Phytochemical fingerprint and chemometrics for natural food preparation pattern recognition: an innovative technique in food supplement quality control

Abstract

Recently, the fingerprint approach using chromatography has become one of the most effective tools for quality assessment of herbal medicines and food supplements: due to the complexity of the chromatographic fingerprint and the irreproducibility of chromatographic instruments and experimental conditions, chemometric approach is employed to deal with the chromatographic fingerprint. The study was aimed at developing new analytical methods for the multivariate phytochemical fingerprinting of bioactive compounds in eight tree-species bud-preparations, commonly used in phytotherapy. Methods was used to identify and quantify the main bioactive compounds (polyphenols, organic acids and vitamins), and obtain a specific botanical profile in order to assess the contribution of each single bioactive class to the total bud preparation phytocomplex. A chemometric approach was used to distinguish among different genotypes assuring the identity, safety and quality of the botanical raw materials. The established protocol was simple, sensitive and reliable and it could be used for the evaluation and quality control of bud-extracts and natural food supplements: the proposed method was successfully applied to the characterization of commercial bud-preparations, demonstrating to be an effective tool for the fingerprinting of this plant material. The new approach developed in this study represents a good alternative for improving the classification results of herbal materials with complex chromatograms. It should be necessary to develop a "multivariate chromatographic fingerprint", in order to differentiate the herbal preparations according to their genotype, avoiding substitutions, changes or adulterations with other species or synthetic drugs.

Keywords: HPLC; Multivariate fingerprinting; Natural food-product; Raw material control.

Fig. 1

TBCC of bud-extract samples of the different reference species. Different letters for each sample indicate the significant differences at P < 0.05

Fig. 2

Phytocomplex representation of all the considered University natural food supplements

Fig. 3

The PC1-PC2 scores and loadings plot (biplot) of the U24,5 data matrix (autoscaled data)

Fig. 4

The PC1-PC2 scores plot resulting from the 45 commercial samples of the test set (C45,5 – red) projected onto the PCA hyperplane calculated for the training set (U24,5 - black)