Smart templates for peak pattern matching with comprehensive two-dimensional liquid chromatography

Comprehensive two-dimensional liquid chromatography (LCxLC) generates information-rich but complex peak patterns that require automated processing for rapid chemical identification and classification. This paper describes a powerful approach and specific methods for peak pattern matching to identify and classify constituent peaks in data from LCxLC and other multidimensional chemical separations. The approach records a prototypical pattern of peaks with retention times and associated metadata, such as chemical identities and classes, in a template. Then, the template pattern is matched to the detected peaks in subsequent data and the metadata are copied from the template to identify and classify the matched peaks. Smart Templates employ rule-based constraints (e.g., multispectral matching) to increase matching accuracy. Experimental results demonstrate Smart Templates, with the combination of retention-time pattern matching and multispectral constraints, are accurate and robust with respect to changes in peak patterns associated with variable chromatographic conditions.     

Bootstrap classification and point-based feature selection from age-staged mouse cerebellum tissues of matrix assisted laser desorption/ionization mass spectra using a fuzzy rule-building expert system

A bootstrap method for point-based detection of candidate biomarker peaks has been developed from pattern classifiers. Point-based detection methods are advantageous in comparison to peak-based methods. Peak determination and selection are problematic when spectral peaks are not baseline resolved or on a varying baseline. The benefit of point-based detection is that peaks can be globally determined from the characteristic features of the entire data set (i.e., subsets of candidate points) as opposed to the traditional method of selecting peaks from individual spectra and then combining the peak list into a data set. The point-based method is demonstrated to be more effective and efficient using a synthetic data set when compared to using Mahalanobis distance for feature selection. In addition, probabilities that characterize the uniqueness of the peaks are determined.This method was applied for detecting peaks that characterize age-specific patterns of protein expression of developing and adult mouse cerebella from matrix assisted laser desorption/ionization (MALDI) mass spectrometry (MS) data. The mice comprised three age groups: 42 adults, 19 14-day-old pups, and 16 7-day-old pups. Three sequential spectra were obtained from each tissue section to yield 126, 57 and 48 spectra for adult, 14-day-old pup, and 7-day-old pup spectra, respectively. Each spectrum comprised 71,879 mass measurements in a range of 3.5-50 kDa. A previous study revealed that 846 unique peaks were detected that were consistent for 50% of the mice in each age group (C. Laurent, D.F. Levinson, S.A. Schwartz, P.B. Harrington, S.P. Markey, R.M. Caprioli, P. Levitt, Direct profiling of the cerebellum by MALDI MS: a methodological study in postnatal and adult mouse, J. Neurosci. Res. 81 (2005) 613-621.).A fuzzy rule-building expert system (FuRES) was applied to investigate the correlation of age with features in the MS data. FuRES detected two outlier pup-14 spectra. Prediction was evaluated using 100 bootstrap samples of 2 Latin-partitions (i.e., 50:50 split between training and prediction set) of the mice. The spectra without the outliers yielded classification rates of 99.1 ± 0.1%, 90.1 ± 0.8%, and 97.0 ± 0.6% for adults, 14-day-old pups, and 7-day-old pups, respectively. At a 95% level of significance, 100 bootstrap samples disclosed 35 adult and 21 pup distinguishing peaks for separating adults from pups; and 8 14-day-old and 15 7-day-old predictive peaks for separating 14-day-old pup from 7-day-old pup spectra. A compressed matrix comprising 40,393 points that were outside the 95% confidence intervals of one of the two FuRES discriminants was evaluated and the classification improved significantly for all classes. When peaks that satisfied a quality criterion were integrated, the 55 integrated peak areas furnished significantly improved classification for all classes: the selected peak areas furnished classification rates of 100%, 97.3 ± 0.6%, and 97.4 ± 0.3% for adult, 14-day-old pups, and 7-day-old pups using 100 bootstrap Latin partitions evaluations with the predictions averaged. When the bootstrap size was increased to 1000 samples, the results were not significantly affected. The FuRES predictions were consistent with those obtained by discriminant partial least squares (DPLS) classifications.     

Identification of Africanized honeybees

Gas chromatography and pattern recognition methods were used to develop a potential method for differentiating European honeybees from Africanized honeybees. The test data consisted of 237 gas chromatograms of hydrocarbon extracts obtained from the wax glands, cuticle, and exocrine glands of European and Africanized honeybees. Each gas chromatogram contained 65 peaks corresponding to a set of standardized retention time windows. A genetic algorithm (GA) for pattern recognition was used to identify features in the gas chromatograms characteristic of the genotype. The pattern recognition GA searched for features in the chromatograms that optimized the separation of the European and Africanized honeybees in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the peaks identified by the pattern recognition GA primarily contained information about differences between gas chromatograms of European and Africanized honeybees. The principal component analysis routine embedded in the fitness function of the pattern recognition GA acted as an information filter, significantly reducing the size of the search space since it restricted the search to feature sets whose principal component plots showed clustering on the basis of the bees' genotype. In addition, the algorithm focused on those classes and/or samples that were difficult to classify as it trained using a form of boosting. Samples that consistently classify correctly are not as heavily weighted as samples that are difficult to classify. Over time, the algorithm learns its optimal parameters in a manner similar to a neural network. The pattern recognition GA integrates aspects of artificial intelligence and evolutionary computations to yield a "smart" one-pass procedure for feature selection and classification.     

Laser desorption/ionization mass spectrometry fingerprinting of complex hydrocarbon mixtures: application to crude oils using data mining techniques

Crude oil fingerprints were obtained from four crude oils by laser desorption/ionization mass spectrometry (LDI-MS) using a silver nitrate cationization reagent. Replicate analyses produced spectral data with a large number of features for each sample (>11,000 m/z values) which were statistically analyzed to extract useful information for their differentiation. Individual characteristic features from the data set were identified by a false discovery rate based feature selection procedure based on the analysis of variance models. The selected features were, in turn, evaluated using classification models. A substantially reduced set of 23 features was obtained through this procedure. One oil sample containing a high ratio of saturated/aromatic hydrocarbon content was easily distinguished from the others using this reduced set. The other three samples were more difficult to distinguish by LDI-MS using a silver cationization reagent; however, a minimal number of significant features were still identified for this purpose. Focus is placed on presenting this multivariate statistical method as a rapid and simple analytical procedure for classifying and distinguishing complex mixtures.     

Profiling food volatiles by comprehensive two-dimensional ga schromatography coupled with mass spectrometry: Advanced fingerprinting approaches for comparative analysis of the volatile fraction of roasted hazelnuts (Corylus avellana L.) from different origins

This study examined how advanced fingerprinting methods (i.e., non-targeted methods) provide reliable and specific information about groups of samples based on their component distribution on the GC × GC chromatographic plane. The volatile fractions of roasted hazelnuts (Corylus avellana L.) from nine different geographical origins, comparably roasted for desirable flavor and texture, were sampled by headspace-solid phase micro extraction (HS-SPME) and then analyzed by GC × GC-qMS. The resulting patterns were processed by: (a) “chromatographic fingerprinting”, i.e., a pattern recognition procedure based on retention-time criteria, where peaks correspondences were established through a comprehensive peak pattern covering the chromatographic plane; and (b) “comprehensive template matching” with reliable peak matching, where peak correspondences were constrained by retention time and MS fragmentation pattern similarity criteria. Fingerprinting results showed how the discrimination potential of GC × GC can be increased by including in sample comparisons and correlations all the detected components and, in addition, provide reliable results in a comparative analysis by locating compounds with a significant role. Results were completed by a chemical speciation of volatiles and sample profiling was extended to known markers whose distribution can be correlated to sensory properties, geographical origin, or the effect of thermal treatment on different classes of compounds. The comprehensive approach for data interpretation here proposed may be useful to assess product specificity and quality, through measurable parameters strictly and consistently correlated to sensory properties and origin.     

Targeted quantitative bioanalysis in plasma using liquid chromatography/high-resolution accurate mass spectrometry: an evaluation of global selectivity as a function of mass resolving power and extraction window, with comparison of centroid and profile modes

There is a growing interest in exploring the use of liquid chromatography coupled with full-scan high resolution accurate mass spectrometry (LC/HRMS) in bioanalytical laboratories as an alternative to the current practice of using LC coupled with tandem mass spectrometry (LC/MS/MS). Therefore, we have investigated the theoretical and practical aspects of LC/HRMS as it relates to the quantitation of drugs in plasma, which is the most commonly used matrix in pharmacokinetics studies. In order to assess the overall selectivity of HRMS, we evaluated the potential interferences from endogenous plasma components by analyzing acetonitrile-precipitated blank human plasma extract using an LC/HRMS system under chromatographic conditions typically used for LC/MS/MS bioanalysis with the acquisition of total ion chromatograms (TICs) using 10 k and 20 k resolving power in both profile and centroid modes. From each TIC, we generated extracted ion chromatograms (EICs) of the exact masses of the [M + H](+) ions of 153 model drugs using different mass extraction windows (MEWs) and determined the number of plasma endogenous peaks detected in each EIC. Fewer endogenous peaks are detected using higher resolving power, narrower MEW, and centroid mode. A 20 k resolving power can be considered adequate for the selective determination of drugs in plasma. To achieve desired analyte EIC selectivity and simultaneously avoid missing data points in the analyte EIC peak, the MEW used should not be too wide or too narrow and should be a small fraction of the full width at half maximum (FWHM) of the profile mass peak. It is recommended that the optimum MEW be established during method development under the specified chromatographic and sample preparation conditions. In general, the optimum MEW, typically ≤ ±20 ppm for 20 k resolving power, is smaller for the profile mode when compared with the centroid mode.     

Development of an algorithm for peak detection in comprehensive two-dimensional chromatography

A method for peak detection in two-dimensional chromatography is presented. The algorithm applies first the methods developed for peak detection in one-dimensional chromatography to detect peaks in one dimension. In a second step, a decision tree is applied to decide which one-dimensional peaks are originated from the same compound and have to be 'merged' into one two-dimensional peak. To this end, different features of the peaks (second-dimension peak regions and second-dimension retention times) are compared and different criteria (common peak regions, retention time differences, unimodality in the first dimension) are applied. Different options can be used, depending on the nature of the data. The user controls this decision tree by establishing several options and "switches". The algorithm was tested with GCxGC chromatograms obtained for a commercial air-freshener sample, detecting and merging the modulated peaks belonging to the same compound. Recommendations for the set of options and switches are given. A utility that calculates and sums peak areas from merged peaks is added to facilitate automated quantification. Although the algorithm was developed for GCxGC, its application to comprehensive two-dimensional liquid chromatography (LCxLC) data should at most require minor modifications.     

Toxicological classification of urine samples using pattern recognition techniques and capillary electrophoresis

In toxicology, hazardous substances detected in organisms may often lead to different pathological conditions depending on the type of exposure and level of dosage; hence, further analysis on this can suggest the best cure. Urine profiling may serve the purpose because samples typically contain hundreds of compounds representing an effective metabolic fingerprint. This paper proposes a pattern recognition procedure for determining the type of cadmium dosage, acute or chronic, administrated to laboratory rats, where urinary profiles are detected using capillary electrophoresis. The procedure is based on the composition of a sample data matrix consisting of areas of common peaks, with appropriate pre-processing aimed at reducing the lack of reproducibility and enhancing the potential contribution of low-level metabolites in discrimination. The matrix is then used for pattern recognition including principal components analysis, cluster analysis, discriminant analysis and support vector machines. Attention is particularly focussed on the last of these techniques, because of its novelty and some attractive features such as its suitability to work with datasets that are small and/or have low samples/variable ratios. The type of cadmium administration is detected as a relevant feature that contributes to the structure of the sample matrix, and samples are classified according to the class membership, with discriminant analysis and support vector machines performing complementarily on a training and on a test set.     

基于挥发性组分的气相色谱指纹图谱评价沉香化气片质量

建立了沉香化气片的气相色谱指纹图谱,并结合化学模式识别评价20批沉香化气片的质量。乙醇超声提取20批沉香化气片的挥发性成分,以正十八烷为内标,分析了3个主要组分的含量,且以内标计算其他各组分的相对峰面积,建立了沉香化气片的气相色谱指纹图谱,确定了11个共有峰,得到了各批次样品的相似度,并通过气相色谱-质谱法和对照品比对对10个共有峰进行了指认。将获得的峰面积指纹图谱采用系统聚类分析和主成分分析进行化学模式识别研究,实现了不同批次沉香化气片的区分,发现了造成不同批次样品差异的主要标记物。该方法有效且综合性强,为科学评价与有效控制沉香化气片的质量提供了可靠的参考。     

Application of comprehensive two-dimensional gas chromatography mass spectrometry and different types of data analysis for the investigation of cigarette particulate matter

In tobacco research, the comparison of different tobacco blends as well as the puff-dependent behaviour of cigarettes is a matter of particular interest. For the investigation of smoke characteristics, GC x GC offers different ways for data analysis, namely, compound target analysis, automated peak-based compound classification and comprehensive pixel-based data analysis. This study will show the application as well as the pros and cons of these types of data analysis for very complex matrices like cigarette particulate matter. In addition, new aspects about the recently discovered puff-dependent behaviour of compounds in cigarette smoke will be presented. Automated peak-based compound classification including mass spectrometric pattern recognition is used for the classification of tobacco particulate matter samples and the puff-dependent investigation of different compound classes. This compound group specific analysis is further reinforced by applying an even more comprehensive pixel-based analysis. This kind of analysis is used to generate fingerprints of different types of cigarettes. The combination of fast feature reduction methods like analysis of variance (ANOVA) and t-test with multivariate feature transformation methods like partial least squares discriminate analysis (PLSDA) for feature selection provides a powerful tool for a detailed inspection of different types of cigarettes.     

Peak pattern variations related to comprehensive two-dimensional gas chromatography acquisition

Identifying compounds of interest for peaks in data generated by comprehensive two-dimensional gas chromatography (GC x GC) is a critical analytical task. Manually identifying compounds is tedious and time-consuming. An alternative is to use pattern matching. Pattern matching identifies compounds by matching previously observed patterns with known peaks to newly observed patterns with unidentified peaks. The fundamental difficulty of pattern matching comes from peak pattern distortions that are caused by differences in data acquisition conditions. This paper investigates peak pattern variations related to varying oven temperature ramp rate and inlet gas pressure and evaluates two types of affine transformations for matching peak patterns. The experimental results suggest that, over the experimental ranges, the changes in temperature ramp rate generate non-linear pattern variations and changes in gas pressure generate nearly linear pattern variations. The results indicate the affine transformations can largely remove the pattern variations and can be used for applications such as pattern matching and normalizing retention times to retention indices.     

Automated detection of differently expressed fragments in mRNA differential display

We present a novel method for the automated detection of fragments showing dissimilar expression in mRNA differential display. The analysis is based on aligning the numerical electrophoretic lane data in respect of a given distance function defined on a set of fragments, or signal peaks in general. We presume that significant dissimilarities between peaks result in extreme score values computed for aligned peak pairs. Whereas in sequence comparison, an overall sequence similarity score is conventionally used, the current method defines a special dissimilarity score for searching the peak pairs showing the largest relative differences between the lanes. The output of the analysis is a highly reduced list of peak pairs, along with a set of associated features extracted from the lanes. Only the peaks of this list need to be visually confirmed instead of the vast amount of peaks in the original electrophoretic results. The results obtained by the algorithm correlate well with results of visual evaluation of the same electropherograms. The current algorithm may be applied to the study of complex expression patterns in multiple lanes and, in general, to automated recognition of variously defined patterns of quantitative electrophoretic data.     

Classification of high-speed gas chromatography-mass spectrometry data by principal component analysis coupled with piecewise alignment and feature selection

A useful methodology is introduced for the analysis of data obtained via gas chromatography with mass spectrometry (GC-MS) utilizing a complete mass spectrum at each retention time interval in which a mass spectrum was collected. Principal component analysis (PCA) with preprocessing by both piecewise retention time alignment and analysis of variance (ANOVA) feature selection is applied to all mass channels collected. The methodology involves concatenating all concurrently measured individual m/z chromatograms from m/z 20 to 120 for each GC-MS separation into a row vector. All of the sample row vectors are incorporated into a matrix where each row is a sample vector. This matrix is piecewise aligned and reduced by ANOVA feature selection. Application of the preprocessing steps (retention time alignment and feature selection) to all mass channels collected during the chromatographic separation allows considerably more selective chemical information to be incorporated in the PCA classification, and is the primary novelty of the report. This methodology is objective and requires no knowledge of the specific analytes of interest, as in selective ion monitoring (SIM), and does not restrict the mass spectral data used, as in both SIM and total ion current (TIC) methods. Significantly, the methodology allows for the classification of data with low resolution in the chromatographic dimension because of the added selectivity from the complete mass spectral dimension. This allows for the successful classification of data over significantly decreased chromatographic separation times, since high-speed separations can be employed. The methodology is demonstrated through the analysis of a set of four differing gasoline samples that serve as model complex samples. For comparison, the gasoline samples are analyzed by GC-MS over both 10-min and 10-s separation times. The successfully classified 10-min GC-MS TIC data served as the benchmark analysis to compare to the 10-s data. When only alignment and feature selection was applied to the 10-s gasoline separations using GC-MS TIC data, PCA failed. PCA was successful for 10-s gasoline separations when the methodology was applied with all the m/z information. With ANOVA feature selection, chromatographic regions with Fisher ratios greater than 1500 were retained in a new matrix and subjected to PCA yielding successful classification for the 10-s separations.     

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.     

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.     

生物样品二恶英分析中两个常见干扰峰的鉴定

利用高分辨气相色谱/高分辨质谱联用仪（HRGC/HRMS）和高分辨气相色谱/低分辨质谱仪（HRGC/LRMS）对实际生物样品中二恶英（polychlorinated dibenzo-p-dioxins and dibenzofurans,PCDD/Fs）的分析过程中¹³C标记的2,3,7,8-四氯代二苯并呋喃（¹³C₁₂-2,3,7,8-TCDF）监测碎片离子通道的两个常见干扰峰进行了分析鉴定。通过实际样品分析结果首先推测这两个干扰峰可能为有机氯农药类化合物滴滴涕（DDT）降解产物滴滴伊（DDE）的两个异构体,其次采用DDE的标准溶液（包括o,p’-DDE和p,p’-DDE）进行分析确认。通过HRGC/HRMS的色谱峰分离效果分析、色谱保留时间匹配以及与DDE碎片离子的理论丰度比进行比较,最终确认实际样品分析中的两个干扰峰依次为o,p’-DDE和p,p’-DDE。本文可为生物样品中二恶英的准确识别提供重要参考。     

Comprehensive comparison of liquid chromatography selectivity as provided by two types of liquid chromatography detectors (high resolution mass spectrometry and tandem mass spectrometry): “Where is the crossover point?”

The selectivity of mass traces obtained by monitoring liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) was compared. A number of blank extracts (fish, pork kidney, pork liver and honey) were separated by ultra performance liquid chromatography (UPLC). Detected were some 100 dummy transitions respectively dummy exact masses (traces). These dummy masses were the product of a random generator. The range of the permitted masses corresponded to those which are typical for analytes (e.g. veterinary drugs). The large number of monitored dummy traces ensured that endogenous compounds present in the matrix extract, produced a significant number of detectable chromatographic peaks. All obtained chromatographic peaks were integrated and standardized. Standardisation was done by dividing these absolute peak areas by the average response of a set of 7 different veterinary drugs. This permitted a direct comparison between the LC-HRMS and LC-MS/MS data. The data indicated that the selectivity of LC-HRMS exceeds LC-MS/MS, if high resolution mass spectrometry (HRMS) data is recorded with a resolution of 50,000 full width at half maximum (FWHM) and a corresponding mass window. This conclusion was further supported by experimental data (MS/MS based trace analysis), where a false positive finding was observed. An endogenous matrix compound present in honey matrix behaved like a banned nitroimidazole drug. This included identical retention time and two MRM traces, producing an MRM ratio between them, which perfectly matched the ratio observed in the external standard. HRMS measurement clearly resolved the interfering matrix compound and unmasked the false positive MS/MS finding.     

Synthesis and optical properties of nanorattles and multiple-walled nanoshells/nanotubes made of metal alloys

The galvanic replacement reaction between silver and chloroauric acid has been exploited as a powerful means for preparing metal nanostructures with hollow interiors. Here, the utility of this approach is further extended to produce complex core/shell nanostructures made of metals by combining the replacement reaction with electroless deposition of silver. We have fabricated nanorattles consisting of Au/Ag alloy cores and Au/Ag alloy shells by starting with Au/Ag alloy colloids as the initial template. We have also prepared multiple-walled nanoshells/nanotubes (or nanoscale Matrioshka) with a variety of shapes, compositions, and structures by controlling the morphology of the template and the precursor salt used in each step of the replacement reaction. There are a number of interesting optical features associated with these new core/shell metal nanostructures. For example, nanorattles made of Au/Ag alloys displayed two well-separated extinction peaks, a feature similar to that of gold or silver nanorods. The peak at approximately 510 nm could be attributed to the Au/Ag alloy cores, while the other peak was associated with the Au/Ag alloy shells and could be continuously tuned in the spectral range from red to near-infrared.     

实时直接分析高分辨质谱技术研究碳硼烷类化合物

该文运用高分辨质谱技术对实时直接分析(Direct analysis in real time,DART)离子化条件下碳硼烷化合物的质谱行为进行了研究,对碳硼烷化合物DART高分辨质谱中所得到的同位素峰簇进行了表征与归属。研究结果表明,选取的碳硼烷化合物在DART负离子条件下均能得到较好的质谱信号,这可能与硼笼结构的“缺电性”有关。含10个B原子的碳硼烷化合物形成的离子同位素峰簇信号中,通常情况下相对丰度最高的同位素峰中含2个10B以及8个11B。将碳硼烷化合物高分辨质谱分析的精确m/z数据信息与图谱中同位素峰轮廓分析相结合,是碳硼烷化合物有效的质谱定性分析与表征策略。     

岛津C－R2A全自动高效液相色谱分析程序的设计

利用Ｃ－Ｒ２Ａ色谱处理机的编程功能，实现了液相色谱法（主机为ＬＣ－４Ａ和ＳＩＬ－２ＡＳ自动进样器）全自动分析，即通过程序实现按设定的样品顺序、进样量、重复次数对样品进行测定，每个样品分析完后，进行数据处理，并打印出结果表