A Novel Strategy for Rapid Quantification of Multiple Quality Indicators and Grade Discrimination of Atractylodis macrocephalae Rhizoma Based on Electronic Nose, Electronic Tongue and Machine-Learning Algorithms

TL;DR

This paper introduces a fast, non-invasive method using electronic nose and tongue with machine learning to assess the quality and grade of Atractylodis macrocephalae Rhizoma, improving efficiency over traditional methods.

Contribution

The study introduces a novel integration of electronic sensors and machine learning for rapid, non-destructive quality assessment of Atractylodis macrocephalae Rhizoma.

Findings

E-tongue with KNN algorithm achieved 95.56% accuracy in AMR grade classification.

Quantitative predictions of eight quality markers showed high R² values, up to 0.9628 for polysaccharides.

The method enables high-throughput, non-destructive screening for AMR quality control.

Abstract

Atractylodes macrocephala Rhizoma (AMR) is a frequently used medicinal herb for treating gastrointestinal disorders, with its quality influenced by factors such as origin and cultivation duration. Traditional quality control methods for AMR are time-consuming and invasive, making the development of faster and more efficient alternatives urgently needed. This study aims to utilize electronic nose (E-nose) and electronic tongue (E-tongue) to achieve the acquisition of odor–taste two-dimensional information of AMR. Integrating this approach with machine learning (ML) enables intelligent transformation from “experience-driven” to “data-driven” quality assessment, thereby developing a rapid and cost-effective quality control strategy for AMR. Feature-extraction and feature-selection techniques were employed to optimize back-propagation neural network (BPNN) classification and regression models for eight key quality markers, selecting the optimal feature subset. Additionally, nine machine-learning algorithms were applied with the optimal feature subset to establish classification models for different AMR grades and quantitative regression models for eight components based on E-nose and E-tongue data. The results demonstrated that the E-tongue combined with the k-nearest neighbors (KNN) algorithm could achieve a rapid classification of AMR grades with an accuracy of 95.56%. It also successfully predicted the contents of the extract, volatile oil, polysaccharides, atractylenolide I, atractylenolide II, atractylenolide III, bis-atractylenolide, and atractylone, with the test set’s coefficient of determination (R2) values of 0.8874, 0.8313, 0.9628, 0.8406, 0.8736, 0.8532, 0.7758, and 0.8101, respectively. In conclusion, this study provides a comprehensive and rapid solution for AMR grade classification and quality evaluation, significantly improving efficiency compared with traditional methods. This strategy holds substantial promise for real-world applications, as it enables a high-throughput, non-destructive screening of AMR in settings such as post-harvest processing and market quality surveillance, thereby supporting the sustainable and intelligent development of the herbal medicine industry.

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