High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis

A rapid retention time alignment algorithm was developed as a preprocessing utility to be used prior to chemometric analysis of large datasets of diesel fuel profiles obtained using gas chromatography (GC). Retention time variation from chromatogram-to-chromatogram has been a significant impediment against the use of chemometric techniques in the analysis of chromatographic data due to the inability of current chemometric techniques to correctly model information that shifts from variable to variable within a dataset. The alignment algorithm developed is shown to increase the efficacy of pattern recognition methods applied to diesel fuel chromatograms by retaining chemical selectivity while reducing chromatogram-to-chromatogram retention time variations and to do so on a time scale that makes analysis of large sets of chromatographic data practical. Two sets of diesel fuel gas chromatograms were studied using the novel alignment algorithm followed by principal component analysis (PCA). In the first study, retention times for corresponding chromatographic peaks in 60 chromatograms varied by as much as 300 ms between chromatograms before alignment. In the second study of 42 chromatograms, the retention time shifting exhibited was on the order of 10 s between corresponding chromatographic peaks, and required a coarse retention time correction prior to alignment with the algorithm. In both cases, an increase in retention time precision afforded by the algorithm was clearly visible in plots of overlaid chromatograms before and then after applying the retention time alignment algorithm. Using the alignment algorithm, the standard deviation for corresponding peak retention times following alignment was 17 ms throughout a given chromatogram, corresponding to a relative standard deviation of 0.003% at an average retention time of 8 min. This level of retention time precision is a 5-fold improvement over the retention time precision initially provided by a state-of-the-art GC instrument equipped with electronic pressure control and was critical to the performance of the chemometric analysis. This increase in retention time precision does not come at the expense of chemical selectivity, since the PCA results suggest that essentially all of the chemical selectivity is preserved. Cluster resolution between dissimilar groups of diesel fuel chromatograms in a two-dimensional scores space generated with PCA is shown to substantially increase after alignment. The alignment method is robust against missing or extra peaks relative to a target chromatogram used in the alignment, and operates at high speed, requiring roughly 1 s of computation time per GC chromatogram.     

Semi-automated alignment and quantification of peaks using parallel factor analysis for comprehensive two-dimensional liquid chromatography–diode array detector data sets

Parallel factor analysis was used to quantify the relative concentrations of peaks within four-way comprehensive two dimensional liquid chromatography–diode array detector data sets. Since parallel factor analysis requires that the retention times of peaks between each injection are reproducible, a semi-automated alignment method was developed that utilizes the spectra of the compounds to independently align the peaks without the need for a reference injection. Peak alignment is achieved by shifting the optimized chromatographic component profiles from a three-way parallel factor analysis model applied to each injection. To ensure accurate shifting, components are matched up based on their spectral signature and the position of the peak in both chromatographic dimensions. The degree of shift, for each peak, is determined by calculating the distance between the median data point of the respective dimension (in either the second or first chromatographic dimension) and the maximum data point of the peak furthest from the median. All peaks that were matched to this peak are then aligned to this common retention data point. Target analyte recoveries for four simulated data sets were within 2% of 100% recovery in all cases. Two different experimental data sets were also evaluated. Precision of quantification of two spectrally similar and partially coeluting peaks present in urine was as good as or better than 4%. Good results were also obtained for a challenging analysis of phenytoin in waste water effluent, where the results of the semi-automated alignment method agreed with the reference LC–LC MS/MS method within the precision of the methods.     

Automated optimization and construction of chemometric models based on highly variable raw chromatographic data

Direct chemometric interpretation of raw chromatographic data (as opposed to integrated peak tables) has been shown to be advantageous in many circumstances. However, this approach presents two significant challenges: data alignment and feature selection. In order to interpret the data, the time axes must be precisely aligned so that the signal from each analyte is recorded at the same coordinates in the data matrix for each and every analyzed sample. Several alignment approaches exist in the literature and they work well when the samples being aligned are reasonably similar. In cases where the background matrix for a series of samples to be modeled is highly variable, the performance of these approaches suffers. Considering the challenge of feature selection, when the raw data are used each signal at each time is viewed as an individual, independent variable; with the data rates of modern chromatographic systems, this generates hundreds of thousands of candidate variables, or tens of millions of candidate variables if multivariate detectors such as mass spectrometers are utilized. Consequently, an automated approach to identify and select appropriate variables for inclusion in a model is desirable. In this research we present an alignment approach that relies on a series of deuterated alkanes which act as retention anchors for an alignment signal, and couple this with an automated feature selection routine based on our novel cluster resolution metric for the construction of a chemometric model. The model system that we use to demonstrate these approaches is a series of simulated arson debris samples analyzed by passive headspace extraction, GC-MS, and interpreted using partial least squares discriminant analysis (PLS-DA).     

Chemometric analysis of comprehensive two-dimensional gas chromatography data using cryogenic modulation

The two-dimensional (2D) data structure generated under a high resolution GC×GC system with a small number of samplings taken across the first dimension is evaluated for the purpose of the application of chemometric deconvolution methods. Chemometric techniques such as generalized rank annihilation method (GRAM) place high demands on the reproducibility of chromatographic experiments. For GRAM to be employed for GC×GC data interpretation, it is critical that the separation method provides data with a bilinear structure; the peak-shape and retention times on both columns must be reproducible. With a limited number of samplings across a ¹D (first dimension) peak (e.g. four to six samplings) repeatability of the pattern of the modulated peaks (controlled by the modulation phase) becomes important in producing a bilinear data structure. Reproducibility of modulation phase can be affected by both reliability of the modulation period and reproducibility of the retention time of the peak on the first column (which arises from oven temperature and carrier flow rate stability). Evaluation of within-run and run-to-run retention time reproducibility (retention time uncertainty) on both columns, and modulation phase reproducibility using a modulated cryogenic system for a pair of overlapping components (fatty acid methyl esters) was undertaken. An investigation of the quality of data to permit quantification of each component by using GRAM deconvolution, was also conducted. Less than 4% run-to-run retention time uncertainty was obtained on column 1 and less than 9% run-to-run and within-run retention time uncertainty was obtained on column 2, where these R.S.D. measures are reported normalised to peak widths on each respective dimension. The R.S.D. of duplicate quantification results by GRAM ranged from 2 to 26% although the average quantification error using GRAM was less than 5%.     

Comprehensive two-dimensional gas chromatography, retention indices and time-of-flight mass spectra of flavonoids and chalcones

The applicability of comprehensive two-dimensional gas chromatography (GC×GC) for flavonoids analysis was investigated by separation and identification of flavonoids in standards, and a complex matrix natural sample. The modulation temperature was optimized to achieve the best separation and signal enhancement. The separation pattern of trimethylsilyl (TMS) derivatives of flavonoids was compared on two complementary column sets. Whilst the BPX5/BPX50 (NP/P) column set offers better overall separation, BPX50/BPX5 (P/NP) provides better peak shape and sensitivity. Comparison of the identification power of GC×GC-TOFMS against both the NIST05 MS library and a laboratory (created in-house) TOFMS library was carried out on a flavonoid mixture. The basic retention index information on high-performance capillary columns with a non-polar stationary phase was established and database of mass spectra of trimethylsilyl derivatives of flavonoids was compiled. TOFMS coupled to GC×GC enabled satisfactory identification of flavonoids in complex matrix samples at their LOD over a range of 0.5-10 μg/mL. Detection of all compounds was based on full-scan mass spectra and for each compound a characteristic ion was chosen for further quantification. This study shows that GC×GC-TOFMS yields high specificity for flavonoids derived from real natural samples, dark chocolate, propolis, and chrysanthemum.     

Trilinearity deviation ratio: A new metric for chemometric analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry data

Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC–TOFMS) is a well-established instrumental platform for complex samples. However, chemometric data analysis is often required to fully extract useful information from the data. We demonstrate that retention time shifting from one modulation to the next, Δ²t_R, is not sufficient alone to quantitatively describe the trilinearity of a single GC × GC–TOFMS run for the purpose of predicting the performance of the chemometric method parallel factor analysis (PARAFAC). We hypothesize that analyte peak width on second dimension separations, ²W_b, also impacts trilinearity, along with Δ²t_R. The term trilinearity deviation ratio, TDR, which is Δ²t_R normalized by ²W_b, is introduced as a quantitative metric to assess accuracy for PARAFAC of a GC × GC–TOFMS data cube. We explore how modulation ratio, M_R, modulation period, P_M, temperature programming rate, T_ramp, sampling phase (in-phase and out-of-phase), and signal-to-noise ratio, S/N, all play a role in PARAFAC performance in the context of TDR. Use of a P_M in the 1–2 s range provides an optimized peak capacity for the first dimension separation (500–600) for a 30 min run, with an adequate peak capacity for the second dimension separation (12–15), concurrent with an optimized two-dimensional peak capacity (6000–7500), combined with sufficiently low TDR values (0–0.05) to facilitate low quantitative errors with PARAFAC (0–0.5%). In contrast, use of a P_M in the 5 s or greater range provides a higher peak capacity on the second dimension (30–35), concurrent with a lower peak capacity on the first dimension (100–150) for a 30 min run, and a slightly reduced two-dimensional peak capacity (3000–4500), and furthermore, the data are not sufficiently trilinear for the more retained second dimension peaks in order to directly use PARAFAC with confidence.     

Quantification in comprehensive two-dimensional gas chromatography and a model of quantification based on selected summed modulated peaks

In common with all gas chromatography (GC) methods, comprehensive two-dimensional gas chromatography (GC x GC) has the potential to provide both qualitative and quantitative analysis. There are fundamental differences in the way one-dimensional (1D-GC) and GC x GC results are interpreted for these parameters. Since 1D-GC produces a single measured peak in the chromatogram, there is a single retention time, and associated with this a single peak response (either area or height). Peak area and height are related by peak width. GC x GC produces a series of modulated peaks at the detector. Thus, the peak metrics of retention, area and height for one component are now not simple single values for one peak, but rather are derived from the multiple peak distribution generated by the modulation process. The peak retention is interpreted in terms of two-dimensional coordinates in a retention plane. In this study, a brief background review to quantification in GC x GC is provided. Previous reviews cover aspects of quantitative GC x GC studies up to the year 2005, including different approaches to quantification, and reports of quantitative analysis with different detectors, for different compounds classes, and in different matrices. Other studies have developed chemometric approaches based on multivariate analysis to provide quantitative reporting of individual compounds. The coverage of the earlier reviews has been updated to include material that has been presented since 2005 and includes considerations of valve-based modulation. Recently the modulation ratio (M(R)) concept was proposed and intended to clarify the meaning of modulation number (n(M)) in GC x GC, which was shown to be a rather poorly defined parameter. Based on the prior studies that introduced this concept, the role of quantitative analysis is investigated here through calculation of the peak areas and peak area ratios of selected series of modulated peaks in GC x GC. The application of isotopically labelled reference compounds for polycyclic aromatic hydrocarbon (PAH) analysis is used here to develop the quantitative metric approach. It is shown that by selecting the two or three major modulated peaks for solutes and internal standards, comparing the response ratio with the sum of all modulated peaks and also with the reference non-modulated result, quantification is statistically equivalent. Thus, adequate quantitative analysis and calibration can be accomplished by using selected major modulated peaks for each compound. This may simplify quantitative interpretation of GC x GC data.     

Comparative analysis of peak-detection techniques for comprehensive two-dimensional chromatography

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful technology for separating complex samples. The typical goal of GC×GC peak detection is to aggregate data points of analyte peaks based on their retention times and intensities. Two techniques commonly used for two-dimensional peak detection are the two-step algorithm and the watershed algorithm. A recent study [4] compared the performance of the two-step and watershed algorithms for GC×GC data with retention-time shifts in the second-column separations. In that analysis, the peak retention-time shifts were corrected while applying the two-step algorithm but the watershed algorithm was applied without shift correction. The results indicated that the watershed algorithm has a higher probability of erroneously splitting a single two-dimensional peak than the two-step approach. This paper reconsiders the analysis by comparing peak-detection performance for resolved peaks after correcting retention-time shifts for both the two-step and watershed algorithms. Simulations with wide-ranging conditions indicate that when shift correction is employed with both algorithms, the watershed algorithm detects resolved peaks with greater accuracy than the two-step method.     

Enhancing the Limit of Detection for Comprehensive Two‐Dimensional Gas Chromatography (GC×GC) using Bilinear Chemometric Analysis

The chemometric method referred to as the generalized rank annihilation method (GRAM) is used to improve the precision, accuracy, and resolution of comprehensive two‐dimensional gas chromatography (GC×GC) data. Because GC×GC signals follow a bilinear structure, GC×GC signals can be readily extracted from noise by chemometric techniques such as GRAM. This resulting improvement in signal‐to‐noise ratio (S/N) and detectability is referred to as bilinear signal enhancement. Here, GRAM uses bilinear signal enhancement on both resolved and unresolved GC×GC peaks that initially have a low S/N in the original GC×GC data. In this work, the chemometric method of GRAM is compared to two traditional peak integration methods for quantifying GC×GC analyte signals. One integration method uses a threshold to determine the signal of a peak of interest. With this integration method only those data points above the limit of detection and within a selected area are integrated to produce the total analyte signal for calibration and quantification. The other integration method evaluated did not employ a threshold, and simply summed all the data points in a selected region to obtain a total analyte signal. Substantial improvements in quantification precision, accuracy, and limit of detection are obtained by using GRAM, as compared to when either peak integration method is applied. In addition, the GRAM results are found to be more accurate than results obtained by peak integration, because GRAM more effectively corrects for the slight baseline offset remaining after the background subtraction of data. In the case of a 2.7‐ppm propylbenzene synthetic sample the quantification result with GRAM is 2.6 times more precise and 4.2 times more accurate than the integration method without a threshold, and 18 times more accurate than the integration method with a threshold. The limit of detection for propylbenzene was 0.6 ppm (parts per million by mass) using GRAM, without implementing any sample preconcentration prior to injection. GRAM is also demonstrated as a means to resolve overlapped signals, while enhancing the S/N. Four alkyl benzene signals of low S/N which were not resolved by GC×GC are mathematically resolved and quantified.     

Chemometric approach for fast analysis of prometryn in human hair by GC–MS

A method for the fast analysis of a specific component in complex samples by GC–MS was developed and used for the quantitative determination of prometryn in hair samples. In this method, the tedious and time‐consuming sample pretreatment for purification was avoided, and a short capillary column and fast temperature program were employed to speed up the analysis. Although the measured total ion chromatogram is composed of overlapping peaks with interference and background noise, the signal of prometryn can be extracted by chemometric methods. Window‐independent component analysis was used to extract the mass spectrum and a non‐negative immune algorithm was employed to obtain the chromatographic profile of the interesting component from the measured data. Due to the complexity of the matrix, a standard addition method was adopted for the quantification. The applicability of the method was validated with spiked samples, and the recoveries were in the range of 99–105%.     

Study of the interdependency of the data sampling ratio with retention time alignment and principal component analysis for gas chromatography

An in-depth study is presented to better understand how data reduction via averaging impacts retention alignment and the subsequent chemometric analysis of data obtained using gas chromatography (GC). We specifically study the use of signal averaging to reduce GC data, retention time alignment to correct run-to-run retention shifting, and principal component analysis (PCA) to classify chromatographic separations of diesel samples by sample class. Diesel samples were selected because they provide sufficient complexity to study the impact of data reduction on the data analysis strategies. The data reduction process reduces the data sampling ratio, S(R), which is defined as the number of data points across a given chromatographic peak width (i.e., the four standard deviation peak width). Ultimately, sufficient data reduction causes the chromatographic resolution to decrease, however with minimal loss of chemical information via the PCA. Using PCA, the degree of class separation (DCS) is used as a quantitative metric. Three "Paths" of analysis (denoted A-C) are compared to each other in the context of a "benchmark" method to study the impact of the data sampling ratio on preserving chemical information, which is defined by the DCS quantitative metric. The benchmark method is simply aligning data and applying PCA, without data reduction. Path A applies data alignment to collected data, then data reduction, and finally PCA. Path B applies data reduction to collected data, and then data alignment, and finally PCA. The optimized path, namely Path C, is created from Paths A and B, whereby collected data are initially reduced to fewer data points (smaller S(R)), then aligned, and then further reduced to even fewer points and finally analyzed with PCA to provide the DCS metric. Overall, following Path C, one can successfully and efficiently classify chromatographic data by reducing to a S(R) of ～15 before alignment, and then reducing down to S(R) of ～2 before performing PCA. Indeed, following Path C, results from an average of 15 different column length-with-temperature ramp rate combinations spanning a broad range of separation conditions resulted in only a ～15% loss in classification capability (via PCA) when the loss in chromatographic resolution was ～36%.     

Data Preprocessing for Chromatographic Fingerprint of Herbal Medicine with Chemometric Approaches

Abstract

Recently, the fingerprinting approach using chromatography has become one of the most potent tools for quality assessment of herbal medicine. Due to the complexity of the chromatographic fingerprint and the irreproducibility of chromatographic instruments and experimental conditions, several chemometric approaches such as variance analysis, peak alignment, correlation analysis, and pattern recognition were employed to deal with the chromatographic fingerprint in this work. To facilitate the data preprocessing, a software named Computer Aided Similarity Evaluation (CASE) was also developed. All programs of chemometric algorithms for CASE were coded in MATLAB5.3 based on Windows. Data loading, removing, cutting, smoothing, compressing, background and retention time shift correction, normalization, peak identification and matching, variation determination of common peaks/regions, similarity comparison, sample classification, and other data processes associated with the chromatographic fingerprint were investigated in this software. The case study of high pressure liquid chromatographic HPLC fingerprints of 50 Rhizoma chuanxiong samples from different sources demonstrated that the chemometric approaches investigated in this work were reliable and user friendly for data preprocessing of chromatographic fingerprints of herbal medicines for quality assessment.     

Chemometric strategies for enhancing the chromatographic methodologies with second-order data analysis of compounds when peaks are overlapped

This overview covers the different chemometric strategies linked to chromatographic methodologies that have been used and presented in the recent literature to cope with problems related to incomplete separation, the presence of unexpected components in the sample, matrix effect and changes in the analytical signal due to pre-treatment of sample.Among the different chemometric strategies it focuses on pre-treatment of data to correct background and time shift of chromatographic peaks and the use of second-order algorithms to cope with overlapping peaks from analytes or from analytes and interferences in liquid chromatography coupled to diode array, fast-scanning fluorescence spectroscopy and mass spectrometry detectors. Finally the review presents the strategies used to deal with changes in the analytical response as result of matrix effect in liquid and gas chromatography, as well as the use of standardization strategies to correct modifications in the analytical signal as a consequence of sample pre-treatment in liquid chromatography.     

Simultaneous deconvolution and re-construction of primary and secondary overlapping peak clusters in comprehensive two-dimensional gas chromatography

In this study, simultaneous deconvolution and reconstruction of peak profiles in the first ((1)D) and second dimension ((2)D) of comprehensive two-dimensional (2D) gas chromatography (GC×GC) is achieved on the basis of the property of this new type of instrumental data. First, selective information, where only one component contributes to the peak elution window of a given modulation event, is employed for stepwise stripping of each (2)D peak with the help of pure components corresponding to that compound from the neighbouring modulations. Simulation based on an exponentially modified Gaussian (EMG) model aids this process, where the EMG represents the envelope of all (2)D peaks for that compound. The peak parameters can be restricted by knowledge of the pure modulated (2)D GC peaks derived from the same primary compound, since it is modulated into several fractions during the trapping and re-focusing process of the cryogenic modulation system according to the modulation period. Next, relative areas of all pure (2)D components of that compound are considered for reconstruction of the primary peak. This strategy of exploitation of the additional information provided by the second dimension of separation allows effective deconvolution of GC×GC datasets. Non-linear least squares curve fitting (NLLSCF) allows the resolved 2D chromatograms to be recovered. Accurate acquisition of the pure profiles in both (1)D and (2)D aids quantification of compositions and prediction of 2D retention parameters, which are of interest for qualitative and quantitative analysis. The ratio between the sum of squares of deconvolution residual and original peak response (R(rr)) is employed as an effective index to evaluate the resolution results. In this work, simulated and experimental examples are used to develop and test the proposed approach. Satisfactory performance for these studies is validated by minimum and maximum R(rr) values of 1.34e-7% and 1.09e-2%; and 1.0e-3% and 3.0e-1% for deconvolution of (1)D and (2)D peaks, respectively. Results suggest that the present technique is suitable for GC×GC data processing.     

直观推导式演进特征投影法对环境样本中多环芳烃的定性定量分析

基于新近发展的直观推导式演进特征投影法(HELP), 本文提出了一个对二维数据进行同时定性定量的分析方法, 并将其成功地用于环境样本中多环芳烃化合物定量解析。对于一维色谱难以定量的重叠峰, HELP方法充分利用色谱、光谱两方面的选择性信息, 得到了具有真实物理意义的唯一解。在定性分辨结果的基础上, 本文还提出了三种可能的定量方法。这种二维数据的解析新方法, 能大幅度地降低对色谱分离条件的要求, 可直接用于复杂实际样本的定性定量分析。     

i-RUBY: a novel software for quantitative analysis of highly accurate shotgun-proteomics liquid chromatography/tandem mass spectrometry data obtained without stable-isotope labeling of proteins

We developed a novel software named i-RUBY (identification-Related qUantification-Based strategY algorithm for liquid chromatography/tandem mass spectrometry (LC/MS/MS) data) that enables us to perform fully automatic ion current-based spectral feature analysis of highly accurate data obtained by LC/MS/MS. At the 1st step, this software utilizes accurate peptide/protein identification information for peak detection and peak matching among measurements. Then, at the 2nd step, it picks yet unidentified peaks and matches them to the peaks identified at the 1st step by a linear interpolation algorithm. The analysis of human plasma externally spiked with a known amount of yeast alcohol dehydrogenase 1 showed a good linear relationship between the amount of protein spiked and the quantitative values obtained by i-RUBY analysis. Experiment using human plasma digests spiked with a mixture of known amounts of synthetic peptides derived from two yeast proteins, alcohol dehydrogenase 1 and glucose-6-phospate isomerase, showed the expansion by the 2nd step of i-RUBY of the lower quantification limits to 1/10 to 1/1000 of those reached only by identified peaks at the 1st step. Good correlations between the i-RUBY results and the amount of proteins were confirmed by the analysis of real samples, i.e., sera of normal subjects and cancer patients, by comparing quantitative values of acute-phase proteins obtained by i-RUBY analysis of LC/MS/MS data with those obtained by an immunological method using Bio-Plex. These results taken together show that i-RUBY is a useful tool for obtaining dependable quantitative information from highly accurate shotgun-proteomics LC/MS/MS data.     

Handling within run retention time shifts in two-dimensional chromatography data using shift correction and modeling

The use of PARAFAC for modeling GC × GC-TOFMS peaks is well documented. This success is due to the trilinear structure of these data under ideal, or sufficiently close to ideal, chromatographic conditions. However, using temperature programming to cope with the general elution problem, deviations from trilinearity within a run are more likely to be seen for the following three cases: (1) compounds (i.e., analytes) severely broadened on the first column hence defined by many modulation periods, (2) analytes with a very high retention factor on the second column and likely wrapped around in that dimension, or (3) with fast temperature program rates. This deviation from trilinearity is seen as retention time-shifted peak profiles in subsequent modulation periods (first column fractions). In this report, a relaxed yet powerful version of PARAFAC, known as PARAFAC2 has been applied to handle this shift within the model step by allowing generation of individual peak profiles in subsequent first column fractions. An alternative approach was also studied, utilizing a standard retention time shift correction to restore the data trilinearity structure followed by PARAFAC. These two approaches are compared when identifying and quantifying a known analyte over a large concentration series where a certain shift is simulated in the successive first column fractions. Finally, the methods are applied to real chromatographic data showing severely shifted peak profiles. The pros and cons of the presented approaches are discussed in relation to the model parameters, the signal-to-noise ratio and the degree of shift.     

New algorithms for processing and peak detection in liquid chromatography/mass spectrometry data

Two new algorithms for automated processing of liquid chromatography/mass spectrometry (LC/MS) data are presented. These algorithms were developed from an analysis of the noise and artifact distribution in such data. The noise distribution was analyzed by preparing histograms of the signal intensity in LC/MS data. These histograms are well fit by a sum of two normal distributions in the log scale. One new algorithm, median filtering, provides increased performance compared to averaging adjacent scans in removing noise that is not normally distributed in the linear scale. Another new algorithm, vectorized peak detection, provides increased robustness with respect to variation in the noise and artifact distribution compared to methods based on determining an intensity threshold for the entire dataset. Vectorized peak detection also permits the incorporation of existing algorithms for peak detection in ion chromatograms and/or mass spectra. The application of these methods to LC/MS spectra of complex biological samples is described.