A comparative study on several algorithms for denoising of thin layer densitograms

Developing a Peak Extraction and Retention (PEER) Algorithm for Improving the Temporal Resolution of Raman Spectroscopy

In spectroscopic analysis, push-to-the-limit sensitivity is one of the important topics, particularly when facing the qualitative and quantitative analyses of the trace target. Normally, the effective recognition and extraction of weak signals are the first key steps, for which there has been considerable effort in developing various denoising algorithms for decades. Nevertheless, the lower the signal-to-noise ratio (SNR), the greater the deviation of the peak height and shape during the denoising process. Therefore, we propose a denoising algorithm along with peak extraction and retention (PEER). First, both the first and second derivatives of the Raman spectrum are used to determine Raman peaks with a high SNR whose peak information is kept away from the denoising process. Second, an optimized window smoothing algorithm is applied to the left part of the Raman spectrum, which is combined with the untreated Raman peaks to obtain the denoised Raman spectrum. The PEER algorithm is demonstrated with much better signal extraction and retention and successfully improves the temporal resolution of Raman imaging of a living cell by at least 1 order of magnitude higher than those by traditional algorithms.

A fast progressive spectrum denoising combined with partial least squares algorithm and its application in online Fourier transform infrared quantitative analysis

Fourier transform infrared (FTIR) spectroscopy is an important method in analytical chemistry. A material can be qualitatively and quantitatively analyzed from its FTIR spectrum. Spectrum denoising is commonly performed before online FTIR quantitative analysis. The average method requires a long time to collect spectra, which weakens real-time online analysis. The Savitzky-Golay smoothing method makes peaks smoother with the increase of window width, causing useful information to be lost. The sparse representation method is a common denoising method, that is used to reconstruct spectrum. However, for the randomness of noise, we can't achieve the sparse representation of noise. Traditional sparse representation algorithms only perform denoising once, and the noise can not be removed completely. FTIR spectrum denoising should therefore be performed in a progressive way. However, it is difficult to determine to what degree of denoising is required. Here, a fast progressive spectrum denoising combined with partial least squares method was developed for online FTIR quantitative analysis. Two real sample data sets were used to test the performance of the proposed method. The experimental results indicated that the progressive spectrum denoising method combined with the partial least squares method performed markedly better than other methods in terms of root mean squared error of prediction and coefficient of determination in the FTIR quantitative analysis.

Applications of the Whittaker smoother in NMR spectroscopy

The Whittaker smoother, a special case of penalized least square, is a multipurpose algorithm that has proven to be very useful in many scientific fields, including image processing, chromatography, and optical spectroscopy. It shares many similarities with the Savitzky-Golay algorithm, but it is significantly faster and easier to automate. Its use in nuclear magnetic resonance, however, is not widespread although several applications have recently been published. In this review, the mathematical background of the method and its main applications in nuclear magnetic resonance spectroscopy will be discussed.

Quantification of total content of non-esterified fatty acids bound to human serum albumin

Non-esterified fatty acids bound to the human serum albumin (HSA) contribute to several HSAs properties of special concern in pathologies, for instance to the reactivity of the free HSA-Cys34 thiol group (important antioxidative thiol pool in plasma), and to the affinity for binding of molecules and ions (for example cobalt as a prominent biomarker in heart ischemia). Therefore, the method for determination of FAs bound to HSA was developed. FAs were released from HSA (previously isolated from serum by ammonium sulfate precipitation) using acidic copper(II) sulfate in phosphoric acid, extracted by n-heptane-chloroform (4:1, v/v) mixture, spotted on TL silica-gel and then developed with n-heptane-chloroform-acetic acid (5:3:0.3, v/v/v). Common office flatbed scanner and software solution for densitometric image analysis, developed in R, were used. The linearity of calibration curve in concentration range from 0.1 to 5.0mmol/L stearic acid was achieved. The method was proved to be precise (with RSD of 1.4-4.7%) and accurate. Accuracy was examined by standard addition method (recoveries 97.2-102.5%) and by comparison to results of GC. The method is sample saving, technically less demanding, and cheap, and therefore suitable for determination of FAs/HSA ratio when elevated concentrations of free FAs are reliable diagnostic/risk parameter of pathological states.

Comparison of Analytical Methods in Chemometric Fingerprinting of Metallicolous and Non-metallicolous Populations of Echium vulgare L

INTRODUCTION

Adverse environmental conditions usually change plant biochemical pathways resulting in accumulation or decreased content of both primary and secondary metabolites. The chemometric fingerprinting analysis proves to be a useful tool to reveal phytochemical differentiation between plants inhabiting heavy metal-contaminated and uncontaminated areas.

OBJECTIVE

Development and assessment of four analytical techniques - high performance capillary electrophoresis (HPCE), thin-layer chromatography (TLC), mass spectrometry (MS), and Fourier transform infrared (FTIR) spectroscopy in chemometric fingerprinting of metallicolous and non-metallicolous populations of Echium vulgare L.

MATERIAL AND METHODS

Twenty-one crude methanol extracts of shoot samples representing three populations of Echium vulgare L., two originating from highly metal polluted areas and one from an unpolluted area, were investigated using four analytical methods: HPCE, TLC, MS, and FTIR spectroscopy. Data pre-processing (denoising, background subtracting, horizontal alignment) followed by principal component analysis (PCA), hierarchical clustering analysis (HCA), and phytochemical difference index (DI) calculations facilitated exploration of the differences and similarities between the populations.

RESULTS

Clear phytochemical divergence between metallicolous and non-metallicolous populations of Echium vulgare was found. The suitability of the analytical techniques for revealing phytochemical markers and discrimination of individuals originating from different populations differed and in general increased in the order: TLC < MS = HPCE < FTIR.

CONCLUSION

The chemometric methods applied were successful in discrimination between samples from polluted and unpolluted areas, showing a potential perspective for environmental quality control. Copyright © 2016 John Wiley & Sons, Ltd.

Miniaturized planar chromatography using office peripherals--office chromatography

Office chromatography (OC) harnesses the novel combination of miniaturized planar separation science and modern print & media technologies. Interdisciplinary knowledge is the essence: Printing of solutions on powerful miniaturized planar separation materials in combination with image capturing and evaluation tools enables an innovative analytical online system. Site-specific printing as lines or areas on defined sections of the layer comprises important steps like application of samples, feeding of the mobile phase as well as supply of the derivatization reagent. Also printing of bioassays can be combined for effect-directed detections and the homogeneous printing of the ultrathin layer itself, enabling tailor-made gradient-layer or multi-layer plates. OC exploits image-giving miniaturized chromatograms being captured and processed with a flatbed scanner or mini-camera. Thus, miniaturized separation materials are the core of OC. Monolithic, electrospun, nanostructured glancing angle deposition and carbon nanotube-templated microfabricated layers or even pillar arrays or polymer brush coated sub-μm silica particles were demonstrated, showing promising results. Layer thicknesses from 50 μm down to few micrometers were explored. A high-throughput capacity is given through the parallel development of as many as possible tiny-printed samples on the separation material. The migration time was reduced to a few minutes and the calculated analysis time per sample lasted few seconds. Considering a substantially reduced solvent consumption at short run times for parallel analysis of numerous samples at the same time, OC is an appropriate analytical technique for green chemistry. OC facilitates the whole planar separation process to be performed with no other equipment but a combined device of printer and flatbed scanner or mini-camera. At the same time, OC can be expected to become a widespread and economical technique with the user-friendliness of high-end office tools, appealing to users.

Chemometrics in Fingerprinting by Means of Thin Layer Chromatography

The paper is written as an introductory review, presenting summary of current knowledge about chemometric fingerprinting in the context of TLC, due to a rather small interest in the literature about joining TLC and chemometrics. The paper shortly covers the most important aspects of the chemometric fingerprinting in general, creating the TLC fingerprints, denoising, baseline removal, warping/registering, and chemometric processing itself. References being good candidates as a starting point are given for each topic and processing step.

Image denoising methods for tumor discrimination in high-resolution computed tomography

Pixel accuracy in images from high-resolution computed tomography (HRCT) is ultimately limited by reconstruction error and noise. While for visual analysis this may not be relevant, for computer-aided quantitative analysis in either densitometric, or shape studies aiming at accurate results, the impact of pixel uncertainty must be taken into consideration. In this work, we study several denoising methods: geometric mean filter, Wiener filtering, and wavelet denoising. The performance of each method was assessed through visual inspection, profile region intensity analysis, and global figures of merit, using images from brain and thoracic phantoms, as well as several real thoracic HRCT images.

Automated spectral smoothing with spatially adaptive penalized least squares

A variety of data smoothing techniques exist to address the issue of noise in spectroscopic data. The vast majority, however, require parameter specification by a knowledgeable user, which is typically accomplished by trial and error. In most situations, optimized parameters represent a compromise between noise reduction and signal preservation. In this work, we demonstrate a nonparametric regression approach to spectral smoothing using a spatially adaptive penalized least squares (SAPLS) approach. An iterative optimization procedure is employed that permits gradual flexibility in the smooth fit when statistically significant trends based on multiscale statistics assuming white Gaussian noise are detected. With an estimate of the noise level in the spectrum the procedure is fully automatic with a specified confidence level for the statistics. Potential application to the heteroscedastic noise case is also demonstrated. Performance was assessed in simulations conducted on several synthetic spectra using traditional error measures as well as comparisons of local extrema in the resulting smoothed signals to those in the true spectra. For the simulated spectra, a best case comparison with the Savitzky-Golay smoothing via an exhaustive parameter search was performed while the SAPLS method was assessed for automated application. The application to several dissimilar experimentally obtained Raman spectra is also presented.

Comparison of Several Methods of Chromatographic Baseline Removal with a New Approach Based on Quantile Regression

The article is intended to introduce and discuss a new quantile regression method for baseline detrending of chromatographic signals. It is compared with current methods based on polynomial fitting, spline fitting, LOESS, and Whittaker smoother, each with thresholding and reweighting approach. For curve flexibility selection in existing algorithms, a new method based on skewness of the residuals is successfully applied. The computational efficiency of all approaches is also discussed. The newly introduced methods could be preferred to visible better performance and short computational time. The other algorithms behave in comparable way, and polynomial regression can be here preferred due to short computational time.