Leveraging probabilistic peak detection to estimate baseline drift in complex chromatographic samples

A Pilot Proteomic Study of Normal Human Tears: Leptin as a Potential Biomarker of Metabolic Disorders

The concentrations of insulin, leptin, active ghrelin, C-peptide and gastric inhibitory polypeptide (GIP) and their inter-day variations were examined in normal human tears. In addition, correlations between the concentrations of these metabolic proteins and ocular surface parameters were determined. Subjects with healthy ocular surfaces attended three visits, with 7-day intervals. Tear evaporation rate (TER) and non-invasive tear break-up time (NITBUT) were assessed, and a total of 2 µL tears were collected from all subjects. Tear fluid concentrations of insulin, leptin, active ghrelin, C-peptide and GIP were measured by multiplex bead analysis. Insulin was the most highly expressed metabolic protein, followed by leptin, C-peptide, active ghrelin and GIP. Of these, only active ghrelin had a significant inter-day variation (p < 0.05). There was no inter-day variation in the mean concentrations of the other metabolic proteins. Leptin had a strong intra-class reproducibility. No correlation was detected between tear metabolic protein concentrations and ocular surface parameters. This pilot study shows, for the first time, that active ghrelin and GIP are detectable in healthy tears. The strong intra-class reproducibility for leptin shows that it could be used as a potential tear fluid biomarker and, possibly, in determining the effects of metabolic disorders on the ocular surface.

Deep convolutional autoencoder for the simultaneous removal of baseline noise and baseline drift in chromatograms

Enhancement of chromatograms, such as the reduction of baseline noise and baseline drift, is often essential to accurately detect and quantify analytes in a mixture. Current methods have been well studied and adopted for decades and have assisted researchers in obtaining reliable results. However, these methods rely on relatively simple statistics of the data (chromatograms) which in some cases result in significant information loss and inaccuracies. In this study, a deep one-dimensional convolutional autoencoder was developed that simultaneously removes baseline noise and baseline drift with minimal information loss, for a large number and great variety of chromatograms. To enable the autoencoder to denoise a chromatogram to be almost, or completely, noise-free, it was trained on data obtained from an implemented chromatogram simulator that generated 190.000 representative simulated chromatograms. The trained autoencoder was then tested and compared to some of the most widely used and well-established denoising methods on testing datasets of tens of thousands of simulated chromatograms; and then further tested and verified on real chromatograms. The results show that the developed autoencoder can successfully remove baseline noise and baseline drift simultaneously with minimal information loss; outperforming methods like Savitzky-Golay smoothing, Gaussian smoothing and wavelet smoothing for baseline noise reduction (root mean squared error of 1.094 mAU compared to 2.074 mAU, 2.394 mAU and 2.199 mAU) and Savitkzy-Golay smoothing combined with asymmetric least-squares or polynomial fitting for baseline noise and baseline drift reduction (root mean absolute error of 1.171 mAU compared to 3.397 mAU and 4.923 mAU). Evidence is presented that autoencoders can be utilized to enhance and correct chromatograms and consequently improve and alleviate downstream data analysis, with the drawback of needing a carefully implemented simulator, that generates realistic chromatograms, to train the autoencoder.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra

Although Raman spectroscopy has been widely used as a noninvasive analytical tool in various applications, backgrounds in Raman spectra impair its performance in quantitative analysis. Many algorithms have been proposed to separately correct the background spectrum by spectrum. However, in real applications, there are commonly multiple spectra collected from the close locations of a sample or from the same analyte with different concentrations. These spectra are strongly correlated and provide valuable information for more robust background correction. Herein, we propose two new strategies to remove background for a set of related spectra collaboratively. Based on weighted penalized least squares, the new approaches will use the fused weights from multiple spectra or the weights from the average spectrum to estimate the background of each spectrum in the set. Background correction results from both simulated and real experimental data demonstrate that the proposed collaborative approaches outperform traditional algorithms which process spectra individually.

Background correction in separation techniques hyphenated to high-resolution mass spectrometry - Thorough correction with mass spectrometry scans recorded as profile spectra

Separation techniques hyphenated with high-resolution mass spectrometry have been a true revolution in analytical separation techniques. Such instruments not only provide unmatched resolution, but they also allow measuring the peaks accurate masses that permit identifying monoisotopic formulae. However, data files can be large, with a major contribution from background noise and background ions. Such unnecessary contribution to the overall signal can hide important features as well as decrease the accuracy of the centroid determination, especially with minor features. Thus, noise and baseline correction can be a valuable pre-processing step. The methodology that is described here, unlike any other approach, is used to correct the original dataset with the MS scans recorded as profiles spectrum. Using urine metabolic studies as examples, we demonstrate that this thorough correction reduces the data complexity by more than 90%. Such correction not only permits an improved visualisation of secondary peaks in the chromatographic domain, but it also facilitates the complete assignment of each MS scan which is invaluable to detect possible comigration/coeluting species.

Reducing Dilution and Analysis Time in Online Comprehensive Two-Dimensional Liquid Chromatography by Active Modulation

Online comprehensive two-dimensional liquid chromatography (LC × LC) offers ways to achieve high-performance separations in terms of peak capacity (exceeding 1000) and additional selectivity to realize applications that cannot be addressed with one-dimensional chromatography (1D-LC). However, the greater resolving power of LC × LC comes at the price of higher dilutions (thus, reduced sensitivity) and, often, long analysis times (>100 min). The need to preserve the separation attained in the first dimension ((1)D) causes greater dilution for LC × LC, in comparison with 1D-LC, and long analysis times to sample the (1)D with an adequate number of second dimension separations. A way to significantly reduce these downsides is to introduce a concentration step between the two chromatographic dimensions. In this work we present a possible active-modulation approach to concentrate the fractions of (1)D effluent. A typical LC × LC system is used with the addition of a dilution flow to decrease the strength of the (1)D effluent and a modulation unit that uses trap columns. The potential of this approach is demonstrated for the separation of tristyrylphenol ethoxylate phosphate surfactants, using a combination of hydrophilic interaction and reversed-phase liquid chromatography. The modified LC × LC system enabled us to halve the analysis time necessary to obtain a similar degree of separation efficiency with respect to UHPLC based LC × LC and of 5 times with respect to HPLC instrumentation (40 compared with 80 and 200 min, respectively), while at the same time reducing dilution (DF of 142, 299, and 1529, respectively) and solvent consumption per analysis (78, 120, and 800 mL, respectively).