Type(s) campaign • beam energy • processing Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15156039
... In line with previous observations (5,(15)(16)(17), inspection of the raster plots and of the instantaneous population firing curves revealed short synchronous population activity events, which occurred both in the anesthetized and awake states but seemed more contrasted and stereotypical under anesthesia ( Fig. 1C-D, S1). We extracted these events by applying a baseline corrected (18) threshold to the population firing rate above which a synchronous event could not be explained by the fluctuations of summed independent Poisson processes ( Fig. 1C-D, S1). Population events were short but of variable durations (327+/-131ms, n=11 mice in awake state, 414+/-237 ms, n=6 mice under anesthesia) and appeared both during the stimulation-free (0.42+/-0.1 Hz, n=11 mice in awake state, 0.51+/-0.11 ...
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Neural activity in sensory cortex combines stimulus responses and ongoing activity, but it remains unclear whether they reflect the same underlying dynamics or separate processes. Here we show that during wakefulness, the neuronal assemblies evoked by sounds in the auditory cortex and thalamus are specific to the stimulus and distinct from the assemblies observed in ongoing activity. In contrast, during anesthesia, evoked assemblies are indistinguishable from ongoing assemblies in cortex, while they remain distinct in the thalamus. A strong remapping of sensory responses accompanies this dynamical state change produced by anesthesia. Together, these results show that the awake cortex engages dedicated neuronal assemblies in response to sensory inputs, which we suggest is a network correlate of sensory perception. One-Sentence Summary Sensory responses in the awake cortex engage specific neuronal assemblies that disappear under anesthesia.
... ImageJ 21 was used to extract the mean gray value F of each ROI. Asymmetric least square smoothing 22 was implemented in MATLAB to calculate the baseline F 0 . Fluorescence change was obtained by ...
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Neuronal cultures are widely used in neuroscience research. However, the randomness of circuits in conventional cultures prevents accurate in vitro modeling of cortical development and of the pathogenesis of neurological and psychiatric disorders. A basic feature of cortical circuits that is not captured in standard cultures of dissociated cortical cells is directional connectivity. In this work, a polydimethylsiloxane (PDMS)-based device that achieves directional connectivity between micro 3D cultures is demonstrated. The device consists of through-holes for micro three-dimensional (μ3D) clusters of cortical cells connected by microtrenches for axon and dendrite guidance. The design of the trenches relies in part on the concept of axonal edge guidance, as well as on the novel concept of specific dendrite targeting. This replicates dominant excitatory connectivity in the cortex, enables the guidance of the axon after it forms a synapse in passing (an “en passant” synapse), and ensures that directional selectivity is preserved over the lifetime of the culture. The directionality of connections was verified morphologically and functionally. Connections were dependent on glutamatergic synapses. The design of this device has the potential to serve as a building block for the reconstruction of more complex cortical circuits in vitro.
... 10 by 10 pixel binning was applied to the fluorescence recordings to minimize impact of contraction of heart organoids. Baseline F 0 of the fluorescence intensities F was calculated using asymmetric least squares smoothing 93 . Fluorescence change ΔF/F 0 was calculated by: ...
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Congenital heart defects constitute the most common human birth defect, however understanding of how these disorders originate is limited by our ability to model the human heart accurately in vitro. Here we report a method to generate developmentally relevant human heart organoids by self-assembly using human pluripotent stem cells. Our procedure is fully defined, efficient, reproducible, and compatible with high-content approaches. Organoids are generated through a three-step Wnt signaling modulation strategy using chemical inhibitors and growth factors. Heart organoids are comparable to age-matched human fetal cardiac tissues at the transcriptomic, structural, and cellular level. They develop sophisticated internal chambers with well-organized multi-lineage cardiac cell types, recapitulate heart field formation and atrioventricular specification, develop a complex vasculature, and exhibit robust functional activity. We also show that our organoid platform can recreate complex metabolic disorders associated with congenital heart defects, as demonstrated by an in vitro model of pregestational diabetes-induced congenital heart defects.
... Current data sampled at 50 kHz was first low-pass filtered using a 200-sample-wide rectangular moving average filter. The filtered signal was downsampled to 2.5 kHz, then detrended using asymmetric least squares smoothing [22]. The element-wise difference of the entire data vector was computed and thresholded according to user-supplied values; for example, for AP-1060 cell measurements, we used a normalized drop-change in current (relative to baseline) cutoff of 2 x 10 −4 for pores and 1 x 10 −3 for contraction segments. ...
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Cellular mechanical properties can reveal physiologically relevant characteristics in many cell types, and several groups have developed microfluidics-based platforms to perform high-throughput single-cell mechanical testing. However, prior work has performed only limited characterization of these platforms’ technical variability and reproducibility. Here, we evaluate the repeatability performance of mechano-node-pore sensing, a single-cell mechanical phenotyping platform developed by our research group. We measured the degree to which device-to-device variability and semi-manual data processing affected this platform’s measurements of single-cell mechanical properties. We demonstrated high repeatability across the entire technology pipeline even for novice users. We then compared results from identical mechano-node-pore sensing experiments performed by researchers in two different laboratories with different analytical instruments, demonstrating that the mechanical testing results from these two locations are in agreement. Our findings quantify the expectation of technical variability in mechano-node-pore sensing even in minimally experienced hands. Most importantly, we find that the repeatability performance we measured is fully sufficient for interpreting biologically relevant single-cell mechanical measurements with high confidence.
... Note that EMSC both acts as baseline correction between spectra and normalization. Asymmetric least square (ALS) was implemented based on the work of Eilers and Boelens [23]. Detrending [24] consisted in subtracting a polynomial fit of the considered spectrum. ...
Article
Vibrational spectroscopy has become a valuable tool in many fields as it provides a molecular signature with a non-destructive measurement. Identification and prediction performance of the technique greatly depend on pre-processing steps used to remove unwanted sources of variability, especially for biological matter. However, finding the right combination of pre-processing methods (smoothing, baseline correction and/or normalization) is not a trivial task and usually depends on the operator habits. As testing all possible pre-processing sequences is time consuming, genetic algorithms (GAs) were put forward as a way to quickly find a relatively good sequence. We present here a GA that additionally optimizes the regression model, making the whole data analysis process automated, paving the way to automated machine learning. To make the best of GAs, we determined the optimal GA parameters, based on three datasets of different VS modalities (Raman or IR spectra from food industry or biological samples). They depended on the desired quality of the solution, but hardly on the dataset itself, meaning they could be used on new data without further tuning. Our method compares positively with random search, ant colony optimization and tree-structured Parzen Estimator, commonly used in machine learning for tuning hyperparameters. In conclusion, we provide a GA adapted to the simultaneous selection of pre-processing and regression of vibrational spectra.
... 91 Baseline correction can be achieved by a variety of mathematical methods, including simple methods like offset correction, calculating derivatives, polynomial baseline correction, Savitzky-Golay baseline correction or Asymmetric Least Squares Smoothing (AsLS). 92 Pre-processing usually includes normalization of data, commonly done by Min-Max normalization, Standard Normal Variate or Vector normalization. Interpretation of spectra relies on peak positions and relative areas and can be achieved by inspection of individual spectra. ...
Thesis
Die Ablagerung von Siliziumdioxid ist ein verbreitetes Phänomen, das mit der Toleranz von Pflanzen gegenüber Belastungen korreliert. Die Pflanzen akkumulieren das amorphe Siliziumdioxid in mikroskopischen Partikeln, den Phytolithen, jedoch ist der exakte Mechanismus nicht vollständig aufgeklärt. Um ein besseres Verständnis über die Ablagerung von Siliziumdioxid zu erlangen, wurden verschiedene spektroskopische Techniken an Sorghumblättern und molekularen Modellen angewandt. Festkörper Kernspinresonanz und thermogravimetrische Analysen zeigen, dass die Siliziumdioxidstruktur von der Phytolithe-Extraktion abhängt. Basierend auf Raman- und IR-Daten einzelner Phytolithe lassen sich die Änderungen dieser Strukturen ermitteln. Das deutet auf unterschiedliche biologische Prozesse der Ablagerung des Siliciumdioxids hin. Die Pflanzengewebe in denen Siliciumdioxid abgelagert ist, wurden mit einem multimodalen Ansatz charakterisiert, welcher Fluoreszenz-, Hellfeld- und Rasterelektronenmikroskopie beinhaltet. Die chemische Zusammensetzung der Pflanzengewebe wurden mit Raman- und FTIR-Mikrospektroskopie kartiert. Ein neuartiger Ansatz zur Untersuchung von Pflanzengeweben wurde verwendet, basierend auf der optischen Nahfeldmikroskopie im mittleren IR-Bereich. Dieser ermöglicht eine kombinierte Analyse von mechanischen Materialeigenschaften sowie der chemischen Zusammensetzung und Struktur. Um die Rolle der organischen Matrix zu verstehen, wurden Modellverbindungen betrachtet, die die Ablagerung von Kieselsäure in den Pflanzen induzieren können. In-vitro-Reaktionen konnten eine gleichzeitige Präzipitation von Lignin und Siliciumdioxid sowie eine Polymerisation zusammen mit Peptiden simulieren. Die Ergebnisse lassen starke Wechselwirkungen zwischen diesen Verbindungen vermuten. Neben einem besseren Verständnis verschiedener Aspekte der Silifizierung von Pflanzen werden in dieser Arbeit neue Methoden zur Charakterisierung von Pflanzenproben vorgeschlagen.
... Noise reduction is conventionally achieved by Savitzky−Golay filtering, 9−11 whereas baseline artifacts can be removed by different baseline-subtraction methods, such as fitting polynomials or asymmetric least-square smoothing. 12,13 Although established, all of the mentioned methods require adjusting a variety of parameters and need to be combined to cope with different kinds of spectral artifacts simultaneously. When applied to a set of spectra containing a large variety of different spectra having different levels of noise and different kinds of baseline distortions, they will not yield ideal results for all of the spectra. ...
Article
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Herein we report on a deep-learning method for the removal of instrumental noise and unwanted spectral artifacts in Fourier transform infrared (FTIR) or Raman spectra, especially in automated applications in which a large number of spectra have to be acquired within limited time. Automated batch workflows allowing only a few seconds per measurement, without the possibility of manually optimizing measurement parameters, often result in challenging and heterogeneous datasets. A prominent example of this problem is the automated spectroscopic measurement of particles in environmental samples regarding their content of microplastic (MP) particles. Effective spectral identification is hampered by low signal-to-noise ratios and baseline artifacts as, again, spectral post-processing and analysis must be performed in automated measurements, without adjusting specific parameters for each spectrum. We demonstrate the application of a simple autoencoding neural net for reconstruction of complex spectral distortions, such as high levels of noise, baseline bending, interferences, or distorted bands. Once trained on appropriate data, the network is able to remove all unwanted artifacts in a single pass without the need for tuning spectra-specific parameters and with high computational efficiency. Thus, it offers great potential for monitoring applications with a large number of spectra and limited analysis time with availability of representative data from already completed experiments.
... Several preprocessing methods were used to minimize baseline variations in the data used for multivariate calibration. The preprocessing methods tested were multiplicative scatter correction (MSC), alternating least squares (ALS) [31], and first derivative. The multivariate calibration method used was partial least squares (PLS) regression. ...
Article
This paper describes a new method to obtain NIR spectra of liquid and gas samples by diffuse reflectance, which is especially suitable for handheld spectrophotometers, since most of these instruments are designed to acquire spectrum using this geometry. The core of the method is a diffuse reflectance cell, which consists of a vial containing a mixture of the liquid or gas sample (rare medium) and a powder (dense medium). Using this strategy, no adaptation is required to measure spectra with most portable NIR spectrometers. This new method was used to obtain NIR spectra of several liquids and gases, which were compared with traditional transmittance spectra. As a proof of concept, measurements of biodiesel/vegetable oil/diesel blends were used to build multivariate calibrations to predict the contents of biodiesel and vegetable oil in diesel blends using benchtop and handheld FT-NIR spectrophotometers. This low-cost method was demonstrated to be suitable for overcoming problems related to the handling of viscous samples and expand the applications with portable NIR instruments.
... For this, asymmetric least squares (ALS) is used. For weighting down predictor variables with substantial error, it employs the least square approach and smoothing by adding a 2 nd derivative restriction 18,30 ...
Article
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Imidazole has anti-inflammatory, antituberculotic, antimicrobial, antimycotic, antiviral, and antitumor properties in the human body, to name a few. Metronidazole [1-(2-Hydroxyethyl)-2-methyl-5-nitroimidazole] is a widely used antiprotozoan and antibacterial medication. Using fourier transform infrared spectroscopy, the current study models the antibacterial activity of already synthesised Metronidazole (MTZ) complexes (MTZ-Benz, MTZ-Benz-Cu, MTZ-Benz-Cu-Cl2CHCOOH, MTZ, MTZ-Cu, MTZ-Cu-Cl2CHCOOH, MTZ-Benz-Ag, MTZ-Benz-Ag-Cl2CHCOOH, MTZ-Ag and MTZ-Ag-Cl2CHCOOH) against E. coli, B. bronceptica, S. epidermidis, B. pumilus and S. aureus. To characterise the Metronidazole complexes for antibacterial activity against 05 microbes, the least angular regression and least absolute shrinkage selection operators were used. Asymmetric Least Squares was used to correct the spectrum baseline. Least angular regression outperforms cross-validated root mean square error in the fitted models. Using Least angular regression, influential wavelengths that explain the variation in antibacterial activity of Metronidazole complexes were identified and mapped against functional groups.
... Here, smoothing, taking the first derivative and normalization to equal length were selected for fluorescence. IR was preprocessed by subtraction of a reference spectrum recorded before elution in addition to smoothing and baseline correction by asymmetric least squares (Eilers & Boelens, 2005) implemented in the R package baseline (Liland et al., 2010). ...
Article
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Technological developments require the transfer to their location of application in order to make use of them. We describe the transfer of a real-time monitoring system for lab-scale preparative chromatography to two new sites where it will be used and developed further. Equivalent equipment was used. The capture of a biopharmaceutical model protein, human fibroblast growth factor 2 (FGF-2) was used to evaluate the system transfer. Predictive models for five quality attributes based on partial least squares (PLS) regression were transferred. Six out of seven online sensors (UV/VIS, pH, conductivity, IR, RI, MALS) showed comparable signals between the sites while one sensor (fluorescence) showed different signal profiles. A direct transfer of the models for real-time monitoring was not possible, mainly due to differences in sensor signals. Adaptation of the models was necessary. Then, among five prediction models, the prediction errors of the test runs at the new sites were on average twice as high as at the training site (model-wise 0.9 – 5.7 times). Additionally, new prediction models for different products were trained at each new site. These allowed monitoring the critical quality attributes of two new biopharmaceutical products during their purification processes with mean relative deviations between 1 and 33%. This article is protected by copyright. All rights reserved.
... A 10 sec acquisition time for the detector was chosen per measurement. Then, the baseline was established and noise was removed automatically using previously established asymmetric least squares smoothing [62]. ...
Article
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Bacterial extracellular vesicles (EVs) are nanoscale lipid-enclosed packages that are released by bacteria cells and shuttle various biomolecules between bacteria or host cells. They are implicated in playing several important roles, from infectious disease progression to maintaining proper gut health, however the tools available to characterise and classify them are limited and impractical for many applications. Surface-enhanced Raman Spectroscopy (SERS) provides a promising means of rapidly fingerprinting bacterial EVs in a label-free manner by taking advantage of plasmonic resonances that occur on nanopatterned surfaces, effectively amplifying the inelastic scattering of incident light. In this study, we demonstrate that by applying machine learning algorithms to bacterial EV SERS spectra, EVs from cultures of the same bacterial species (Escherichia coli) can be classified by strain, culture conditions, and purification method. While these EVs are highly purified and homogeneous compared to complex samples, the ability to classify them from a single species demonstrates the incredible power of SERS when combined with machine learning, and the importance of considering these parameters in future applications. We anticipate that these findings will play a crucial role in developing the laboratory and clinical utility of bacterial EVs, such as the label-free, noninvasive, and rapid diagnosis of infections without the need to culture samples from blood, urine, or other fluids.
... A confocal hole was set to 250 μm in backscattering geometry. Then, the baseline was established and noise was removed using asymmetric least-squares smoothing established in ref 82. ...
Article
Placental extracellular vesicles (EVs) play an essential role in pregnancy by protecting and transporting diverse biomolecules that aid in fetomaternal communication. However, in preeclampsia, they have also been implicated in contributing to disease progression. Despite their potential clinical value, current technologies cannot provide a rapid and effective means of differentiating between healthy and diseased placental EVs. To address this, a fabrication process called laser-induced nanostructuring of SERS-active thin films (LINST) was developed to produce scalable nanoplasmonic substrates that provide exceptional Raman signal enhancement and allow the biochemical fingerprinting of EVs. After validating the performance of LINST substrates with chemical standards, placental EVs from tissue explant cultures were characterized, demonstrating that preeclamptic and normotensive placental EVs have classifiably distinct Raman spectra following the application of advanced machine learning algorithms. Given the abundance of placental EVs in maternal circulation, these findings encourage immediate exploration of surface-enhanced Raman spectroscopy (SERS) of EVs as a promising method for preeclampsia liquid biopsies, while this novel fabrication process will provide a versatile and scalable substrate for many other SERS applications.
... En effet, la ligne de base d'un spectre varie lentement, il est donc possible de la modéliser par un polynôme d'ordre faible déterminé numériquement. L'algorithme de moindres carrés asymétriques (ALS) développé par [Eilers and Boelens, 2005] a montré les résultats les plus prometteurs pour être appliqué à la spectrométrie γ aéroportée. ...
Thesis
La spectrométrie gamma aéroportée est une technique de mesure efficace pour établir un bilan radiologique ponctuel d'un site de grande étendue. Son application à la surveillance environnementale nécessite une répétabilité temporelle des mesures de concentration des radionucléides dans le sol. Le caractère innovant de cette étude repose sur l'acquisition couplée de spectres gamma issus d'un vol stationnaire simulé à 50 mètres et de mesures de facteurs environnementaux sur une chronique temporelle continue de 14 mois, entre Avril 2019 et Juin 2020, qui permet de caractériser les facteurs d'influence. Le site d'application, le P2OA-CRA de Lannemezan, a été choisi pour la présence d'un mât météorologique de 60 mètres permettant d'installer un spectromètre NaI(Tl), de type RSX-5 de même composition que celui utilisé lors des campagnes de mesures du CEA. L'objectif principal de cette thèse est de s'affranchir des différents biais de mesures pour isoler le signal gamma émis depuis le sol. Ainsi, les chroniques temporelles ont permis d'observer et de quantifier les effets environnementaux associés à la variation de l'humidité du sol, de la pression atmosphérique, des rayonnements cosmiques, des précipitations et du radon-222 atmosphérique. Chaque paramètre a fait l'objet d'une étude dédiée qui a débouché sur la mise en place d'une correction de cet effet sur le signal gamma mesuré. La loi d'ajustement théorique des radionucléides, couplées à des simulations Geant-4, ont validé la correction des effets de l'humidité du sol. Elles permettent d'exprimer les concentrations des radionucléides en sol sec et ne sont donc plus fonction de l'humidité du sol à l'instant de la mesure, qui représente une importante source de variabilité. Cette procédure a d'abord été validée pour le 40K, le 232Th ainsi que le 137Cs, puis pour le 238U, une fois l'influence du 222Rn atmosphérique soustraite du signal. Cette correction est également applicable en utilisant les humidités du sol acquises par télédétection satellitaire, notamment les produits L4 de SMAP. Une étude complémentaire sur le suivi du profil de l'humidité du sol par des rayonnements gamma d'un même radionucléide à différentes énergies ouvre des perspectives d'application en agriculture de précision. La caractérisation d'une fenêtre d'énergie centrée sur les rayonnements cosmiques a permis d'éliminer leur influence sur le signal gamma mesuré. Si l'effet du cycle semi-diurne de la pression atmosphérique sur les taux de comptage a pu être mis en évidence pour tous les radionucléides, son influence limitée n'a pas fait l'objet d'une correction dédiée. L'effet des précipitations centré uniquement sur le 238U a pu être étudié sur des profils d'événements de pluie uniques d'intensité variable. L'augmentation du taux de comptage de 238U associée à ces événements de pluie, présente une relation linéaire avec l'intensité des épisodes pluvieux. L'étude couplée des chroniques temporelles du 222Rn et du 238U a permis de quantifier l'influence du radon atmosphérique sur le signal gamma de l'uranium. Une composante spectrale associée au radon atmosphérique a été établie en ce sens. Suite à cette nouvelle quantification de l'influence du 222Rn atmosphérique sur le signal du 238U, une méthode pionnière d'estimation de l'activité volumique de 222Rn à partir de la spectrométrie gamma aéroportée a été développée. [...]
... Initially, before data pre-treatment, the raw spectra were scanned to exclude aberrant measurements (e.g., due to an empty measurement chamber), by checking the overall intensity and that of the water bands in particular. Due to the different levels of fluorescence background, all spectra were then baseline-corrected with an asymmetric least-squares algorithm [20] (Figure S2B), as this correction method proved to be superior to the standard pre-treatment with standard normal variate (SNV) and first derivative analysis. The water band at 1650 cm −1 was utilized to normalize all spectra, and therefore correct the potential variations that were not caused by the process itself, but to other confounding factors ( Figure S2C). ...
Article
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Raman spectroscopy is an analytical technology for the simultaneous measurement of important process parameters, such as concentrations of nutrients, metabolites, and product titer in mammalian cell culture. The majority of published Raman studies have concentrated on using the technique for the monitoring and control of bioreactors at pilot and manufacturing scales. This research presents a novel approach to generating Raman models using a high-throughput 250 mL mini bioreactor system with the following two integrated analysis modules: a prototype flow cell enabling on-line Raman measurements and a bioanalyzer to generate reference measurements without a significant time-shift, compared to the corresponding Raman measurement. Therefore, spectral variations could directly be correlated with the actual analyte concentrations to build reliable models. Using a design of experiments (DoE) approach and additional spiked samples, the optimized workflow resulted in robust Raman models for glucose, lactate, glutamine, glutamate and titer in Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies (mAb). The setup presented in this paper enables the generation of reliable Raman models that can be deployed to predict analyte concentrations, thereby facilitating real-time monitoring and control of biologics manufacturing.
... All spectra were baseline-corrected prior to analysis using an asymmetric least-squares method applied in MATLAB. 66 Image Analysis. Particle tracking was used on SEM images to determine the area occupied by silver deposits. ...
Article
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Several reports present methods to fabricate thin-film substrates capable of surface-enhanced Raman scattering (SERS). Substrates synthesized by displacing silver onto copper using facile synthesis methods such as galvanic displacement can generate high levels of SERS enhancement rivaling commercially available substrates manufactured by lithographic methods. Here, we describe the optimization of a novel set of SERS-active thin-film substrates synthesized via the electroless displacement of Ag onto the surface of three-dimensional (3D) printed disks composed of the copper/polymer (PLA) composite filament. The effect of AgNO3 concentration on the deposition, morphology, and overall SERS activity of the substrates has been carefully studied. Two commonly used Raman reporters, 4-mercaptobenzoic acid (MBA) and malachite green isothiocyanate (MGITC), were used to measure the SERS output of the substrates. Good SERS signal reproducibility (RSD ∼16.8%) was measured across the surface of replicate substrates and high-sensitivity detection of MBA was achieved (10–12 M). To test the real-world application of our substrates, we opted to detect 5-chloro-2-methyl-4-isothiazolin-3-one (CMIT), which is a genotoxic, biocide common in many household products, known to leach into water supplies. Our newly developed SERS-active substrates could detect CMIT down to 10 ppm when spiked in simulated lake water samples, which is well within current agency standards.
... First, data pre-treatment was necessary to eliminate the fluorescence background and sensitivity differences that are hardware related such as probe mounting variations. Therefore, all spectra were baseline corrected with an asymmetric least squares algorithm (p = 0.05, λ = 10 7 ) (Eilers and Boelens, 2005) before normalizing the spectra to the integral of the waterband around 1650 cm −1 (Lawaetz and Stedmon, 2009; Li et al., 2013). For each analyte of interest, i.e., glucose, and lactate, separate OPLS models (Orthogonal Projection to Latent Structures) were built. ...
Article
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Continuous manufacturing is becoming more important in the biopharmaceutical industry. This processing strategy is favorable, as it is more efficient, flexible, and has the potential to produce higher and more consistent product quality. At the same time, it faces some challenges, especially in cell culture. As a steady state has to be maintained over a prolonged time, it is unavoidable to implement advanced process analytical technologies to control the relevant process parameters in a fast and precise manner. One such analytical technology is Raman spectroscopy, which has proven its advantages for process monitoring and control mostly in (fed-) batch cultivations. In this study, an in-line flow cell for Raman spectroscopy is included in the cell-free harvest stream of a perfusion process. Quantitative models for glucose and lactate were generated based on five cultivations originating from varying bioreactor scales. After successfully validating the glucose model (Root Mean Square Error of Prediction (RMSEP) of ∼0.2 g/L), it was employed for control of an external glucose feed in cultivation with a glucose-free perfusion medium. The generated model was successfully applied to perform process control at 4 g/L and 1.5 g/L glucose over several days, respectively, with variability of ±0.4 g/L. The results demonstrate the high potential of Raman spectroscopy for advanced process monitoring and control of a perfusion process with a bioreactor and scale-independent measurement method.
... The raw Raman spectra were processed to obtain the contributions of the E 1 2g and A 1g modes. First, the fluorescence baseline was removed using an asymmetric least squares smoothing algorithm [39]. Figure 3a shows a typical Raman spectrum (after subtracting the fluorescence background) of few-layered 2H-TaS 2 flakes. ...
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The use of simple, fast and economic experimental tools to characterize low-dimensional materials is an important step in the process of democratizing the use of such materials in laboratories around the world. Raman spectroscopy has arisen as a way of indirectly determining the thickness of nanolayers of transition metal dichalcogenides (TMDs), avoiding the use of more expensive tools such as atomic force microscopy, and it is therefore a widely used technique in the study of semiconducting TMDs. However, the study of many metallic TMDs in the limit of few atomic layers is still behind when compared to their semiconducting counterparts, partly due to the lack of similar alternative characterization studies. In this work we present the characterization of the Raman spectrum, specifically of the E$^1_{2g}$- and A$_{1g}$-modes, of mechanically exfoliated crystals of Ta$_{1-x}$Mo$_x$S$_2$, a metallic TMD which exhibits charge density wave formation and superconductivity. The clear identification of contributions to the Raman spectrum coming from the SiO$_2$/Si substrate, which overlap with the peaks coming from the sample, and which dominate in intensity in the few-layer-samples limit, allowed the isolation of the individual E$^1_{2g}$- and A$_{1g}$-modes of the samples and, for the first time, the observation of a clear evolution of the Raman shifts of both modes as a function of sample thickness. The evolution of such peaks qualitatively resembles the evolution seen in other TMDs, and provide a way of indirectly determining sample thickness in the limit of few atomic layers at a low cost. In addition, we observe a softening (red-shift) of both E$^1_{2g}$- and A$_{1g}$-modes with Mo-doping in the nanolayers, possibly related to the increased out-of-plane lattice parameter with respect to the pure compound.
... The spectra were smoothed using a Savitzky-Golay seven-point filter with order 0 and degree 3. Following this smoothing step, the background signals, which are mostly caused by the coverslip glass and surface impurities, were subtracted using a baseline correction method ALS. 33 Finally, the spectra were normalized using the Euclidean standard, which is calculated for a spectrum as the square root of the sum of all its squared pixel values. ...
Article
Raman spectroscopy is a non-destructive and label-free molecular identification technique capable of producing highly specific spectra with various bands correlated to molecular structure. Moreover, the enhanced detection sensitivity offered by Surface-Enhanced Raman spectroscopy (SERS) allows analyzing mixtures of related chemical species in a relatively short measurement time. Combining SERS with deep learning algorithms allows in some cases to increase detection and classification capabilities even further. The present study evaluates the potential of applying deep learning algorithms to SERS spectroscopy to differentiate and classify different species of bile acids, a large family of molecules with low Raman cross sections and molecular structures that often differ by a single hydroxyl group. Moreover, the study of these molecules is of interest for the medical community since they have distinct pathological roles and are currently viewed as potential markers of gut microbiome imbalances. A Convolutional Neural Network (CNN) model was developed and used to classify SERS spectra from five bile acid species. The model succeeded in identifying the five analytes despite very similar molecular structures and was found to be reliable even at low analyte concentrations.
... El método de PCA fue implementado después de aplicar el proceso de normalización a 61 espectros constituyendo lo que se llama matriz de datos, utilizando los algoritmos desarrollados por Paul y Hans [27]. En este método primeramente se obtiene la matriz de covarianza y finalmente las componentes principales. ...
... El método de PCA fue implementado después de aplicar el proceso de normalización a 61 espectros constituyendo lo que se llama matriz de datos, utilizando los algoritmos desarrollados por Paul y Hans [27]. En este método primeramente se obtiene la matriz de covarianza y finalmente las componentes principales. ...
Thesis
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En nuestro actual entorno econ´omico-financiero cada vez m´as globalizado est´an jugando un papel m´as trascendental el comportamiento de las distintas bolsas de valores en el mundo. Por tal motivo, desde hace ya varias d´ecadas se han desarrollado diferentes metodolog´ıas encaminadas a comprender e intentar predecir el comportamiento burs´atil. Un total de 61 bolsas de valores de diferentes pa´ıses fueron monitoreadas a lo largo de septiembre de 2009 a agosto de 2010. En este trabajo de tesis se propone analizar el comportamiento accionario de las bolsas, discriminando por m´etodos estad´ısticos multivariados como el An´alisis de Componentes Principales (PCA), aplicado a los datos de los ´ındices burs´atiles de las principales bolsas en el mundo. Posteriormente, aplicando los m´etodos de An´alisis Discriminante Lineal (LDA) y Jerarqu´ıa de Cluster (HCA) se reafirmaron los agrupamientos o clusters obtenidos mediante el m´etodo de PCA. Los programas fueron ejecutados en una PC Dell con procesador Core2 Duo en la plataforma de MatLab. Estos resultados muestran la existencia de cuatro grupos importantes de bolsas de valores, indicando claramente la relaci´on existente entre algunas de ellas, as´ı por ejemplo, una relaci´on obvia conocida es la existente entre la bolsa mexicana y la americana indicada por su localizaci´on en el mismo cluster o bien por su estrecha cercan´ıa en el PCA. Adem´as los resultados est´an en perfecto acuerdo con lo observado en el sector econ´omico�financiero mundial actual en referencia a la situaci´on econ´omica de pa´ıses como Grecia, Italia, Espa˜na y Portugal. La metodolog´ıa aqu´ı propuesta ofrece resultados confiables por lo que en un futuro podr´ıa ser utilizada para asesor´ıas financieras y/o toma de decisiones de personas que invierten en la bolsa de valores.
Article
Bone is a biological tissue with unique mechanical properties, owing to a complex hierarchical structure ranging from the nanoscale up to the macroscale. To better understand bone mechanics, investigation of mechanical properties of all structural elements at every hierarchical level and how they interact is necessary. Testing of bone structures at the lower microscale, e.g. bone lamellae has been least performed and remains a challenge. Focused ion beam (FIB) milling is an attractive technique for machining microscopic samples from bone material and performing mechanical testing at the microscale using atomic force microscopy (AFM) and nanoindentation setups. So far, reported studies have been performed on bone samples of animal origin, mostly in a dehydrated state, except for one study. Here we present an AFM-based microbeam bending method for performing bending measurement in both dehydrated and rehydrated conditions at the microscale. Single lamella bone microbeams of four human donors, aged 65–94 y, were machined via FIB and tested both in air and fully submerged in Hank's Balanced Salt Solution (HBSS) to investigate the effect of dehydration and to a certain extent, age on bone mechanics. Bending moduli obtained were found to reduce up to 5 times after 2 h of rehydration and no trend of change in bending moduli with respect to age could be observed. Mechanical behavior changed from almost purely elastic to viscoelastic upon rehydration and a trend of lower dissipated energy of older samples could be observed, but only in the rehydrated state. These results confirm directly the importance of water for the mechanical properties of bone tissue. Moreover, the possible trend of lower capability of energy dissipation in older donors could have implications in decrease of fracture toughness and thus increase in bone fragility with age.
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Filamentous cable bacteria display long-range electron transport, generating electrical currents over centimeter distances through a highly ordered network of fibers embedded in their cell envelope. The conductivity of these periplasmic wires is exceptionally high for a biological material, but their chemical structure and underlying electron transport mechanism remain unresolved. Here, we combine high-resolution microscopy, spectroscopy, and chemical imaging on individual cable bacterium filaments to demonstrate that the periplasmic wires consist of a conductive protein core surrounded by an insulating protein shell layer. The core proteins contain a sulfur-ligated nickel cofactor, and conductivity decreases when nickel is oxidized or selectively removed. The involvement of nickel as the active metal in biological conduction is remarkable, and suggests a hitherto unknown form of electron transport that enables efficient conduction in centimeter-long protein structures. Filamentous cable bacteria conduct electrical currents over centimeter distances through fibers embedded in their cell envelope. Here, Boschker et al. show that the fibers consist of a conductive core containing nickel proteins that is surrounded by an insulating protein shell.
Article
Residues of veterinary antibiotics in honey may be damaging to human health. Surface‐enhanced Raman scattering spectroscopy (SERS) is an emerging technology widely applied in food safety. SERS has advantages of enabling fingerprint identification and fast detection, as well as does not require complex pretreatment. Considering the overuse of nitrofurans in honeybee breeding, SERS combined with spectral preprocessing was used to detect nitrofurantoin in honey. By using standardized experimental procedures and improved spectral correction methods, the lowest detection limit of nitrofurantoin was 0.1321 mg/kg. A good linear relationship in the partial least squares regression model was found among spiked samples, which allowed prediction of nitrofurantoin content in honey sample (RC2$R_C^2$= 0.9744; RP2$R_P^2$= 0.976; RMSECV = 1.0353 mg/kg; RMSEP = 0.9987 mg/kg). Collectively, these results reliably demonstrated that quantification is more accurate when spectral preprocessing is better controlled. Therefore, this study indicates that SERS could be further implemented in fast and onsite detection of nitrofurantoin in honey for improved food safety. This article presents a novel SERS‐based method for the rapid detection of nitrofurantoin residues in honey. The original spectra were corrected by multiple linear regression based on the fitting baseline. This study aims to develop a rapid onsite detection method for toxic hazardous substance residues in food.
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Alpha-synucleinopathies are featured by fibrillar inclusions in brain cells. Although α-synuclein fibrils display structural diversity, the origin of this diversity is not fully understood. We used molecular dynamics simulations to design synthetic peptides, based on the NAC 71-82 amino acid fragment of α-synuclein, that govern protofilament contacts and generation of twisted fibrillar polymorphs. Four peptides with structures based on either single or double fragments and capped or non-capped ends were selected for further analysis. We determined the fibrillar yield and the structures from these peptides found in the solution after fibrillisation using protein concentration determination assay and circular dichroism spectroscopy. In addition, we characterised secondary structures formed by individual fibrillar complexes using laser-tweezers Raman spectroscopy. Results suggest less mature fibrils, based on the lower relative β-sheet content for double- than single-fragment peptide fibrils. We confirmed this structural difference by TEM analysis which revealed, in addition to short protofibrils, more elongated, twisted and rod-like fibril structures in non-capped and capped double-fragment peptide systems, respectively. Finally, time-correlated single-photon counting demonstrated a difference in the Thioflavin T fluorescence lifetime profiles upon fibril binding. It could be proposed that this difference originated from morphological differences in the fibril samples. Altogether, these results highlight the potential of using peptide models for the generation of fibrils that share morphological features relevant for disease, e.g., twisted and rod-like polymorphs.
Article
Process monitoring of product quality attributes using spectroscopic tools is a prerequisite to implementing process analytical technology (PAT) to achieve consistency in process performance and product quality. Raman spectroscopy is an established tool for real-time monitoring of product quality attributes during pharmaceutical manufacturing. This paper proposes real-time monitoring of atomic layer deposition (ALD) coating on an active pharmaceutical ingredient (API) using Raman spectroscopy. Palbociclib, a cytotoxic drug for treating advanced breast cancer, is used to develop the model. Product quality attributes such as homogeneity, surface modifications, and dissolution profile were measured using Raman spectroscopy, X-ray diffraction, and dissolution testing, respectively. The principal least squares (PLS) model developed had a cumulative coefficient of determination of 0.988. The proposed approach has been demonstrated to accurately measure the number of ALD coating cycles and identify the type of metal oxide coating. Hence, it can offer an effective method for online monitoring of coating during ALD.
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Organoids play an increasingly important role as in vitro models for studying organ development, disease mechanisms, and drug discovery. Organoids are self-organizing, organ-like three-dimensional (3D) cell cultures developing organ-specific cell types and functions. Recently, three groups independently developed self-assembling human heart organoids (hHOs) from human pluripotent stem cells (hPSCs). In this study, we utilized a customized spectral-domain optical coherence tomography (SD-OCT) system to characterize the growth of hHOs. Development of chamber structures and beating patterns of the hHOs were observed via OCT and calcium imaging. We demonstrated the capability of OCT to produce 3D images in a fast, label-free, and non-destructive manner. The hHOs formed cavities of various sizes, and complex interconnections were observed as early as on day 4 of differentiation. The hHOs models and the OCT imaging system showed promising insights as an in vitro platform for investigating heart development and disease mechanisms.
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Here we introduce PRISMA, an open‐source tool to rapidly analyse large numbers of spectra into meaningful spectroscopic trends. PRISMA follows a human‐in‐the‐loop workflow, where the user interacts with an intuitive graphical user interface to control multiple steps in the spectrum analysis process: trimming, baseline correction, and peak fitting. The tool outputs the results in an easy‐to‐read csv format within seconds. We describe the functionalities implemented in PRISMA and test its capabilities with three experimental cases relevant to the study of Electrochemical energy storage and conversion devices: a temperature‐dependent Raman measurement of phase transitions, a linear Raman mapping of a graphite composite electrode, and an operando X‐ray diffraction experiment of lithium nickel oxide composite battery electrode. The case studies demonstrate the robustness of the app and its ability to unearth insightful trends in peak parameters, which are easier to represent, interpret and further analyse with more advanced techniques. An open source app to visualize and process hundreds of spectra from in situ, operando and unrelated experiments. The app implements methods for baseline correction and peak fitting, and a friendly graphical user interface. Users load spectra (or diffraction patterns), tune baseline and peak fitting parameters, run a high‐throughput processing step and export the results in a csv format – all within minutes. PRISMA enables extracting spectroscopic trends that characterize the properties and phenomena inherent to the operation of functional materials.
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The aluminium Twitch fraction of a Belgian recycling facility could be further sorted by implementing Laser-Induced Breakdown Spectroscopy (LIBS). To achieve this goal, the presented research identifies commercially interesting output fractions and investigates machine learning methods to classify the post-consumer aluminium scrap samples based on the spectral data collected by the LIBS sensor for 834 aluminium scrap pieces. The classification performance is assessed with X-Ray Fluorescence (XRF) reference measurements of the investigated aluminium samples, and expressed in terms of accuracy, precision, recall, and f1 score. Finally, the influence of misclassifications on the composition of the desired output fractions is evaluated.
Conference Paper
Eye blink is indicative of various mental states. Generally, vision based approaches are used for detecting eye blinks. However, performance of such approaches varies across participants. Standard eye tracker or eye glasses used for detecting blinks, are very costly. Here, we are proposing a personalized vision based eye blink detector system. Proposed approach is ubiquitous and unobtrusive in nature and can be implemented using standard webcams/mobile camera, making it deployable for real world scenarios. Our approach has been validated on a set of data collected from our lab and on an open data set. Results show that in both cases, our system performs well for various conditions like natural/artificial light, with or without spectacles. We achieved a Fscore of 0.98 for own collected data and 0.91 for open dataset, which outperform state of the art approaches.
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Accurate and precise measurement of the relative protein content of blood-based samples using mass spectrometry is challenging due to the large number of circulating proteins and the dynamic range of their abundances. Traditional spectral processing methods often struggle with accurately detecting overlapping peaks that are observed in these samples. In this work, we develop a novel spectral processing algorithm that effectively detects over 1650 peaks with over 3.5 orders of magnitude in intensity in the 3 to 30 kD m/z range. The algorithm utilizes a convolution of the peak shape to enhance peak detection, and accurate peak fitting to provide highly reproducible relative abundance estimates for both isolated peaks and overlapping peaks. We demonstrate a substantial increase in the reproducibility of the measurements of relative protein abundance when comparing this processing method to a traditional processing method for sample sets run on multiple matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) instruments. By utilizing protein set enrichment analysis, we find a sizable increase in the number of features associated with biological processes compared to previously reported results. The new processing method could be very beneficial when developing high-performance molecular diagnostic tests in disease indications.
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As an alternative to beam-based additive manufacturing, 3D ink-extrusion additive manufacturing is studied here for thermoelectric Bi2Te3, starting from Bi2O3+TeO2 oxide precursor powders. In situ synchrotron XRD in flowing H2 at elevated temperatures reveals the complex phase evolution upon co-reduction and sluggish reduction kinetics leading to the formation of Bi2Te3, Bi2TeO5 and Bi2TeO2. Sintering trials performed using optimal temperatures identified by in situ XRD show that low heating rates and extensive holding times are required to achieve full co-reduction to pure Bi2Te3. The formation of liquid Bi at the temperatures required for oxide reduction leads to local transient-liquid-phase sintering, creating a coarse-grained porous structure. To limit the amount of free Bi, compositional homogenization prior to reduction is achieved by oxide pre-sintering in air, allowing to access the high temperatures required for interdiffusion. After co-reduction of the complex Bi2Te4O11+Bi2Te2O7+TeO2 ceramic, fine-grained, Te-rich n-type Bi2Te3 is obtained, achieving a zT of ∼0.4 between 373 and 423 K. The present study demonstrates the feasibility of synthesizing thermoelectrics from oxide precursors, with the potential of simplifying the processing chain and reducing cost to obtain 3D-extruded thermoelectric parts with complex shapes and architectures.
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This work presents a multisensor hyperspectral approach for the characterization of ultramarine blue, a valuable historical pigment, at the microscopic scale combining the information of four analytical techniques at the elemental and molecular levels. The hyperspectral images collected were combined in a single hypercube, where the pixels of the various spectral components are aligned on top of each other. Selected spectral descriptors have been defined to reduce data dimensionality before applying unsupervised chemometric data analysis approaches. Lazurite, responsible for the blue color of the pigment, was detected as the major mineral phase present in synthetic and good quality pigments. Impurities like pyrite were detected in lower quality samples, although the clear identification of other mineral phases with silicate basis was more difficult. There is no correlation between the spatial distribution of the bands arising in the Raman spectra of natural samples in the region 1200–1850 cm ⁻¹ and any of the transition metals or rare earth elements (REE). With this information, the previous hypothesis (based on bulk analysis) attributing these bands to luminescence emissions due to impurities of these elements must be revised. We propose the consideration of CO 2 molecules trapped in the cages of the aluminosilicate structure of sodalite-type. Additionally, correlation between certain Raman features and the combined presence of Ca, P, and REE, in particular Nd, was detected for the lowest quality pigment. Our results highlight the usefulness of fusing chemical images obtained via different imaging techniques to obtain relevant information on chemical structure and properties.
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Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique capable of increasing the Raman signal of an analyte using specific nanostructures. The close contact between those nanostructures, usually a suspension of nanoparticles, and the molecule of interest produces an important exaltation of the intensity of the Raman signal. Even if the exaltation leads to an improvement of Raman spectroscopy sensitivity, the complexity of the SERS signal and the numbers of parameters to be controlled allow the use of SERS for detection rather than quantification. The aim of this study was to develop a robust discriminative and quantitative analysis in accordance with pharmaceutical standards. In this present work, we develop a discriminative and quantitative analysis based on the previous optimized parameters obtained by the design of experiments fixed for norepinephrine (NOR) and extended to epinephrine (EPI) which are two neurotransmitters with very similar structures. Studying the short evolution of the Raman signal intensity over time coupled with chemometric tools allowed the identification of outliers and their removal from the data set. The discriminant analysis showed an excellent separation of EPI and NOR. The comparative analysis of the data showed the superiority of the multivariate analysis after logarithmic transformation. The quantitative analysis allowed the development of robust quantification models from several gold nanoparticle batches with limits of quantification of 32 µg/mL for NOR and below 20 µg/mL for EPI even though no Raman signal is observable for such concentrations. This study improves SERS analysis over ultrasensitive detection for discrimination and quantification using a handheld Raman spectrometer.Graphical abstract
Article
Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning–based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications. Raman spectroscopy is increasingly being used in biological assays and studies. This protocol provides guidance for performing chemometric analysis to detect and extract information relating to the chemical differences between biological samples.
Preprint
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Placental extracellular vesicles (EVs) play an essential role in pregnancy by protecting and transporting diverse biomolecules that aid in fetomaternal communication. However, in preeclampsia, they have also been implicated in contributing to disease progression. Despite their potential clinical value, most current technologies cannot provide a rapid and effective means of differentiating between healthy and diseased placental EVs. To address this, we developed a fabrication process called laser-induced nanostructuring of SERS-active thin films (LINST), which produces nanoplasmonic substrates that provide exceptional Raman signal enhancement and allow the biochemical fingerprinting of EVs. After validating LINST performance with chemical standards, we used placental EVs from tissue explant cultures and demonstrated that preeclamptic and normotensive placental EVs have classifiably distinct Raman spectra following the application of both conventional and advanced machine learning algorithms. Given the abundance of placental EVs in maternal circulation, these findings will encourage immediate exploration of surface-enhanced Raman spectroscopy (SERS) as a promising method for preeclampsia liquid biopsies, while our novel fabrication process can provide a versatile and scalable substrate for many other SERS applications.
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In the evaluation and development of lignocellulosic biomass pyrolysis chemical kinetics, one of the most uncertain aspects in modelling is the volatile species composition. This work proposes an in situ novel methodology to evaluate the kinetics of cellulose pyrolysis considering the representative functional groups of gas and volatile compounds produced during this process. This methodology (named IRKM) was based on the temporal monitoring of infrared absorption spectra for characteristic functional groups in volatile products and comparation with Ranzi et al. kinetic model, that describes the pyrolysis process for cellulose. Temperature range for cellulose pyrolysis (570-670 K), temperature in maximum decomposition (616 K) and activation energies and pre-exponential factors in reaction products determined by this new methodology and by thermogravimetry and gas chromatography matched with differences less than 5%. This new methodology could be applied to measure temperature ranges for the production of volatiles and kinetics constants in another lignocellulosic biomass or using other kinetic models.
Article
In this paper, the problem of estimating the background of a spectrum is addressed. We propose to fit this background to a low-order polynomial, but rather than determining the polynomial parameters that minimise a least-squares criterion (i.e. a quadratic cost function), non-quadratic cost functions well adapted to the problem are proposed. To minimise these cost functions, we use the half-quadratic minimisation. It yields a fast and simple method, which can be applied to a wide range of spectroscopic signal. Guidelines for the choice of the design parameters are given and illustrated on simulated spectra. Finally, the effectiveness of the method is shown by processing experimental infrared and Raman spectra.
Article
A technique entitled robust baseline estimation is introduced, which uses techniques of robust local regression to estimate baselines in spectra that consist of sharp features superimposed upon a continuous, slowly varying baseline. The technique is applied to synthetic spectra, to evaluate its capabilities, and to laser-induced fluorescence spectra of OH (produced from the reaction of ozone with hydrogen atoms). The latter example is a particularly challenging case for baseline estimation because the experimental noise varies as a function of frequency.
Article
To increase the power and the robustness of spectroscopic process analyzers, methods are needed that suppress the spectral variation that is not related to the property of interest in the process stream. An approach for the selection of a suitable method is presented. The approach uses the net analyte signal (NAS) to analyze the situation and to select methods to suppress the nonrelevant spectral variation. The empirically determined signal-to-noise of the NAS is used as a figure of merit. The advantages of the approach are (i). that the error of the reference method does not affect method selection and (ii). that only a few spectral measurements are needed. A diagnostic plot is proposed that guides the user in the evaluation of the particular suppression method. As an example, NIR spectroscopic monitoring of a mol-sieve separation process is used.
  • H F M Boelens
  • P H C Eilers
Boelens, H.F.M. ; Eilers, P.H.C. ; Hankemeier Th. Anal. Chem. 2005, (in press)
  • A Jirasek
  • G Schulze
  • M M L Yu
  • M W Blades
  • R F B Turner
Jirasek, A. ; Schulze G. ; Yu, M.M.L. ; Blades, M.W. ; Turner, R.F.B. Appl. Spectrosc. 2004, 58, 1488-1499
  • M Mazet
  • C Carteret
  • D Brie
  • J Idier
  • B Humbert
Mazet, M. ; Carteret, C. ; Brie, D. ; Idier, J. ; Humbert, B. Chemom. Intell. Lab. Syst. 2005, 76, 121-133
  • D A Mcnulty
  • H J H Macfie
McNulty, D.A. ; MacFie, H.J.H. J. of Chemom. 1997, 11, 1-11
  • P H C Eilers
  • R X De Menezes
Eilers, P.H.C. ; de Menezes, R.X. Bioinformatics, 2005, 21, 1146-1153
  • M A Knee
  • H J Annegarn
Knee, M.A. and Annegarn H.J. Nucl. Instrum. Methods Phys. Rese. Sect. B, 1996, 109, 201-213
  • A J Phillips
  • P Hamilton
Phillips A.J. ; Hamilton P.A Anal. Chem. 1996, 68, 4020-4025
  • B Bogaert
  • H F M Boelens
  • H C Smit
Bogaert, B. ; Boelens, H.F.M. ; Smit, H.C. Anal. Chim. Acta 1993, 274, 71-85
  • P H C Eilers
Eilers, P.H.C. Anal. Chem. 2004, 76, 404-411
  • V P Andreev
  • T Rejtar
  • H Chen
  • E V Mosovets
  • A R Ivanov
  • B L Karger
Andreev, V.P. ; Rejtar, T. ; Chen, H. ; Mosovets, E.V. ; Ivanov A.R. ; Karger B.L. Anal. Chem. 2003, 75, 6314-6326
  • P H C Eilers
Eilers, P.H.C. Anal. Chem. 2003, 75, 3631-3636