Application of fast Fourier transform cross-correlation and mass spectrometry data for accurate alignment of chromatograms

Automatic peak detection algorithm based on continuous wavelet transform for complex chromatograms from multi-detector micro-scale gas chromatographs

Peak detection for chromatograms, including the detection of peak retention times, peak start locations, and peak end locations, is an important processing step for extracting peak information that is used for chemical recognition. Compared to benchtop gas chromatographs, the chromatograms generated by microscale gas chromatographs (µGCs) often contain higher noise levels, peak overlap, peak asymmetry, and both positive and negative chromatographic peaks, increasing the challenges for peak detection. This paper reports an automatic peak detection algorithm based on continuous wavelet transform (CWT) for chromatograms generated by multi-detector µGCs. The relationship between chemical retention time and peak width is leveraged to differentiate chromatographic peaks from noise and baseline drift. Special features in the CWT coefficients are leveraged to detect peak overlap and asymmetry. For certain detectors that may generate positive and negative chromatographic peaks, the peaks cannot be independently detected reliably, but the peak information can be well extracted using peak information generated by other in-line single-polarity detectors. The implemented algorithm provided a true positive rate of 97.2 % and false discovery rate of 7.8 % for chromatograms generated by a µGC with three integrated detectors, two capacitive and one photoionization. The chromatograms included complex scenarios with positive and negative chromatographic peaks, up to five consecutive overlapping peaks, peak asymmetry factor up to 24, and signal-to-noise ratios spanning 9-2800.

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.

Automatic time-shift alignment method for chromatographic data analysis

Time shift among samples remains a significant challenge in data analysis, such as quality control of natural plant extracts and metabolic profiling analysis, because this phenomenon may lead to invalid conclusions. In this work, we propose a new time shift alignment method, namely, automatic time-shift alignment (ATSA), for complicated chromatographic data analysis. This technique comprised the following alignment stages: (1) automatic baseline correction and peak detection stage for providing useful chromatographic information; (2) preliminary alignment stage through adaptive segment partition to correct alignment for the entire chromatogram; and (3) precise alignment stage based on test chromatographic peak information to accurately align time shift. In ATSA, the chromatographic peak information of both reference and test samples can be completely employed for time-shift alignment to determine segment boundaries and avoid loss of information. ATSA was used to analyze a complicated chromatographic dataset. The obtained correlation coefficients among samples and data analysis efficiency indicated that the influences of time shift can be considerably reduced by ATSA; thus accurate conclusion could be obtained.

Vertical jumping biomechanical evaluation through the use of an inertial sensor-based technology

Progress in micro-electromechanical systems has turned inertial sensor units (IUs) into a suitable tool for vertical jumping evaluation. In total, 9 men and 8 women were recruited for this study. Three types of vertical jumping tests were evaluated in order to determine if the data provided by an IU placed at the lumbar spine could reliably assess jumping biomechanics and to examine the validity of the IU compared with force plate platform recordings. Robust correlation levels of the IU-based jumping biomechanical evaluation with respect to the force plate across the entire analysed jumping battery were found. In this sense, significant and extremely large correlations were found when raw data of both IU and force plate-derived normalised force-time curves were compared. Furthermore, significant and mainly moderate correlation levels were also found between both instruments when isolated resultant forces' peak values of predefined jumping phases of each manoeuvre were analysed. However, Bland and Altman graphical representation demonstrated a systematic error in the distribution of the data points within the mean ±1.96 SD intervals. Using IUs, several biomechanical variables such as the resultant force-time curve patterns of the three different vertical jumps analysed were reliably measured.

Robust alignment of chromatograms by statistically analyzing the shifts matrix generated by moving window fast Fourier transform cross-correlation

Retention time shift is one of the most challenging problems during the preprocessing of massive chromatographic datasets. Here, an improved version of the moving window fast Fourier transform cross-correlation algorithm is presented to perform nonlinear and robust alignment of chromatograms by analyzing the shifts matrix generated by moving window procedure. The shifts matrix in retention time can be estimated by fast Fourier transform cross-correlation with a moving window procedure. The refined shift of each scan point can be obtained by calculating the mode of corresponding column of the shifts matrix. This version is simple, but more effective and robust than the previously published moving window fast Fourier transform cross-correlation method. It can handle nonlinear retention time shift robustly if proper window size has been selected. The window size is the only one parameter needed to adjust and optimize. The properties of the proposed method are investigated by comparison with the previous moving window fast Fourier transform cross-correlation and recursive alignment by fast Fourier transform using chromatographic datasets. The pattern recognition results of a gas chromatography mass spectrometry dataset of metabolic syndrome can be improved significantly after preprocessing by this method. Furthermore, the proposed method is available as an open source package at https://github.com/zmzhang/MWFFT2.

Multiscale peak detection in wavelet space

Multi-scale peak detection (MSPD) for analytical instruments is presented by taking full advantage of additional information in wavelet space including ridges, valleys, and zero-crossings.

Sampling and analysis of metabolomes in biological fluids

Metabolome analysis involves the study of small molecules that are involved in the metabolic responses that occur through patho-physiological changes caused by genetic stimuli or chemical agents.