Enhanced chromatographic fingerprinting of herb materials by multi-wavelength selection and chemometrics

A strategy for multi-wavelength chromatographic fingerprinting of herbal materials, using high performance liquid chromatography with a UV–Vis diode array detector is presented. Valeriana officinalis was selected to show the proposed methodology since it is a widely used commercially available herbal drug, and because misfit with other valerian species is a current issue. The enhanced fingerprints were constructed by compiling into a single data vector the chromatograms from four wavelengths (226, 254, 280 and 326 nm), at which characteristic chemical constituents of studied herbs presented maximum absorbance. Chromatographic data pretreatment included baseline correction, normalization and correlation optimized warping. A simplex optimization was performed to retrieve the optimal values of the parameters used in the warping. General success rates of a classification above 90% were achieved by soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The sensitivity and specificity of constructed models were above 94%. Tests on laboratory-made mixtures showed that it is possible to detect adulterations or counterfeits with 5% foreign herbal material, even if it is from the Valerianaceae family. The results suggest that the proposed enhanced fingerprinting approach can be used to authenticate herb materials with complex chromatographic profiles.

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► We describe an approach for multi-wavelength fingerprinting.
► PCA was used to conduct the alignment of complex chromatographic data.
► Multi-wavelength fingerprints increased the sample's data information.
► Improved results on fingerprinting V. officinalis were demonstrated.
► General success rates of a classification above 90% were achieved by SIMCA and PLS-DA.