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

A chemometric-assisted method for automatic, rapid and non-targeted detection of multi-pesticides in plant-derived foods by gas chromatography-mass spectrometry

An automatic, rapid and non-targeted detection method for multi-pesticides in plant-derived foods was developed by gas chromatography-mass spectrometry and chemometrics. In this method, a novel algorithm named moving window iterative target transformation factor analysis was proposed. Although there are challenges of peak overlapping and background interference, the retention time and corrected mass spectra of unknown pesticides can be automatically obtained through iteration calculation in the 'moving window' with reference to the pesticide mass spectral library. One mixed pesticide standard and nine varieties of plant-derived foods were investigated with the proposed method. By contrast, a fast temperature programme was used to shorten detection time compared to the standard temperature programme. For the mixed standard, the mass spectra and retention times of all 39 pesticides were successfully obtained from the overlapping signal. Furthermore, all spiked pesticides were successfully detected in plant-derived foods within 10 min using a fast temperature programme.

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.

Gain deeper insights into traditional Chinese medicines using multidimensional chromatography combined with chemometric approaches

Traditional Chinese medicines (TCMs) possess a rich historical background, unique theoretical framework, remarkable therapeutic efficacy, and abundant resources. However, the modernization and internationalization of TCMs have faced significant obstacles due to their diverse ingredients and unknown mechanisms. To gain deeper insights into the phytochemicals and ensure the quality control of TCMs, there is an urgent need to enhance analytical techniques. Currently, two-dimensional (2D) chromatography, which incorporates two independent separation mechanisms, demonstrates superior separation capabilities compared to the traditional one-dimensional (1D) separation system when analyzing TCMs samples. Over the past decade, new techniques have been continuously developed to gain actionable insights from complex samples. This review presents the recent advancements in the application of multidimensional chromatography for the quality evaluation of TCMs, encompassing 2D-gas chromatography (GC), 2D-liquid chromatography (LC), as well as emerging three-dimensional (3D)-GC, 3D-LC, and their associated data-processing approaches. These studies highlight the promising potential of multidimensional chromatographic separation for future phytochemical analysis. Nevertheless, the increased separation capability has resulted in higher-order data sets and greater demands for data-processing tools. Considering that multidimensional chromatography is still a relatively nascent research field, further hardware enhancements and the implementation of chemometric methods are necessary to foster its robust development.