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

Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy

Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively gives chemical structural information with the Raman spectrometer. Despite its technical advantages, in practical application scenarios, Raman spectroscopy often suffers from interference, such as noises and baseline drifts, resulting in the inability to acquire high-quality Raman spectroscopy signals, which brings challenges to subsequent spectral analysis. The commonly applied spectral preprocessing methods, such as Savitzky-Golay smooth and wavelet transform, can only perform corresponding single-item processing and require manual intervention to carry out a series of tedious trial parameters. Especially, each scheme can only be used for a specific data set. In recent years, the development of deep neural networks has provided new solutions for intelligent preprocessing of spectral data. In this paper, we first creatively started from the basic mechanism of spectral signal generation and constructed a mathematical model of the Raman spectral signal. By counting the noise parameters of the real system, we generated a simulation dataset close to the output of the real system, which alleviated the dependence on data during deep learning training. Due to the powerful nonlinear fitting ability of the neural network, fully connected network model is constructed to complete the baseline estimation task simply and quickly. Then building the Unet model can effectively achieve spectral denoising, and combining it with baseline estimation can realize intelligent joint processing. Through the simulation dataset experiment, it is proved that compared with the classic method, the method proposed in this paper has obvious advantages, which can effectively improve the signal quality and further ensure the accuracy of the peak intensity. At the same time, when the proposed method is applied to the actual system, it also achieves excellent performance compared with the common method, which indirectly indicates the effectiveness of the Raman signal simulation model. The research presented in this paper offers a variety of efficient pipelines for the intelligent processing of Raman spectroscopy, which can adapt to the requirements of different tasks while providing a new idea for enhancing the quality of Raman spectroscopy signals.

Adaptive baseline model for autonomous marine equipment and systems

With the rapid development of the Internet of Things (IoT) and the Fourth Industrial Revolution, marine equipment and systems are becoming increasingly automated and autonomous. Judging the status of equipment and systems for autonomous shipping assumes that the benchmark of status evaluation is not easily obtained, and the performance baseline for the benchmark is usually static and cannot be accurately adapted under dynamic operating conditions. This paper deals with the issue of establishing a baseline for marine equipment and systems by using a data-driven method. Considering the working conditions of marine equipment and systems, a reference-site (R-S) model was first proposed to determine the initial baseline. This method could solve the problem of inadequate parameters in the initial state very well. Then, a dynamic kernel (D-K) model was used to increase the number of reference sites and update the reference points. This method reduced the amount of data calculation in the process of a dynamic update of the baseline. Continuously fitting the reference points enabled the dynamically updated performance baseline to cope with the working conditions. To implement the proposed method, the index parameters in the R-S and D-K models were processed, and the sliding window capacity was determined using the Kolmogorov-Smirnov method. Finally, the proposed baseline model was applied to a practical case of the exhaust temperature of a marine diesel engine. The result revealed that the proposed method yielded a more accurate baseline and better adaptive performance.

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.

Study on baseline correction methods for the Fourier transform infrared spectra with different signal-to-noise ratios

Removing the baseline from the spectra, which are measured by a Fourier transform infrared spectrometer (FTIR), is an important preprocessing step for further spectra analysis such as quantitative and qualitative analysis. An automatic baseline correction method named iterative averaging, which is based on the basic knowledge of moving average, is presented. We also compared it to other methods, such as rubber band, adaptive iterative reweight penalized least squares, automatic iterative moving average, and morphological weighted penalized least squares, using simulated and experimental spectra with different signal-to-noise ratios (SNRs) to evaluate the performance of these methods by performance metrics and to select an appropriate method to analyze FTIR spectra. Performance metrics such as root-mean-square error, goodness-of-fit coefficient, and chi-square are calculated. The iterative averaging method achieves the best results, which are judged by performance metrics values, when it is applied to the FTIR spectra with different SNRs. It also can correct the baseline of the FTIR spectra automatically, and improve the capability and adaptability of the unsupervised online analysis of the FTIR system effectively.

An Automated Baseline Correction Method Based on Iterative Morphological Operations

Raman spectra usually suffer from baseline drift caused by fluorescence or other reasons. Therefore, baseline correction is a necessary and crucial step that must be performed before subsequent processing and analysis of Raman spectra. An automated baseline correction method based on iterative morphological operations is proposed in this work. The method can adaptively determine the structuring element first and then gradually remove the spectral peaks during iteration to get an estimated baseline. Experiments on simulated data and real-world Raman data show that the proposed method is accurate, fast, and flexible for handling different kinds of baselines in various practical situations. The comparison of the proposed method with some state-of-the-art baseline correction methods demonstrates its advantages over the existing methods in terms of accuracy, adaptability, and flexibility. Although only Raman spectra are investigated in this paper, the proposed method is hopefully to be used for the baseline correction of other analytical instrumental signals, such as IR spectra and chromatograms.

Analysis of Volatile Compounds by Advanced Analytical Techniques and Multivariate Chemometrics

Smelling is one of the five senses, which plays an important role in our everyday lives. Volatile compounds are, for example, characteristics of food where some of them can be perceivable by humans because of their aroma. They have a great influence on the decision making of consumers when they choose to use a product or not. In the case where a product has an offensive and strong aroma, many consumers might not appreciate it. On the contrary, soft and fresh natural aromas definitely increase the acceptance of a given product. These properties can drastically influence the economy; thus, it has been of great importance to manufacturers that the aroma of their food product is characterized by analytical means to provide a basis for further optimization processes. A lot of research has been devoted to this domain in order to link the quality of, e.g., a food to its aroma. By knowing the aromatic profile of a food, one can understand the nature of a given product leading to developing new products, which are more acceptable by consumers. There are two ways to analyze volatiles: one is to use human senses and/or sensory instruments, and the other is based on advanced analytical techniques. This work focuses on the latter. Although requirements are simple, low-cost technology is an attractive research target in this domain; most of the data are generated with very high-resolution analytical instruments. Such data gathered based on different analytical instruments normally have broad, overlapping sensitivity profiles and require substantial data analysis. In this review, we have addressed not only the question of the application of chemometrics for aroma analysis but also of the use of different analytical instruments in this field, highlighting the research needed for future focus.

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.

Current status and prospects of computational resources for natural product dereplication: a review

Research in natural products has always enhanced drug discovery by providing new and unique chemical compounds. However, recently, drug discovery from natural products is slowed down by the increasing chance of re-isolating known compounds. Rapid identification of previously isolated compounds in an automated manner, called dereplication, steers researchers toward novel findings, thereby reducing the time and effort for identifying new drug leads. Dereplication identifies compounds by comparing processed experimental data with those of known compounds, and so, diverse computational resources such as databases and tools to process and compare compound data are necessary. Automating the dereplication process through the integration of computational resources has always been an aspired goal of natural product researchers. To increase the utilization of current computational resources for natural products, we first provide an overview of the dereplication process, and then list useful resources, categorizing into databases, methods and software tools and further explaining them from a dereplication perspective. Finally, we discuss the current challenges to automating dereplication and proposed solutions.

Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy

A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety.

Comparison of three algorithms for the baseline correction of hyphenated data objects

Three novel two-way baseline correction algorithms, that is, orthogonal basis (OB), fuzzy optimal associative memory (FOAM), and polynomial fitting (PF), were evaluated with high performance liquid chromatography-mass spectrometry (HPLC-MS) and gas chromatography/mass spectrometry (GC/MS) data objects. Among these algorithms, both OB and FOAM are two-way baseline correction algorithms, which reconstruct the entire two-way backgrounds from blank data objects, while the PF algorithm is a pseudo-two-way method, which models each ion chromatogram baseline with a third-order polynomial. The performance of baseline correction methods was first evaluated with respect to the signal-to-noise ratios (SNRs) of 4 major peaks of the HPLC-MS total ion current (TIC) chromatograms of celery seed extracts. Then, the effect of baseline correction on pattern recognition was evaluated by using 42 two-way headspace (HS) solid phase microextraction (SPME) GC/MS data objects of 7 polychlorinated biphenyl (PCB) mixture standard solutions. Two types of classifiers, that is, a fuzzy rule-building expert system (FuRES) and partial least-squares-discriminant analysis (PLS-DA) were evaluated in parallel. Bootstrapped Latin partitions (BLPs) were used to give an unbiased and generalized evaluation of the classification accuracy. Results indicate that SNRs of major peaks of the TIC chromatogram representative of two-way HPLC-MS data objects are increased by baseline correction. In addition, higher prediction accuracies can be obtained by performing baseline correction on the entire GC/MS data set prior to pattern recognition. It is also found that proper data transformation is able to improve the performance of baseline correction. This report is the first of two-way baseline correction methods for hyphenated chromatography/mass spectrometry data objects. Both the orthogonal basis and FOAM baseline correction methods are novel in-house algorithms and proved to be generally effective for two-way baseline correction in the present study. Polynomial fitting is a conventional baseline correction method for one-way data objects and is applied to two-way data objects for the first time. It is applicable when blank data objects are unavailable.

Selective iteratively reweighted quantile regression for baseline correction

Extraction of qualitative and quantitative information from large numbers of analytical signals is difficult with drifted baselines, particularly in multivariate analysis. Baseline drift obscures and "fuzzies" signals, and even deteriorates analytical results. In order to obtain accurate and clear results, some effective methods should be proposed and implemented to perform baseline correction before conducting further data analysis. However, most of the classic methods require user intervention or are prone to variability, especially with low signal-to-noise signals. In this study, a novel baseline correction algorithm based on quantile regression and iteratively reweighting strategy is proposed. This does not require user intervention and prior information, such as peak detection. The iteratively reweighting strategy iteratively changes weights of residuals between fitted baseline and original signals. After a series of tests and comparisons with several other popular methods, using various kinds of analytical signals, the proposed method is found to be fast, flexible, robust, and easy to use both in simulated and real datasets.

Morphological weighted penalized least squares for background correction

Backgrounds existing in the analytical signal always impair the effectiveness of signals and compromise selectivity and sensitivity of analytical methods. In order to perform further qualitative or quantitative analysis, the background should be corrected with a reasonable method. For this purpose, a new automatic method for background correction, which is based on morphological operations and weighted penalized least squares (MPLS), has been developed in this paper. It requires neither prior knowledge about the background nor an iteration procedure or manual selection of a suitable local minimum value. The method has been successfully applied to simulated datasets as well as experimental datasets from different instruments. The results show that the method is quite flexible and could handle different kinds of backgrounds. The proposed MPLS method is implemented and available as an open source package at http://code.google.com/p/mpls.

An automated method for baseline correction, peak finding and peak grouping in chromatographic data

An automated method (FastChrom) for baseline correction, peak detection and assignment (grouping) of similar peaks across samples has been developed. The method has been tested both on artificial data and a dataset obtained from gas chromatograph analysis of wine samples. As part of the automated approach, a new method for baseline estimation has been developed and compared with other methods. FastChrom has been shown to perform at least as well as conventional software. However, compared to other approaches, FastChrom finds more peaks in the chromatograms and not only those with retention times defined by the user. FastChrom is fast and easy to use and offers the possibility of applying a retention time index which facilitates the identification of peaks and the comparison between experiments.

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.

A simple, robust orthogonal background correction method for two-dimensional liquid chromatography

Background correction is a very important step that must be performed before peak detection or any quantification procedure. When successful, this step greatly simplifies such procedures and enhances the accuracy of quantification. In the past, much effort has been invested to correct drifting baseline in one-dimensional chromatography. In fast online comprehensive two-dimensional liquid chromatography (LC×LC) coupled with a diode array detector (DAD), the change in the refractive index (RI) of the mobile phase in very fast gradients causes extremely serious baseline disturbances. The method reported here is based on the use of various existing baseline correction methods of one-dimensional (1D) liquid chromatography to correct the two-dimensional (2D) background in LC×LC. When such methods are applied orthogonally to the second dimension ((2)D), background correction is dramatically improved. The method gives an almost zero mean background level and it provides better background correction than does simple subtraction of a blank. Indeed, the method proposed does not require running a blank sample.

High-precision isothermal titration calorimetry with automated peak-shape analysis

Isothermal titration calorimetry (ITC) is a powerful classical method that enables researchers in many fields to study the thermodynamics of molecular interactions. Primary ITC data comprise the temporal evolution of differential power reporting the heat of reaction during a series of injections of aliquots of a reactant into a sample cell. By integration of each injection peak, an isotherm can be constructed of total changes in enthalpy as a function of changes in solution composition, which is rich in thermodynamic information on the reaction. However, the signals from the injection peaks are superimposed by the stochastically varying time-course of the instrumental baseline power, limiting the precision of ITC isotherms. Here, we describe a method for automated peak assignment based on peak-shape analysis via singular value decomposition in combination with detailed least-squares modeling of local pre- and postinjection baselines. This approach can effectively filter out contributions of short-term noise and adventitious events in the power trace. This method also provides, for the first time, statistical error estimates for the individual isotherm data points. In turn, this results in improved detection limits for high-affinity or low-enthalpy binding reactions and significantly higher precision of the derived thermodynamic parameters.