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

Analysis of GC × GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of Curcuma wenyujin

This study introduces an innovative contour detection algorithm, PeakCET, designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram (GC × GC). This method innovatively combines contour edge tracking with affinity propagation (AP) clustering for peak detection in GC × GC fingerprints, the first in this field. Contour edge tracking significantly reduces false positives caused by "burr" signals, while AP clustering enhances detection accuracy in the face of false negatives. The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin. PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples. Furthermore, this algorithm compares the GC × GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins. The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues. Each sample exhibits unique characteristic components alongside common ones, and variations in content may influence their therapeutic effectiveness. This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional (2D) fingerprint analysis of GC × GC data.

Compensation of temperature effects on spectra through evolutionary rank analysis

Spectra measured in various ranges of temperature are usually slightly different from each other in shape and position of the bands. Although the displayed inconsistencies are rather small, yet may lead to incorrect analysis and interpretation of the collected spectrothermal data. Thus the unspecific spectral effects induced by temperature, in particular the thermal shifts and broadening of the bands, have to be compensated. In the paper, a simple two-step method of thermospectral dataset uniformisation is presented. Thermally induced 'movement' of the bands is approximated as a linear function of the difference of temperatures, so the co-shifting of the spectra is done linearly. Thermal broadening is mimicked by convoluting the low-temperature signal (spectrum) with a Gaussian or Lorentzian spreading filter. Proper widths (values of FWHM) of these filters, used to uniform the whole dataset, are assumed to depend on the difference of temperatures, in a form of one-parameter functions. This assumption, which has been empirically confirmed, is a fundamental premise of the method of Partial Compensation for Thermal Broadening (PCTB). Optimal values of the parameters of all the functions, used to compensate both thermal shifting and broadening, are found by the Evolutionary Rank Analysis (ERA) applied on an evolving data matrix. Efficiency of the proposed approach was verified on the UV-Vis thermospectral dataset of one-component model systems. In addition, since the method is aimed at making uniformed the thermospectral datasets of multi-component systems with similar spectral properties of individual components, the two-component conformer system of t-APE (trans-1-(2'-anthryl)-2-phenylethene) has also been analyzed.

Herbal Component Correlation and Matrix-based Resolution in Comprehensive two-dimensional Gas Chromatography - Mass Spectrometry data via Intelligent Clustering of Modulation Peaks

In order to facilitate correlation calculation and matrix-based resolution in comprehensive two-dimensional gas chromatography - mass spectrometry (GC × GC-MS) data-set, an intelligent clustering of modulation peaks (ICMP) algorithm was developed in this paper. ICMP is start with the second -dimension (²D) peak restriction, then conducting the peak shape restriction in the first dimension (¹D), finally end with the eigenvalues calculation against mass spectra in moving sub-windows. After this three-tier restriction, multi-component spectral correlative chromatography (MSCC) was applied in peak clustering result from a row-wise augmented "two-dimension (2D) slice" set. Then the component similarities and differences were distinguished rapidly/ accurately in chemical fingerprints from ChaiHu Shugan San and Cyperus rotundus. Faced with co-eluted phenomenon, matrix-based resolution was made in the representative sub-matrices that have been locked in ICMP procedure. From the example data shows that ICMP- multivariate curve resolution (MCR) can served as a good complement to (non) trilinear decomposition. To summarize, the GC × GC data-structure can be simplified to facilitate MSCC or MCR operation in fingerprints from herbal or biological samples.

Determination and comparison of agarwood from different origins by comprehensive two-dimensional gas chromatography-quadrupole time-of-flight mass spectrometry

Agarwood, a species of resinous heartwood, is a precious medicinal plant and a type of rare natural spice, which is widely used in medicine, cosmetics, religious activities, and other fields. In this study, agarwood samples from eight different regions across four countries were analyzed by comprehensive two-dimensional gas chromatography-quadrupole time-of-flight mass spectrometry. A total of 232 species were identified (the match factors of these compounds were above 750). The main compounds of agarwood are oxygenated sesquiterpenes and chromones. The compositions of India1 and Malaysia2 were significantly different from those of other samples, which might be attributed to the different production processes of agarwood. For further investigation, factor analysis was conducted for six agarwood samples. The results showed that the data classification possessed a regional characteristic; according to the retention time and relative content, characteristic compositions were determined by factor scores. Finally, the differences of characteristic compositions were simply analyzed, and the reasons were speculated.

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.