Rapid qualitative and quantitative detection for adulteration of Atractylodis Rhizoma using hyperspectral imaging combined with chemometric methods

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Molecular and biomolecular spectroscopy, 2025-02, Vol.327, p.125426, Article 125426
• Main Authors: Wang, Siman, Bai, Ruibin, Long, Wanjun, Wan, Xiufu, Zhao, Zihan, Fu, Haiyan, Yang, Jian
• Format: Article
• Language: English
• Subjects: Adulteration; Atractylodis Rhizoma; Chemometrics; Hyperspectral imaging; Wavelength extraction
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2024.125426

[Display omitted] •Hyperspectral combined with chemometrics arrives great qualitative and quantitative analysis of adulteration issues.•Feature wavelength extraction methods can simplify the models to improve efficiency.•Hyperspectral combined with chemometrics is a reliable and rapid way for prediction. In the field of traditional Chinese medicine, Atractylodis Rhizoma (AR) is commonly used for various diseases due to its excellent ability to dry dampness and strengthen the spleen, especially popular in East Asia. The aim of this study is to proposed Hyperspectral Imaging (HSI) in combination with chemometric methods for the rapid qualitative and quantitative detection of AR adulteration with other types of powder. Partial Least Squares Discriminant Analysis (PLS-DA) was used to construct the classification models the best, with the First-order Derivative (F-D) preprocessing method. The accuracy values of the test sets for classification models were above 99%. Furthermore, Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and BP Neural Network (BPNN) were used to quantitatively analyze the adulteration level. On the whole, the BPNN model has a relatively stable effect. The R-square (R2) values of different models were all greater than 0.97, the Root Mean Square Error (RMSE) values were all less than 0.0300, and the Relative Percentage Difference (RPD) values were over 6.00. After applying three characteristic wavelength selection algorithms, namely Iterative Retained Information Variable (IRIV), Successive Projections Algorithm (SPA), and Variable Iterative Space Shrinkage Approach (VISSA) algorithms, the classification accuracy values remained over 99.00% while the quantification models’ RPD values were over 4.00. These results demonstrate the reliability of using hyperspectral imaging combined with chemometrics methods for the adulteration problems in AR.