Identification of discriminatory variables in proteomics data analysis by clustering of variables

This article presents a data analysis method for biomarker discovery in proteomics data analysis. In factor analysis-based discriminate models, the latent variables (LV's) are calculated from the response data measured at all employed instrument channels. Since some channels are irrelevant and their responses do not possess useful information, the extracted LV's possess mixed information from both useful and irrelevant channels. In this work, clustering of variables (CLoVA) based on unsupervised pattern recognition is suggested as an efficient method to identify the most informative spectral region and then it is used to construct a more predictive multivariate classification model. In the suggested method, the instrument channels (m/z value) are clustered into different clusters via self-organization map. Subsequently, the spectral data of each cluster are separately used as the input variables of classification methods such as partial least square-discriminate analysis (PLS-DA) and extended canonical variate analysis (ECVA). The proposed method is evaluated by the analysis of two experimental data sets (ovarian and prostate cancer data set). It is found that our proposed method is able to detect cancerous from healthy samples with much higher sensitivity and selectivity than conventional PLS-DA and ECVA methods.     

Chemometric analysis of diesel fuel for forensic and environmental applications

Diesel fuel samples were analyzed using gas chromatography-mass spectrometry (GC-MS) and chemometric procedures to associate and discriminate samples for potential use in forensic and environmental applications. Twenty-five diesel samples, representing 13 different brands, were collected from service stations in the Lansing, Michigan area. From the GC-MS data, mass-to-charge ratios were identified to represent aliphatic (m/z 57) and aromatic (m/z 91 and 141) compounds. The total ion chromatogram (TIC) and extracted ion chromatograms (EICs) of the chosen ions were evaluated using Pearson product moment correlation (PPMC) and principal component analysis (PCA). Diesel samples from the same brand showed higher PPMC coefficients, while those from different brands showed lower values. EICs generally provided a wider range of correlation coefficients than the TIC, with correspondingly increased discrimination among samples for EIC m/z 91. PCA grouped the diesel samples into four distinct clusters for the TIC. The first cluster consisted of four samples from the same brand, two clusters contained one diesel sample each of different brands, and the fourth cluster contained the remaining diesel samples. The same trend was observed using each EIC, with an increase in the number of clusters formed for EIC m/z 57 and 91. Both statistical procedures suggest aromatic components (specifically, those with m/z 91) provide the greatest discrimination among diesel samples. This conclusion was supported by identifying the chemical components that contribute the most to the variance. The relative amount of aliphatic versus aromatic components was found to cause the greatest discrimination among samples in the data set.     

Coupling of capillary electrophoresis with electrospray ionization multiplexing ion mobility spectrometry

In this work, ion mobility spectrometry (IMS) function as a detector and another dimension of separation was coupled with CE to achieve two‐dimensional separation. To improve the performance of hyphenated CE‐IMS instrument, electrospray ionization correlation ion mobility spectrometry is evaluated and compared with traditional signal averaging data acquisition method using tetraalkylammonium bromide compounds. The effect of various parameters on the separation including sample introduction, sheath fluid of CE and drift gas, data acquisition method of IMS were investigated. The experimental result shows that the optimal conditions are as follows: hydrodynamic sample injection method, the electrophoresis voltage is 10 kilo volts, 5 mmol/L ammonium acetate buffer solution containing 80% acetonitrile as both the background electrolyte and the electrospray ionization sheath fluid, the ESI liquid flow rate is 4.5 μL/min, the drift voltage is 10.5 kilo volts, the drift gas temperature is 383 K and the drift gas flow rate is 300 mL/min. Under the above conditions, the mixture standards of seven tetraalkylammoniums can be completely separated within 10 min both by CE and IMS. The linear range was 5–250 μg/mL, with LOD of 0.152, 0.204, 0.277, 0.382, 0.466, 0.623 and 0.892 μg/mL, respectively. Compared with traditional capillary electrophoresis detection methods, the developed CE‐ESI‐IMS method not only provide two sets of qualitative parameters including electrophoresis migration time and ion drift time, ion mobility spectrometer can also provide an additional dimension of separation and could apply to the detection ultra‐violet transparent compounds or none fluorescent compounds.     

Detection of toluene diisocyanate in air with a coated piezoelectric crystal: Part 2. Development of an Instrumental Method For Personal Monitoring

Gases in the atmosphere are monitored with chemically-coated quartz piezoelectric crystals; the method is demonstrated in the use of polyethylene glycol for toluene diisocyanate determination. A microprocessor is used to control the gas sample flow through the detector head as well as the data acquisition. A computer-based procedure for data treatment permits signal integration and background drift correction, resulting in a theoretical detection limit of about 0.006 ppm. The design of a basic portable instrument for piezoelectric crystal monitoring of toxic gas is described with a view to future modification and microprocessor control.     

Comparative analysis of automotive paints by laser induced breakdown spectroscopy and nonparametric permutation tests

Laser-induced breakdown spectroscopy (LIBS) has been investigated for the discrimination of automobile paint samples. Paint samples from automobiles of different makes, models, and years were collected and separated into sets based on the color, presence or absence of effect pigments and the number of paint layers. Twelve LIBS spectra were obtained for each paint sample, each an average of a five single shot “drill down” spectra from consecutive laser ablations in the same spot on the sample. Analyses by a nonparametric permutation test and a parametric Wald test were performed to determine the extent of discrimination within each set of paint samples. The discrimination power and Type I error were assessed for each data analysis method. Conversion of the spectral intensity to a log-scale (base 10) resulted in a higher overall discrimination power while observing the same significance level. Working on the log-scale, the nonparametric permutation tests gave an overall 89.83% discrimination power with a size of Type I error being 4.44% at the nominal significance level of 5%. White paint samples, as a group, were the most difficult to differentiate with the power being only 86.56% followed by 95.83% for black paint samples. Parametric analysis of the data set produced lower discrimination (85.17%) with 3.33% Type I errors, which is not recommended for both theoretical and practical considerations. The nonparametric testing method is applicable across many analytical comparisons, with the specific application described here being the pairwise comparison of automotive paint samples.     

Discrimination and classification techniques applied on Mallotus and Phyllanthus high performance liquid chromatography fingerprints

Mallotus and Phyllanthus genera, both containing several species commonly used as traditional medicines around the world, are the subjects of this discrimination and classification study. The objective of this study was to compare different discrimination and classification techniques to distinguish the two genera (Mallotus and Phyllanthus) on the one hand, and the six species (Mallotus apelta, Mallotus paniculatus, Phyllanthus emblica, Phyllanthus reticulatus, Phyllanthus urinaria L. and Phyllanthus amarus), on the other. Fingerprints of 36 samples from the 6 species were developed using reversed-phase high-performance liquid chromatography with ultraviolet detection (RP-HPLC-UV). After fingerprint data pretreatment, first an exploratory data analysis was performed using Principal Component Analysis (PCA), revealing two outlying samples, which were excluded from the calibration set used to develop the discrimination and classification models. Models were built by means of Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Classification and Regression Trees (CART) and Soft Independent Modeling of Class Analogy (SIMCA). Application of the models on the total data set (outliers included) confirmed a possible labeling issue for the outliers. LDA, QDA and CART, independently of the pretreatment, or SIMCA after “normalization and column centering (N_CC)” or after “Standard Normal Variate transformation and column centering (SNV_CC)” were found best to discriminate the two genera, while LDA after column centering (CC), N_CC or SNV_CC; QDA after SNV_CC; and SIMCA after N_CC or after SNV_CC best distinguished between the 6 species. As classification technique, SIMCA after N_CC or after SNV_CC results in the best overall sensitivity and specificity.     

Sensor array data profiling for gas identification

The paper presents a new method of qualitative identification of gas. It is based on a dynamic response of sensor array with the emphasis on the processing of discrete measurement data. The information needed for identification of test samples is obtained in course of profiling the data from calibration measurements. This operation consists of the following steps: classification of data sets, selection of representative data sets, parameterization of classifiers associated with representative data sets and determination of data records. In our work Discriminant Function Analysis was used for data classification. The information saved in data record describes: the sequential number of discrete measurement, combination of gas sensors in this measurement which are best for classification of calibration samples, and the parameters of associated classifier. They are identifiers of gas class. The procedure of data record determination itself is time consuming. However this operation will be performed only at the stage of the development of the measurement instrument and when its malfunction is diagnosed. The routine use of the instrument will be restricted to gas identification task, which only utilizes the results of profiling.The identification of unknown gas is performed on the base of data records and measurement data obtained for this gas. Data records guide the preparation of data sets, separately for each class of gases. These data sets are used as input of the discriminant functions which have parameter values also indicated by data records. It was shown in the present contribution, that the qualitative identification of nine test gas samples (vapors of ethanol, acetic acid and ethyl acetate in air) with our method was very accurate and fast.     

Structure-drift time relationships in ion mobility mass spectrometry

Ion mobility spectrometry (IMS) separates ions while they travel through a buffer gas under the influence of an electrical field. The separation is affected by mass and charge but most particularly by shape (collision cross section). When coupled to MS, IMS-MS offers therefore a powerful tool for structural elucidation and isomer separation. Systematic studies aimed to compare and quantitate the effects of structural changes on drift time such as length and ramification of carbon chain, unsaturation, geometrical isomerism (cis/trans isomers for instance), cyclization and ring size are, however, scarce. Herein we used traveling wave ion mobility mass spectrometry (TWIM-MS) to systematically evaluate the relationship between structure and drift time. For that, a series of deprotonated carboxylic acids were used as model ions with a carboxylate “charge tag” for gas phase MS manipulation. Carboxylic acids showed a near linear correlation between the increase of carbon number and the increase of collision cross section (CCS). The number of double bonds changes slightly the CCS of unsaturated acids. No differences in drift time and no significant differences in CCS of cis- and trans-double bond of oleic and elaidic acids were observed. Cyclization considerably reduces the CCS. In cyclic carboxylic acids, the increase of double bonds and aromatization significantly reduces the CCS and the drift times. The use of a more polarizable drift gas, CO_2, improved in some cases the separation, as for biomarker isomers of steranoic acids. The β-isomer (cis-decaline) has smaller CCS and therefore displayed lower drift time compared to the α-isomer (trans-decaline). Structural changes revealed by calculations were correlated with trends in drift times.     

New discrimination method combining hit quality index based spectral matching and voting

A new discrimination method, called hit quality index (HQI)-voting, that uses the HQI for discriminant analysis has been developed. HQI indicates the degree of spectral matching between two spectra as known. In this method, a library sample yielding the highest HQI value for an unknown sample was initially searched and a group containing this sample was chosen as the group for the unknown sample. When overall spectral features of two groups are quite close to each other, many library samples with similar HQI values could be available for an unknown sample. In this situation, the simultaneous consideration of multiple votes (several library samples with close HQI values) for final decision would be more robust. In order to evaluate the discrimination performance of HQI-voting, three different near-infrared (NIR) spectroscopic datasets composed of two sample groups were used: (1) domestic and imported sesame samples, (2) domestic and imported Angelica gigas samples, and (3) diesel and light gas oil (LGO) samples. For the purpose of comparison, principal component analysis–linear discriminant analysis (PCA–LDA), partial least squares–discriminant analysis (PLS–DA) as well as k-nearest neighbor (k-NN) were also performed using the same datasets and the resulting accuracies were compared. The discrimination performances improved with the use of HQI-voting in comparison with those resulted from PCA–LDA and PLS–DA. The overall results support that HQI-voting is a comparable discrimination method to that of existing factor-based multivariate methods.     

One- and two-dimensional gas chromatography–mass spectrometry and high performance liquid chromatography–diode-array detector fingerprints of complex substances: A comparison of classification performance of similar,complex Rhizoma Curcumae samples with the aid of chemometrics

Many complex natural or synthetic products are analysed either by the GC–MS (gas chromatography–mass spectrometry) or HPLC–DAD (high performance liquid chromatography–diode-array detector) technique, each of which produces a one-dimensional fingerprint for a given sample. This may be used for classification of different batches of a product. GC–MS and HPLC–DAD analyses of complex, similar substances represented by the three common types of the TCM (traditional Chinese medicine), Rhizoma Curcumae were analysed in the form of one- and two-dimensional matrices firstly with the use of PCA (Principal component analysis), which showed a reasonable separation of the samples for each technique. However, the separation patterns were rather different for each analytical method, and PCA of the combined data matrix showed improved discrimination of the three types of object; close associations between the GC–MS and HPLC–DAD variables were observed. LDA (linear discriminant analysis), BP-ANN (back propagation-artificial neural networks) and LS-SVM (least squares-support vector machine) chemometrics methods were then applied to classify the training and prediction sets. For one-dimensional matrices, all training models indicated that several samples would be misclassified; the same was observed for each prediction set. However, by comparison, in the analysis of the combined matrix, all models gave 100% classification with the training set, and the LS-SVM calibration also produced a 100% result for prediction, with the BP-ANN calibration closely behind. This has important implications for comparing complex substances such as the TCMs because clearly the one-dimensional data matrices alone produce inferior results for training and prediction as compared to the combined data matrix models. Thus, product samples may be misclassified with the use of the one-dimensional data because of insufficient information.     

Development of an ion mobility spectrometer for use in an atmospheric pressure ionization ion mobility spectrometer/mass spectrometer instrument for fast screening analysis

An ion mobility spectrometer that can easily be installed as an intermediate component between a commercial triple-quadrupole mass spectrometer and its original atmospheric pressure ionization (API) sources was developed. The curtain gas from the mass spectrometer is also used as the ion mobility spectrometer drift gas. The design of the ion mobility spectrometer allows reasonably fast installation (about 1 h), and thus the ion mobility spectrometer can be considered as an accessory of the mass spectrometer. The ion mobility spectrometer module can also be used as an independently operated device when equipped with a Faraday cup detector. The drift tube of the ion mobility spectrometer module consists of inlet, desolvation, drift, and extraction regions. The desolvation, drift and extraction regions are separated by ion gates. The inlet region has the shape of a stainless steel cup equipped with a small orifice. Ion mobility spectrometer drift gas is introduced through a curtain gas line from an original flange of the mass spectrometer. After passing through the drift tube, the drift gas serves as a curtain gas for the ion-sampling orifice of the ion mobility spectrometer before entering the ion source. Counterflow of the drift gas improves evaporation of the solvent from the electrosprayed sample. Drift gas is pumped away from the ion source through the original exhaust orifice of the ion source. Initial characterization of the ion mobility spectrometer device includes determination of resolving power values for a selected set of test compounds, separation of a simple mixture, and comparison of the sensitivity of the electrospray ionization ion mobility spectrometry/mass spectrometry (ESI-IMS/MS) mode with that of the ESI-MS mode. A resolving power of 80 was measured for 2,6-di-tert-butylpyridine in a 333 V/cm drift field at room temperature and with a 0.2 ms ion gate opening time. The resolving power was shown to be dependent on drift gas flow rate for all studied ion gate opening times. Resolving power improved as the drift gas flow increased, e.g. at a 0.5 ms gate opening time, a resolving power of 31 was obtained with a 0.65 L/min flow rate and 47 with a 1.3 L/min flow rate for tetrabutylammonium iodide. The measured limits of detection with ESI-MS and with ESI-IMS/MS modes were similar, demonstrating that signal losses in the IMS device are minimal when it is operated in a continuous flow mode. Based on these preliminary results, the IMS/MS instrument is anticipated to have potential for fast screening analysis that can be applied, for example, in environmental and drug analysis.     

气相色谱仪FID检定过程中的常见故障及排除

气相色谱仪氢火焰离子化检测器（FID）检定过程中常见故障为仪器无法正常点火、仪器进样重复性差、仪器基线噪声漂移大,对这些故障原因进行了分析并提出了解决办法。     

A component tracking algorithm for accelerated and improved liquid chromatography-mass spectrometry method development

A method for tracking of sample components during liquid chromatography-mass spectrometry (LC-MS) method development has been proposed. The method manages to, fully automatically and without user intervention, find the chromatographic peaks in the data sets, discriminate them to sample components and track them when the separation conditions have been changed. The algorithm utilises the resolution obtained from all considered data sets and has the ability to discriminate the non informative parts. The technique has a great sensitivity even in cases where a majority of the tracked components cannot easily be spotted by means of traditional total ion chromatogram (TIC) or base peak chromatogram (BPC) representations. The method was tested on an experimental sample using six different columns and an average of 79% of the suggested sample components could be successfully tracked at a minimum area of 0.05% of the main component in the sample. 66 components with 79-92% of the total suggested component area were able to be tracked between all data sets. The method could be used to rapidly investigate selectivity during different types of separation conditions.     

Multi-wavelength HPLC fingerprints from complex substances: An exploratory chemometrics study of the Cassia seed example

Multi-wavelength fingerprints of Cassia seed, a traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography (HPLC) at two wavelengths with the use of diode array detection. The two data sets of chromatograms were combined by the data fusion-based method. This data set of fingerprints was compared separately with the two data sets collected at each of the two wavelengths. It was demonstrated with the use of principal component analysis (PCA), that multi-wavelength fingerprints provided a much improved representation of the differences in the samples. Thereafter, the multi-wavelength fingerprint data set was submitted for classification to a suite of chemometrics methods viz. fuzzy clustering (FC), SIMCA and the rank ordering MCDM PROMETHEE and GAIA. Each method highlighted different properties of the data matrix according to the fingerprints from different types of Cassia seeds. In general, the PROMETHEE and GAIA MCDM methods provided the most comprehensive information for matching and discrimination of the fingerprints, and appeared to be best suited for quality assurance purposes for these and similar types of sample.     

Optimized sample-weighted partial least squares

In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored. In this paper, by introducing the concept of weighted sampling into partial least squares (PLS), a new multivariate regression method, optimized sample-weighted PLS (OSWPLS) is proposed. OSWPLS differs from PLS in that it builds a new calibration set, where each sample in the original calibration set is weighted differently to account for its representativeness to improve the prediction ability of the algorithm. A recently suggested global optimization algorithm, particle swarm optimization (PSO) algorithm is used to search for the best sample weights to optimize the calibration of the original training set and the prediction of an independent validation set. The proposed method is applied to two real data sets and compared with the results of PLS, the most significant improvement is obtained for the meat data, where the root mean squared error of prediction (RMSEP) is reduced from 3.03 to 2.35. For the fuel data, OSWPLS can also perform slightly better or no worse than PLS for the prediction of the four analytes. The stability and efficiency of OSWPLS is also studied, the results demonstrate that the proposed method can obtain desirable results within moderate PSO cycles.     

Determination of Malathion Using Corona Discharge – Ion Mobility Spectrometry with Solid-Phase Microextraction

A corona discharge (CD) ion mobility spectrometer combined with solid-phase microextraction was constructed. The detection limit and stability of the system for malathion detection were improved using a novel sample introduction port through which the sample desorption was performed in high-temperature drift tube and the sample was introduced directly to the ionization area. The working parameters of the distance between CD needle tip and carrier gas outlet, the drift tube and carrier gas temperature, the carrier gas flow, and the voltage on CD needle were selected. When the extraction time for standard malathion was set to 5?min, the detection limit was 0.96?µg?L^?1 based on thrice the baseline noise, the linear range was 50–500?µg?L^?1, the correlation coefficient was approximately 0.9941, and the relative standard deviation was below 10%. Malathion-spiked lake water samples were analyzed using the developed system and the recovery was 92–102%. This method is suitable for the rapid detection of organophosphorus pesticide residues.     

Influence of variable selection on partial least squares discriminant analysis models for explosive residue classification

Using a series of thirteen organic materials that includes novel high-nitrogen energetic materials, conventional organic military explosives, and benign organic materials, we have demonstrated the importance of variable selection for maximizing residue discrimination with partial least squares discriminant analysis (PLS-DA). We built several PLS-DA models using different variable sets based on laser induced breakdown spectroscopy (LIBS) spectra of the organic residues on an aluminum substrate under an argon atmosphere. The model classification results for each sample are presented and the influence of the variables on these results is discussed. We found that using the whole spectra as the data input for the PLS-DA model gave the best results. However, variables due to the surrounding atmosphere and the substrate contribute to discrimination when the whole spectra are used, indicating this may not be the most robust model. Further iterative testing with additional validation data sets is necessary to determine the most robust model.     

电感耦合等离子体发射光谱法测定锌铝镁合金中铝、镁含量

采用基体匹配法绘制校准曲线和镧元素作内标校正来消除基体效应和仪器漂移的影响,建立了电感耦合等离子体发射光谱仪测定锌铝镁合金中铝、镁元素的分析方法.将方法应用于锌铝镁合金试样的测定,结果的相对标准偏差(RSD)在0.86％～2.13％之间,加标回收率为96.4％～99.6％.并与化学分析方法进行了比对,两种方法测定值吻合...     

A comparison of laser ablation inductively coupled plasma mass spectrometry,micro X-ray fluorescence spectroscopy,and laser induced breakdown spectroscopy for the discrimination of automotive glass

Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), micro X-ray fluorescence spectroscopy (μXRF), and laser induced breakdown spectroscopy (LIBS) are compared in terms of discrimination power for a glass sample set consisting of 41 fragments. Excellent discrimination results (> 99% discrimination) were obtained for each of the methods. In addition, all three analytical methods produced very similar discrimination results in terms of the number of pairs found to be indistinguishable. The small number of indistinguishable pairs that were identified all originated from the same vehicle. The results also show a strong correlation between the data generated from the use of µXRF and LA-ICP-MS, when comparing µXRF strontium intensities to LA-ICP-MS strontium concentrations. A 266 nm laser was utilized for all LIBS analyses, which provided excellent precision (< 10% RSD for all elements and < 10% RSD for all ratios, N = 5). The paper also presents a thorough data analysis review for forensic glass examinations by LIBS and suggests several element ratios that provide accurate discrimination results related to the LIBS system used for this study. Different combinations of 10 ratios were used for discrimination, all of which assisted with eliminating Type I errors (false exclusions) and reducing Type II errors (false inclusions). The results demonstrate that the LIBS experimental setup described, when combined with a comprehensive data analysis protocol, provides comparable discrimination when compared to LA-ICP-MS and μXRF for the application of forensic glass examinations. Given the many advantages that LIBS offers, most notably reduced complexity and reduced cost of the instrumentation, LIBS is a viable alternative to LA-ICP-MS and μXRF for use in the forensic laboratory.