# A novel algorithm- specific loci screening accelerates the establishment of molecular quantification of Glycyrrhiza glabra, G. uralensis, and G. inflata

## TL;DR

This study developed a new method to accurately identify and quantify different licorice species in mixtures using a novel algorithm and molecular techniques.

## Contribution

A novel SLS algorithm and Herb-Q assay were developed for precise licorice species quantification in mixtures.

## Key findings

- The SLS algorithm identified variable loci in licorice species using chloroplast genomes.
- The Herb-Q assay achieved high accuracy in quantifying licorice species in mixtures with low bias.
- The method was successfully applied to homemade licorice mixtures and the patent medicine Liuyi San.

## Abstract

This study aims to establish a specialized molecular method for distinguishing different licorice and quantifying target species accurately from mixtures.

A rapid specific loci screening (SLS) algorithm was developed in this study, and used the chloroplast genome sequences were used to screen for variable loci in the Glycyrrhiza glabra (GG), G. uralensis (GU), and G. inflata (GI). Each locus has been validated by polymerase chain reaction and pyrosequencing. The selected loci were analyzed by Herb molecular quantification (Herb-Q) assay to complete the quantitative methodology verification and establish a quantitative detection system for homemade licorice mixtures and one of the patent medicines—Liuyi San.

Outstanding performance was observed in quantitative validation, which evaluated linearity (0.9989), limit of detection (2%) and quantification (2%), and repeatability; additionally, a quantitative detection system was established for homemade licorice mixtures and Liuyi San (one of the patent medicines), with the lowest bias being 3.48%. When both GG and GU were present in the mixed powder, the average biases for GG and GU quantification were 8.25% and 8.01%, respectively. When only GG was present in Liuyi San, the bias was 6.43%; when both GG and GU were present, the biases for GG and GU were 5.61% and 3.48%, respectively.

This study successfully established an accurate detection system for quantifying the botanical origin of edible-medicinal licorice, which represents a significant milestone in enhancing the safety, efficacy, and quality control in licorice products.

The online version contains supplementary material available at 10.1186/s13020-025-01263-2.