Subwindow Factor Analysis Research Articles

Peak alignment for herbal fingerprints from liquid chromatography-high resolution mass spectrometry via diffusion model and bi-directional eigenvalues

To align the herbal fingerprints from liquid chromatography-high resolution mass spectrometry (LC-HRMS) more accurately, an algorithm based on sub-window factor analysis (SFA-HRMS) was proposed in this paper. Initially, transforming raw files into equidistant matrices based on region of interest (ROI) searching and fuzzy matching. And Haar continuous wavelet transform (Haar CWT) was used for peak detection in diffusion enhancement signals subsequently. Then bi-directional eigenvalues between two moving sub-windows were determined to obtain the optimum shifts of each candidate peak. That is to say, two types of eigenvalues were calculated for mass spectra (m/z dimension) and diffusion profiles (rt direction) in this critical step, respectively. The last step in SFA-HRMS algorithm, moving the peak zone and keeping the peak shape as much as possible. As a result, three kinds of LC-HRMS fingerprints were aligned by SFA-HRMS algorithm succesfully, including LC- quadrupole time-of-flight mass spectrometry (qTOF MS), ion trap time-of-flight mass spectrometry (IT TOF MS) and Orbitrap mass spectrometry (Orbitrap MS). Comparing with those of XCMS-online, similar behaviors may be observed in the LC-qTOF MS fingerprints from Radix Bupleuri. Even for LC-IT TOF MS signals with burrs, accurate alignments can also be achieved in the fingerprints under two ionization modes. So then twelve sets of Fructus Aurantii samples were classified by hierarchical cluster analysis and functional principal components analysis. The next LC-Orbitrap MS fingerprint from herbal preparation was also discussed. In summary, SFA-HRMS provides new ideas for peak alignment of LC-HRMS fingerprints from complex herbal samples.

Multi‐scale Gaussian/Haar wavelet strategies coupled with sub‐window factor analysis for an accurate alignment in nontargeted metabolic profiling to enhance herbal origin discrimination capability

Metabolic dataset can provide an overview of different herbal origin, which is conducted by some statistical procedures. Such results often deviate to a certain degree, due to peaks shifts in chromatographic signals. In order to solve this problem, an improved algorithm of combining sub-window factor analysis with the mass spectrum information is proposed. The algorithm uses a peak detection approach derived either from multi-scale Gaussian function or Haar wavelet to locate the peaks with different application scope; the candidate drift points at each peak are estimated by Fast Fourier transform cross correlation; Specifically, the best drift points at each candidate peaks are confirmed by sub-window factor analysis and mass spectrum information in nontargeted metabolic profiling. Finally, the peak regions were aligned against a reference chromatogram, and the non-peak regions were used linear interpolation. The chromatographic signals of 30 Bupleurum samples were aligned as an illustration of this algorithm, and they could be well distinguished using some statistical procedures. The result demonstrates that the presented method is stronger than other mass-spectra based algorithms, when facing the alignment of some co-eluted peaks.

Application of Subwindow Factor Analysis and Mass Spectral information for accurate alignment of non-targeted metabolic profiling

The peak shifts may lead to an incorrect statistical result for nontargeted metabolomics profiling, such as classification and discrimination in pattern recognition. In the paper, a more accurate alignment algorithm is developed based on Subwindow Factor Analysis and Mass Spectral information (SFA-MS). Compared with other methods, this new algorithm aligns the peaks more accurately without changing their shapes, especially for the overlapping peak clusters. To begin, the Continuous Wavelet Transform with Haar wavelet as the mother wavelet (Haar CWT) is used to determine the position and width of peaks. On this basis, the candidate drift points are confirmed by Fast Fourier Transform (FFT) cross correlation. Furthermore, the MS fitting degree of the common components between the reference chromatogram and the raw chromatogram is determined by the Subwindow Factor Analysis (SFA). When the MS information between reference and raw peaks is identical, the corresponding moving points are the optimum shifts. It is remarkable that all the peaks are moved through linear interpolation in the non-peak parts, so that the aligned chromatograms remain unchanged. The SFA-MS algorithm was implemented in the Matlab language and is available as an open source package.

Chromatographic Fingerprints Combined with Chemometric Methods Reveal the Chemical Features of Authentic Radix Polygalae.

GC-MS fingerprints of Radix Polygalae (RP) were measured for deliberately collected samples. A total of 88 volatile components were identified and quantified by subwindow factor analysis, heuristic evolving latent projection, and retention index. Next, an efficient discrimination model based on partial least-squares (PLS) discriminant analysis (DA) was developed to distinguish the superior RP samples from the inferior ones, and the reliability and predictive ability of the model was evaluated by cross-validation and permutation tests. Furthermore, four components (1-octanol, shyobunone, isobornyl acetate, and α-asarone) were screened by coefficient β of PLS-DA. They represented the important chemical features of authentic RP and could be applied to the accurate discrimination and QC of RP in the future. Our results suggest that chromatographic fingerprints coupled with chemometric methods provide an effective and convenient strategy for QC of RP and are helpful for revealing the chemical features of a complex analytical sample.

Comparative Analysis of the Volatile Components ofAgrimonia eupatoriafrom Leaves and Roots by Gas Chromatography-Mass Spectrometry and Multivariate Curve Resolution

Gas chromatography-mass spectrometry and multivariate curve resolution were applied to the differential analysis of the volatile components in Agrimonia eupatoria specimens from different plant parts. After extracted with water distillation method, the volatile components in Agrimonia eupatoria from leaves and roots were detected by GC-MS. Then the qualitative and quantitative analysis of the volatile components in the main root of Agrimonia eupatoria was completed with the help of subwindow factor analysis resolving two-dimensional original data into mass spectra and chromatograms. 68 of 87 separated constituents in the total ion chromatogram of the volatile components were identified and quantified, accounting for about 87.03% of the total content. Then, the common peaks in leaf were extracted with orthogonal projection resolution method. Among the components determined, there were 52 components coexisting in the studied samples although the relative content of each component showed difference to some extent. The results showed a fair consistency in their GC-MS fingerprint. It was the first time to apply orthogonal projection method to compare different plant parts of Agrimonia eupatoria, and it reduced the burden of qualitative analysis as well as the subjectivity. The obtained results proved the combined approach powerful for the analysis of complex Agrimonia eupatoria samples. The developed method can be used to further study and quality control of Agrimonia eupatoria.

Study of the Volatile Constituents in Radix Flemingiae Macrophyllae and a Substitute by Gas Chromatography-Mass Spectrometry and Chemometric Methods

A combined approach of subwindow factor analysis and spectral correlative chromatography was used to analyze the volatile components in Radix Flemingiae Macrophyllae and Flemingiae Latifolia Benth, one of its substitutes. After extraction by a water distillation method, the volatile components in Radix Flemingiae Macrophyllae and Flemingiae Latifolia Benth were detected by GC-MS. Then the qualitative and quantitative analysis of the volatile components in Radix Flemingiae Macrophyllae was completed with the help of subwindow factor analysis resolving two-dimensional original data into mass spectra and chromatograms. Sixty five of 82 separated constituents in the total ion chromatogram of the volatile components in Radix Flemingiae Macrophyllae were identified and quantified, accounting for about 88.79% of the total content. Then, spectral correlative chromatography was used to extract correlative constituents in Flemingiae Latifolia Benth. Fifty one correlative components were recognized in essential oil of Flemingiae Latifolia Benth. The result proves the combined approach is powerful in the analysis of complex herbal samples. The developed method can be used to compare the sameness and differences of Radix Flemingiae Macrophyllae and its substitutes and it can also be used for quality control of Radix Flemingiae Macrophyllae.

Analytical comparison of different parts of Radix Angelicae Sinensis by gas chromatography coupled with mass spectrometry

The three parts of Radix Angelicae Sinensis (Danggui) were reported to have different therapeutic effects. In order to investigate their chemical compositions of different parts of Danggui, gas chromatography–mass spectrometry (GC–MS) combined with chemometrics data analysis was applied to provide a more detailed study. Subwindow factor analysis (SFA) and a modified augmented evolving window orthogonal projection (AEWOP) method were used to resolve the batch GC–MS data sets from the Danggui samples. Then, t-test, Wilcoxon rank sum test, and principal component analysis (PCA) were applied to carry out the comparison job. The results indicate which components are found to have significant differences among the three parts. These findings may be helpful for further research of the pharmacological activities of Danggui.

Fingerprint developing of coffee flavor by gas chromatography–mass spectrometry and combined chemometrics methods

In this paper, chromatographic fingerprint was firstly used for quality control of tobacco flavors. Based on gas chromatography–mass spectrometry (GC–MS) and combined chemometrics methods, a simple, reliable and reproducible method for developing chromatographic fingerprint of coffee flavor, one of tobacco flavors, was described. Six coffee flavor samples obtained from different locations were used to establish the fingerprint. The qualitative and quantitative analysis of coffee flavor sample from Shenzhen was completed with the help of subwindow factor analysis (SFA). Fifty-two components of 68 separated constituents in coffee flavor sample from Shenzhen, accounting for 88.42% of the total content, were identified and quantified. Then, spectral correlative chromatography (SCC) was used to extract the common peaks from other five studied coffee flavor samples. Thirty-eight components were found to exist in all six samples. Finally, the method validation of fingerprint analysis was performed based on the relative retention time and the relative peak area of common peaks, sample stability and similarity analysis. The similarities of six coffee flavor samples were more than 0.9104 and showed that samples from different locations were consistent to some extent. The developed chromatographic fingerprint was successfully used to differentiate coffee flavor from coco flavor and some little difference sample prepared with coffee flavor and coco flavor by both similarity comparison and principal component projection analysis. The developed method can be used for quality control of coffee flavor.

Comparative analysis of the volatile components in cut tobacco from different locations with gas chromatography–mass spectrometry (GC–MS) and combined chemometric methods

A combined approach of subwindow factor analysis and orthogonal projection resolution was used to analyze the volatile components of cut tobacco samples from different sources. After extracted with simultaneous distillation and extraction method, the volatile components in cut tobacco from five different locations were detected by GC–MS. Then, the qualitative and quantitative analysis of the volatile components of cut tobacco from Changde area was completed with the help of subwindow factor analysis resolving two-dimensional original data into pure mass spectra and chromatograms. One hundred and two volatile components among 138 separated peaks were identified and quantified, accounting for about 88.90% of the total content. Finally, orthogonal projection method was used to extract the common peaks from different locations. Among the identified components, there were 74 components coexisting in five studied samples although the relative content of each component showed difference to some extent. The results showed a fair consistency in their GC–MS fingerprints. It was the first time to apply orthogonal projection method to compare different cut tobacco samples, and it reduced the burden of qualitative analysis as well as the subjectivity. The obtained results proved the combined approach powerful for the analysis of complex cut tobacco samples. The developed method can be used to compare the sameness and differences of cut tobacco from different sources and for quality control of cigarette production and materials.

Resolution of multicomponent overlapped peaks: A comparison of several curve resolution methods

Several curve resolution methods were compared in their performance of identification and quantification of overlapping peaks. These resolution methods are heuristic evolving latent projections (HELP), sub-window factor analysis (SFA), and orthogonal projection resolution (OPR). In this paper, HELP, SFA and OPR with the rank map produced by evolving factor analysis (EFA) or fixed size moving window-evolving factor analysis (FSMW-EFA) were applied to the simulated and experimental data. The resolved results were compared qualitatively and quantitatively. In addition, the further comparison was carried out with the results obtained from multivariate curve resolution-alternate least square (MCR-ALS) by using the initial estimates provided by EFA.

Comparative analysis of chemical components of essential oils from different samples of Rhododendron with the help of chemometrics methods

Essential oils from four samples of Rhododendron were extracted by water distillation and analyzed by gas chromatography–mass spectrometry with the help of retention indices and chemometrics resolution method named subwindow factor analysis (SFA). A total of 128 volatile components were identified reliably and fleetly. A temperature-programmed retention index ( I u T) dataset including these components has been constructed on a slightly polar capillary column (HP-5MS) at the given GC operating condition in which the standard GC parameter S = r T t M / β = 0.0087. The present work proved the usefulness of chemometrics and retention indices in complicated systems analysis and the I u T's obtained can be used in other essential oil identification. The major components of the analyzed samples showed that essential oils from different genus or even different parts of azaleas are different from each other in chemical components.

Determination of constituents of essential oil from Angelica sinensis by gas chromatography — mass spectrometry

Gas chromatography — mass spectrometry (GC-MS) coupled with chemometric resolution upon two-dimensional data was employed to analyze the constituents of essential oils of essential oils of Angelica sinensis. Constituents in essential oils of Angelica sinensis root were identified by GC-MS with the help of subwindow factor analysis (SFA) method resolving two-dimensional original data into mass spectra and chromatograms. 76 of 97 separated constituents in essential oil of Angelica sinensis root were identified and quantified, and they account for about 91. 36% of the total content. The results show that ligustilide, butylene phthalide, 2-methoxy-4-vinylphenol, carvacrol, alloocimene,2,6,6-trimethylbicyclo-[3,1,1]hept-2-ene are the main constituents in essential oil of Angelica sinensis root.

Application of multivariate curve resolution methods to on-flow LC-NMR

The application of evolving window factor analysis (EFA), subwindow factor analysis (SFA), iterative target transformation factor analysis (ITTFA), alternating least squares (ALS), Gentle, automatic window factor analysis (AUTOWFA) and constrained key variable regression (CKVR) to resolve on-flow LC-NMR data of eight compounds into individual concentration and spectral profiles is described. CKVR has been reviewed critically and modifications are suggested to obtain improved results. A comparison is made between three single variable selection methods namely, orthogonal projection approach (OPA), simple-to-use interactive self-modelling mixture analysis approach (SIMPLISMA) and simplified Borgen method (SBM). It is demonstrated that LC-NMR data can be resolved if NMR peak cluster information is utilised.

Comparison of the volatile constituents of Artemisia capillaris from different locations by gas chromatography–mass spectrometry and projection method

The volatile chemical constituents of Artemisia capillaries (an important traditional Chinese medicine) were determined by gas chromatography–mass spectrometry (GC–MS) and sub-window factor analysis (SFA). Seventy-five components were separated and 43 of them were qualitatively and quantitatively determined, which represented about 89.03% of the total content. This profile was then used to identify and assess the consistency of the herb by using an orthogonal projection method. Four different sources of A. capillaries were analyzed and compared with each other. Among the components determined, there were 51 components coexisting in all samples although the relative peak areas of a few showed variations. It is the first time to apply orthogonal projection method to the comparison of different samples, and it reduces the burden of qualitative analysis as well as the subjectivity. The results showed a fair consistency in their GC–MS fingerprint. A. capillaris was distinguished from Artemisia sacrorum L., a possible substitute in traditional Chinese medicine by comparing the fingerprints with each other.

Application of combined approach to analyze the constituents of essential oil from Dong quai.

A combined approach of sub-window factor analysis and spectral correlative chromatography has been employed to analyze the constituents of essential oils of Dong quai. Essential oils are the main pharmacological active individuals of Dong quai. Some constituents in the main root of Dong quai have been identified by GC-MS with the help of sub-window factor analysis resolving two-dimensional original data into mass spectra and chromatograms. Correlative constituents in another part of the root fiber have been recognized by spectral correlative chromatography. Seventy six of 97 separated constituents in the essential oil of main root were identified and quantified, accounting for about 91.36% of the total content. Sixty seven correlative components in the essential oil of root fiber were recognized. The result proves that the combined approach is powerful enough for the analysis of complex herbal samples.

Analysis of volatile fractions of Schisandra chinensis (Turcz.) Baill. using GC-MS and chemometric resolution.

The two-dimensional data obtained from GC-MS has been used qualitatively and quantitatively to determine the components of the volatile fractions of Schisandra chinensis obtained by six different extraction methods. Sub-window factor analysis (SFA) was employed to confirm the identities of components determined in different samples. With the help of SFA, and other chemometric techniques, peak purity in the chromatograms was determined, and overlapping peaks were resolved to yield a pure chromatographic profile and mass spectrum for each component. It is demonstrated that the accuracy of qualitative and quantitative analysis may be greatly enhanced using chemometric resolution methods, such methods being particularly valuable with respect to the analysis of complex samples such as traditional Chinese medicines. It is further demonstrated that different extraction methods give rise to volatile fractions of S. chinensis which differ qualitatively and quantitatively in their composition.

Combination of GC‐MS with local resolution for determining volatile components in si‐wu decoction

AbstractIn this paper, the combination of gas chromatography‐mass spectrometry with chemometric local resolution techniques such as subwindow factor analysis (SFA) and orthogonal projection resolution (OPR) is investigated as a method of determining volatile components present in a traditional Chinese medicinal formulation known as si‐wu decoction and its two individual herbs Rhizoma chuanxiong and Radix angelicae sinensis. In order to validate the reliability of the results obtained, the volatile components of interest were further separated on open glass columns and then analyzed in the same way as above. With the help of SFA and OPR approaches, the purity of chromatographic peaks can first be identified. Then, the pure chromatogram and mass spectrum of each component involved in a target peak cluster can be easily resolved and subsequently subjected to similarity searches in the NIST MS database to qualitatively and quantitatively determine the volatile components. Our results showed that about 127, 80, and 97 chemical components could be separated and 81, 49, and 55 of them identified, representing 83.95%, 91.86%, and 85.11% of the total relative content of volatile components from Rhizoma chuanxiong, Radix angelicae sinensis, and si‐wu decoction, respectively. The results obtained in this work strongly indicate that the combination of GC‐MS with chemometric local resolution methods could greatly improve the chromatographic separation ability by means of mathematical approaches. Moreover, they indicated the reliability and practicability of this combined technique.

Resolution of the essential constituents of Ramulus cinnamomi by an evolving chemometric approach.

The analysis of complex mixtures, such as those of traditional Chinese medicine (TCM), is difficult by conventional methods of analysis. Chemometric methods provide a new way to solve such problems. Subwindow factor analysis (SFA) paired with the evolving window orthogonal projection (EWOP) method, has been used as a new evolving approach to the resolution of volatile components of Ramulus cinnamomi (RC). Compared with conventional chromatographic analysis, the chromatographic separation conditions necessary are greatly mitigated in our approach, yet the accuracy of qualitative and quantitative results is improved, because the measured data matrix has been resolved into chromatograms and mass spectra of the chemical components. Our method is, moreover, friendly to use and easy to program. Experimental results show the efficiency and convenience of the proposed approach. Forty-seven of the seventy-eight separated constituents in an essential oil, accounting for 89.55% of the sample, were identified by mass spectroscopy (MS).

Gas chromatography–mass spectrometry and chemometric resolution applied to the determination of essential oils in Cortex Cinnamomi

In this paper, a novel procedure for qualitative and quantitative analysis of the two-dimensional data obtained from GC–MS is investigated to determine chemical components of essential oils in Cortex Cinnamomi from four different producing areas. A new method named iterative optimization procedure (IOP) specially used to resolve embedded peaks is also developed. With the help of IOP and other chemometric techniques, such as heuristic evolving latent projections, evolving factor analysis, sub-window factor analysis and orthogonal projection resolution, and etc., the detection of the purity of chromatographic peaks can be first addressed, and then the overlapping peaks are resolved into the pure chromatogram and mass spectrum of each component. The similarity searches in the MS database are finally conducted to qualitatively determine the chemical components. The results obtained showed that the accuracy of qualitative and quantitative analysis could be greatly enhanced by chemometric resolution methods. The chemometric resolution techniques upon the two-dimensional data can be quite promising tools for the analysis of the complex samples like traditional Chinese medicine.

Local resolution of hyphenated chromatographic data

Subwindow factor analysis (SFA), a new local resolution method, is applied to the resolution of the target analyte in strongly overlapping chromatographic peaks. Making full use of information hidden in overlapping regions, SFA successfully resolved the spectrum of the analyte of interest. Orthogonal projection is then used to obtain the corresponding chromatographic profile. The separation ability of chromatography is greatly enhanced with this local resolution method. It can be directly used for the qualitative and quantitative analysis of the target analytes for the hyphenated two-way chromatographic data. The geometrical meaning implied in this procedure is also discussed. Real and simulated examples are given to illustrate the efficiency of the proposed method.