Peak Alignment Research Articles

Introducing a novel procedure for peak alignment in one-dimensional 1H-NMR spectroscopy: a prerequisite for chemometric analyses of wine samples.

Shifted peak positions in 1H-nuclear magnetic resonance (1H-NMR) spectroscopy of wine samples are inevitably occurring mainly due to variations in the sample matrix, which consists of ethanol, glycerol, carbohydrates, acids, phenolic compounds, minerals, and aroma compounds. Slight variations in pH during sample preparation or fluctuations in instrumental factors may contribute to shifted peak positions that need to be corrected before subjecting the NMR data to chemometric techniques to ensure samples are compared on the correct chemical shift scale. In the current work, a novel procedure for correcting 1H-NMR spectroscopy peak positions was developed by mapping of the raw NMR spectra on a common chemical shift axis using a simple interpolation approach. The mapping allowed a substantial correction of peak positions and subsequently reduced the computational burden in further spectral processing. Fine-tuning of the peak alignments was carried out efficiently by interval-wisely applying the correlation optimized warping (COW) algorithm. Our preceding mapping approach enabled the use of substantially simpler alignment parameters of the COW algorithm, thereby accelerating the whole peak alignment process. The developed procedure may also be suitable for facilitating NMR analyses of other sample types, such as agricultural, clinical or pharmaceutical samples in targeted or untargeted analytical approaches.

Peak and Valley Alignment of the Anaconda Proximal Saddle-shaped Rings After Endovascular Aortic Aneurysm Repair: Implications for Device Positioning and Neck Length

Peak and Valley Alignment of the Anaconda Proximal Saddle-shaped Rings After Endovascular Aortic Aneurysm Repair: Implications for Device Positioning and Neck Length

Second language acquisition of English intonation: Nuclear peak alignment

Second language acquisition of English intonation: Nuclear peak alignment

Second language acquisition of English intonation: Nuclear peak alignment

Second language acquisition of English intonation: Nuclear peak alignment

High-Resolution Reconstruction for Diffusion-Ordered NMR Spectroscopy.

Diffusion-ordered NMR spectroscopy (DOSY) presents an essential tool for the analysis of compound mixtures by revealing intrinsic diffusion behaviors of mixed components. The applicability of DOSY measurements on complex mixtures is generally limited by the performance of data reconstruction algorithms. Here, based on constraints on low rank and sparsity of DOSY data, we propose a reconstruction method to achieve high-resolution DOSY spectra with excellent peak alignments and accurate diffusion coefficients for measurements of complex mixtures even when component signals are congested and mixed together along the spectral dimension. This proposed method is robust and suitable for DOSY data acquired from common commercial NMR instruments; thus, it may broaden the scope of DOSY applications.

The Presence of a High Peak Feature Within Low-Average Shear Stimuli Induces Quiescence in Venous Endothelial Cells.

Wall shear stress (WSS) is an important stimulus in vascular remodelling and vascular lesion development. The current methods to assess and predict the risk associated with specific unsteady WSS consider the WSS mean values or the presence of reverse phases described by the oscillatory shear index. Recent evidence has shown that the accuracy of these methods is limited, especially with respect to the venous environment. Unsteady WSS are characterised by several features that may individually affect endothelial cells. Consequently, we assessed the effects of averaged WSS (TAWSS), temporal WSS gradient (TWSSG), maximum WSS (WSS peak) and reverse phase (OSI) by applying different WSS profiles to venous EC in-vitro, using a real-time controlled cone-and-plate cell-shearing device for 24 h. We found that TWSSG and WSS peak affect cell elongation and alignment respectively. We also found that the WSS waveforms with a peak of 1.5 Pa or higher significantly correlate with the induction of a protective phenotype. Cell phenotype induced by these high peak waveforms does not correlate to what is predicted by the hemodynamic indices currently used. The definition of reliable hemodynamic indices can be used to inform the computational models aimed at estimating the hemodynamic effects on vascular remodelling.

Diagnostic and Prognosis of Li-Ion Battery State-of-Health Based on Electrodes Potentials Shifts

The degradation mechanisms affecting each electrode of a Li-ion cell are physical and chemical processes widely depicted in the literature [1-4] They lead to the battery loss of performance in terms of energy availability (capacity decay) and power capability (resistance increase). To diagnose the electrode degradation and cell available capacity, many models have been designed [5-8]. These models provide battery state-of-heath (SOH) indicators by simulating various scenarios of degradation modes. In this work, we want to go further and present a new solution not only able to estimate the battery SOH, but also to predict the evolution of the battery available capacity over the next couple of months. This diagnosis/prognosis solution is a key-enabling technology to ensure the storage system reliability of future electric cars. In our approach, each electrode open circuit potential (OCP) is used to rebuild the cell voltage at low rate. Only three parameters (fig.1) are used to define the cell voltage curve: the positive electrode capacity (Cpos), the negative electrode capacity (Cneg), and the offset (OFS) which represents the shift between both electrode OCPs. The cell capacity is directly related to these three parameters and depends on the voltage range applied during cycling. To diagnose the cell degradations, a two-step method was proposed (fig.2). First, we fitted the experimental voltage of the cell measured at low rate of discharge (C/10) with the open circuit potential (OCP) of the negative and positive electrodes to identify the values of each of the three parameters (Cneg, Cpos, OFS). Then, we finely tune the identified parameter values from differential voltage curves by ensuring proper peak alignment. The capacity is therefore derived capacity from the final values of the three parameters. This method has been applied on a graphite/LMO-NMC cell for various calendar and cycling ageing conditions, with temperatures ranging from 0 to 60 °C, under low and high discharge rates (C/3, 1C), and different state-of-charge (0 to 100 %). As expected, calendar and cycling conditions have different impacts on the cell ageing and therefore on the electrodes ageing. Figure 3 shows a good agreement between experimental and simulated capacities when identifying the three parameters while Figure 4 shows the evolution of the three parameters (Cneg, Cpos, OFS) during calendar and cycling ageing. These three parameters evolution are path dependent of the ageing test conditions. For the cell SOH prognosis, we propose a novel empirical ageing model predicting the electrode parameter evolution over the time. It is worth noticing that the Palmgren-Miner rule [9] allows us to cumulate calendar and cycling ageing effects for the total loss of three electrodes parameters in our aging model.

Peak alignment of gas chromatography–mass spectrometry data with deep learning

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC–MS) data. In GC–MS data, a compound’s retention time (RT) may not stay fixed across multiple chromatograms. To use GC–MS data for biomarker discovery requires alignment of identical analyte’s RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC–MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC–MS data sets of various complexities and analysed the alignment results quantitatively. We show the model has very good performance (AUC ∼ 1 for simple data sets and AUC ∼ 0.85 for very complex data sets). Further, our model easily outperforms existing algorithms on complex data sets. Compared with existing methods, ChromAlignNet is very easy to use as it requires no user input of reference chromatograms and parameters. This method can easily be adapted to other similar data such as those from liquid chromatography. The source code is written in Python and available online.

Analysis of Scotch Whisky by 1H NMR and chemometrics yields insight into its complex chemistry

Scotch Whisky has been analysed as a complex mixture in its raw form using high resolution Nuclear Magnetic Resonance (NMR) and previously developed water and ethanol suppression techniques. This has allowed for the positive identification of 25 compounds in Scotch Whisky by means of comparison to reference standards, spike-in experiments, and advanced 1D and 2D NMR experiments. Quantification of compounds was hindered by signal overlap, though peak alignment strategies were largely successful. Statistical total correlation spectroscopy (STOCSY) yielded information on signals arising from the same compound or compounds of similar origin. Statistical analysis of the spectra was performed using Independent and Principal Components Analysis (ICA, PCA) as well as Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Several whisky production parameters were successfully modelled, including blend or malt status, use of peated malt, alcohol strength, generic authentication and maturation wood type, whilst age and geographical origin could not be modelled.

Statistical analysis of MCC-IMS data for two group comparisons—an exemplary study on two devices

The Multi-capillary-column-Ion-mobility-spectrometry (MCC-IMS) technology for measuring breath gas can be used for distinguishing between healthy and diseased subjects or between different types of diseases. The statistical methods for classifying the corresponding breath samples typically neglects potential confounding clinical and technical variables, reducing both accuracy and generalizability of the results. Especially measuring samples on different technical devices can heavily influence the results. We conducted a controlled breath gas study including 49 healthy volunteers to evaluate the effect of the variables sex, smoking habits and technical device. Every person was measured twice, once before and once after consuming a glass of orange juice. The two measurements were obtained on two different devices. The evaluation of the MCC-IMS data regarding metabolite detection was performed once using the software VisualNow, which requires manual interaction, and once using the fully automated algorithm SGLTR-DBSCAN. We present statistical solutions, peak alignment and scaling, to adjust for the different devices. For the other potential confounders sex and smoking, in our study no significant influence was identified.

Mass Accuracy Check Using Common Background Peaks for Improving Metabolome Data Quality in Chemical Isotope Labeling LC-MS.

Chemical isotope labeling (CIL) LC-MS is a highly sensitive and quantitative method for metabolome analysis. Because of a large number of peaks detectable in a sample and the need of running many samples in a metabolomics project, any significant change in mass measurement accuracy during the whole period of running samples can adversely affect the downstream peak alignment and quantitative analysis. Herein, we report a rapid method to check the mass accuracy of individual spectra in each CIL LC-MS run in order to flag up any run containing spectra with accuracy drift that falls outside the expected error. The flagged run may be re-run or discarded before merging with other runs for peak alignment and analysis. This method is based on the observation that some background signals are commonly detected in almost all spectra collected in CIL LC-MS runs. A mass accuracy check (MAC) software program has been developed to first find the common background mass peaks and then use them as mass references to calculate any mass shifts over the course of multiple sample runs. Using a metabolome dataset of 324 human cerebrospinal fluid (CSF) samples and 35 quality control (QC) samples produced by CIL LC-MS, we show that this accuracy check method can streamline the initial raw data processing for downstream analysis in metabolomics.

Analysis of the fingerprint profile of bioactive constituents of traditional Chinese medicinal materials derived from animal bile using the HPLC–ELSD and chemometric methods: An application of a reference scaleplate

Traditional Chinese medicinal materials derived from animal bile are widely applied in clinical therapy for thousands of years in several Southeast Asian countries. Although the constituents are similar, these crude drugs exhibit different pharmacological activities; bile acids are the main bioactive constituent. Depending on the source, the price of these crude drugs differs significantly. Therefore, a reliable fingerprint method is needed to analyze and distinguish these crude drugs with a similar composition. In this milieu, we aimed to establish a fingerprint chromatography method that can separate and detect several bile acids simultaneously. A high-performance liquid chromatography separation method was established with evaporative light scattering detection to detect the analytes. The main bioactive constituents of pig bile, cattle bile, sheep bile, bear bile, and three types of cow bezoar were analyzed using the proposed method. The fingerprint chromatography profile of 35 samples were obtained and analyzed using chemometric methods. Considering the differences among samples, a reference scaleplate method was used in the peak alignment procedure. Unsupervised methods (hierarchical cluster analysis and principle component analysis) and supervised methods (K-nearest neighbor, partial least squares discriminant analysis, support vector machine discriminant analysis, and soft independent modeling of class analogy) were used in the chemometric analysis. The results indicated that the fingerprint chromatograms of the seven crude drugs had fingerprint specificity and that they can be well distinguished. In addition, the reference scaleplate method using the chromatogram of a mixed standard solution is practically applicable for peak alignment in the analysis of samples with a large difference in chromatographic peaks. Overall, 17 bile acids can be separated and detected simultaneously using this method and some frequently used TCMMs derived from animal bile can be distinguished accurately.

ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets

BackgroundThe analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each “feature” (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field.ResultsHere, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications.ConclusionsThe methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data.

Alternative approach of applying 1H NMR in conjunction with chemometrics for wine classification

1H NMR spectroscopy was applied for the classification of a wine set, according to geographical origin and variety. The originality of the proposed approach consisted in a single uniform pH adjustment to all wine samples, in order to reduce the pH variation range but allowing to keep unaltered the wine samples fingerprint, as well as a signal treatment limited to water suppression during acquisition. No further peak alignment was performed, neither through individual pH adjustment, nor with specific software. The wine classifications were made separately on four spectral areas after exclusion of ethanol signal regions. It was found that the spectral region having the highest discrimination potential for both geographical and varietal discrimination is represented by Area 1 (5.1–9.8 ppm). In this spectral range, the separation between samples coming from different geographical areas was realized in a 100% percentage, in both initial and cross-validation procedure. For simultaneous variety classification percentages better than 99% for initial classification and 97% for cross validation procedure were achieved. Besides, the high discrimination potential, another advantage of this approach is given by a simplified working protocol which consists in an easy, rapid and affordable preparation step, making it a good candidate for laboratories which are dealing with a high number of samples.

Application of differential mobility-mass spectrometry for untargeted human plasma metabolomic analysis.

Differential mobility spectrometry (DMS) has been gaining popularity in small molecule analysis over the last few years due to its selectivity towards a variety of isomeric compounds. While DMS has been utilized in targeted liquid chromatography-mass spectrometry (LC-MS), its use in untargeted discovery workflows has not been systematically explored. In this contribution, we propose a novel workflow for untargeted metabolomics based solely on DMS separation in a clinically relevant chronic kidney disease (CKD) patient population. We analyzed ten plasma samples from early- and late-stage CKD patients. Peak finding, alignment, and filtering steps performed on the DMS-MS data yielded a list of 881 metabolic features (unique mass-to-charge and migration time combinations). Differential analysis by CKD patient group revealed three main features of interest. One of them was putatively identified as bilirubin based on high-accuracy MS data and comparison of its optimum compensation voltage (COV) with that of an authentic standard. The DMS-MS analysis was four times faster than a typical HPLC-MS run, which suggests a potential for the utilization of this technique in screening studies. However, its lower separation efficiency and reduced signal intensity make it less suitable for low-abundant features. Fewer features were detected by the DMS-based platform compared with an HPLC-MS-based approach, but importantly, the two approaches resulted in different features. This indicates a high degree of orthogonality between HPLC- and DMS-based approaches and demonstrates the need for larger studies comparing the two techniques. The workflow described here can be adapted for other areas of metabolomics and has a value as a prescreening method to develop semi-targeted workflows and as a faster alternative to HPLC in large biomedical studies.

Chemometric analysis of Mesoamerican obsidian sources

We propose a combination of portable X-ray fluorescence (pXRF) and chemometrics to discriminate between Mesoamerican obsidian sources and to assign archaeological artifacts of unknown origin to their respective deposits using a procedure that does not require any type of calibration or reference standards. A set of 109 samples of known origin and a total of 257 samples of unknown origin were analyzed with a portable XRF spectrometer. The resultant spectra were used as spectral signatures for the chemometric data analysis. First, we applied spectral pre-treatment techniques, such as CluPA algorithm for peak alignment and the Savitzky-Golay and Extended Multiplicative Signal Correction for data smoothing and noise removal, combined with methods for the selection of a spectral range containing the variables with the most relevant information (iPLS and). The full spectrum of the obsidian samples was divided into 20 subintervals, fitting a local regression model (PLS) to each subinterval. The performance was evaluated by the Root Mean Square Error of Cross-Validation, the Root Mean Square Error of Prediction and the correlation coefficient. The selected spectral regions were then analyzed with ROBPCA algorithm for the discrimination of outliers and the projection of the observations in the PCA space. For the classification, we propose a robust procedure (RSIMCA) which is based on a ROBPCA method for high-dimensional data. The classification rules were obtained by using the orthogonal and the score distances, from which it is possible to distinguish samples that belong to a given group. Using the proposed methodology, we were able to provide evidence about which variables were meaningful for the classification and provided information about group membership or provenance. This approach proves to be a valid technique for the quantitative analysis of XRF spectra.

Analysis of energy and major components in chromatographic signals for the diagnosis of prostate cancer

The prostate exam is an early detection tool to prevent prostate cancer and the main diagnostic tools for obtaining signs are generally invasive. This article tries chromatographic signals from the urine of prostate cancer patients and control patients as a non-invasive examination proposal. For this purpose, methodologically, urine samples are taken, digitized in chromatograms, treated with mathematical techniques and classified. The mathematical techniques are time normalization, dead time elimination, baseline correction, noise elimination, and peak alignment. Classification techniques analyze energy, in the domain of time and frequency, and the main components in sedimentation graphs and scores. As a result, the chromatographic signal is characterized and identifies the characteristic curve that represents the signal of prostate cancer patients and control patients. The data structure shows a cluster distribution of 88.88% of the vectors for the control patients. In the case of prostate cancer patients, the distribution of data is in clusters around the area defined by control patients. This characterization demarcates signal classification regions to diagnose possible prostate cancer patients, validating the relationship between the chromatographic signal and cancer.

84: Metabolomic testing for gestational diabetes: utility & reproducibility

Glucose tolerance testing for gestational diabetes (GDM) has poor reproducibility between tests and high variability by time of day. In contrast, metabolomic testing of serum has good reproducibility for multiple metabolites, including those altered in GDM. We hypothesize that metabolomics can identify GDM and maintain reproducibility over 3-7 days. Prospective observational study of women with singleton gestation undergoing GDM screening at 24- 28 wks. Two fasting plasma samples were obtained 3-7 days apart. Women were excluded for major medical illness. Exposure groups were defined as glucose tolerant (GT), defined as 1-hour, 50-g glucose challenge test <120mg/dL, and GDM, defined by Carpenter Coustan criteria on a 3-hour, 100-g glucose tolerance test. We determined a priori that 35 subjects per group were needed to detect a mean difference of 1-standard deviation with an alpha of 0.01 and 90% power between GDM and GT. Aqueous extracts of plasma were analyzed by reverse-phase nanoLC-ESI- MS/MS on a SCIEX 5600 TripleTOF in the positive ion mode. Data processing: XCMSonline (peak alignment), MetaboAnalyst (statistics) and Mummichog (pathways). Partial least squared discriminant analysis (PLS-DA) was performed to detect differences in the four groups: GDM visit 1, GDM visit 2, GT visit 1, GT visit 2. Of 65 patients with 2 samples, 30 (46%) had GDM. Women with GDM were significantly more likely to be Hispanic; GT women were mostly black (p<0.01). GDM samples were obtained at a slightly later gestational age (27.5 vs 26.0 wks, p<0.001). Overall, distinct differences were seen between GT and GDM groups. Although there were some variation in metabolomics over a 3-7 day period, several analytes of interest that can be used to distinguish GDM from GT remained stable over a 3-7 day period (Figure 1). Random forest plots demonstrate the samples separating based on GDM, not visit. Pathway analysis demonstrated differences in ascorbate, amino acid, and glycerophospholipid metabolism between GDM and GT; these pathways were not identified in differences between visits. The metabolomics profile at 24-28 wks can distinguish GDM and is reproducible over a 3-7 day period. Further exploration of the specific metabolites and the possibility of using combinations of these metabolites to diagnose GDM should be explored further.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Comparison of simple monophasic versus classical biphasic extraction protocols for comprehensive UHPLC-MS/MS lipidomic analysis of Hela cells

In this study, two monophasic isopropanol-water mixtures (IPA:H2O 75:25 v/v and IPA:H2O 90:10 v/v) were compared with traditionally employed biphasic methods of Bligh & Dyer and Matyash et al. as extraction systems for lipidomics analysis in Hela cells. Samples were analyzed by UHPLC-ESI-QTOF-MS/MS in positive and negative mode using sequential window acquisition of all theoretical fragment ion spectra (SWATH) and a relatively new software (MS-DIAL) was employed for the processing of the data which includes detection of peaks, MS/MS spectra deconvolution, identification of detected lipids and alignment of peaks through the analyzed samples.The studied performance parameters such as precision, recoveries of isotopically labeled internal standards and endogenous lipids, number of extracted lipids, and complexity of employed procedure showed that extraction with IPA:H2O 90:10 v/v performs similar to the Matyash protocol and better than Bligh & Dyer as well as IPA:H2O 75:25 v/v. However, less complex monophasic protocol which is simpler to implement and can be executed in plastic rather than glass, make the monophasic IPA:H2O 90:10 v/v protocol an excellent alternative to the classical biphasic protocols for reversed phase LC-MS lipidomics studies.

Pre-treatment of soil X-ray powder diffraction data for cluster analysis

X-ray powder diffraction (XRPD) is widely applied for the qualitative and quantitative analysis of soil mineralogy. In recent years, high-throughput XRPD has resulted in soil XRPD datasets containing thousands of samples. The efforts required for conventional approaches of soil XRPD data analysis are currently restrictive for such large data sets, resulting in a need for computational methods that can aid in defining soil property – soil mineralogy relationships. Cluster analysis of soil XRPD data represents a rapid method for grouping data into discrete classes based on mineralogical similarities, and thus allows for sets of mineralogically distinct soils to be defined and investigated in greater detail. Effective cluster analysis requires minimisation of sample-independent variation and maximisation of sample-dependent variation, which entails pre-treatment of XRPD data in order to correct for common aberrations associated with data collection.A 24 factorial design was used to investigate the most effective data pre-treatment protocol for the cluster analysis of XRPD data from 12 African soils, each analysed once by five different personnel. Sample-independent effects of displacement error, noise and signal intensity variation were pre-treated using peak alignment, binning and scaling, respectively. The sample-dependent effect of strongly diffracting minerals overwhelming the signal of weakly diffracting minerals was pre-treated using a square-root transformation. Without pre-treatment, the 60 XRPD measurements failed to provide informative clusters. Pre-treatment via peak alignment, square-root transformation, and scaling each resulted in significantly improved partitioning of the groups (p < 0.05). Data pre-treatment via binning reduced the computational demands of cluster analysis, but did not significantly affect the partitioning (p > 0.1). Applying all four pre-treatments proved to be the most suitable protocol for both non-hierarchical and hierarchical cluster analysis. Deducing such a protocol is considered a prerequisite to the wider application of cluster analysis in exploring soil property – soil mineralogy relationships in larger datasets.