Quantitative detection method for adulteration in angelica sinensis based on terahertz spectroscopy and data fusion

A method for quantitative detection of adulteration in Angelica sinensis based on terahertz spectroscopy and data fusion, comprising:

• (1) taking Angelica pubescens powder as an adulterant;
• and mixing different concentrations of the Angelica pubescens powder into Angelica sinensis powder to prepare samples with varying adulteration levels;
• wherein an amount of the Angelica pubescens powder added ranges from 0% to 100%;
• (2) placing each sample into a terahertz system, and measuring each sample to obtain a terahertz absorption spectrum and time-domain spectral information for the sample;
• (3) preprocessing spectral data by Standard Normal Variate (SNV) transformation combined with Baseline Correction, and then performing feature extraction on the terahertz absorption spectra by Competitive Adaptive Reweighted Sampling (CARS), Iteratively Retaining Informative Variables (IRIV), and Variable Iterative Space Shrinkage Approach (VISSA);
• (4) utilizing Gramian Angular Difference Field (GADF) to encode terahertz time-domain spectral data within 15-20 ps into GADF images:
• normalizing one-dimensional data, and then mapping the one-dimensional data to a cosine angle φ in polar coordinates, with time-series points represented by radial distance r, expressed as follows:
• φ
• =
• arccos
• ⁡
• (
• x
• i
• )
• ,
• -
• 1
• ≤
• x
• i
• ≤
• 1
• ,
• x
• i
• ∈
• X
• r
• =
• t
• i
• N
• ,
• t
• i
• ∈
• N
• where xi represents a time series, ti represents a timestamp, and N is a parameter to adjust span of the polar coordinates;
• the GADF is calculated by the following formula:
• GADF
• =
• [
• cos
• ⁡
• (
• φ
• 1
• -
• φ
• 1
• )
• cos
• ⁡
• (
• φ
• 1
• -
• φ
• 2
• )
• …
• cos
• ⁡
• (
• φ
• 1
• -
• φ
• n
• )
• cos
• ⁡
• (
• φ
• 2
• -
• φ
• 1
• )
• cos
• ⁡
• (
• φ
• 2
• -
• φ
• 2
• )
• …
• cos
• ⁡
• (
• φ
• 2
• -
• φ
• n
• )
• ⋮
• ⋮
• ⋱
• ⋮
• cos
• ⁡
• (
• φ
• n
• -
• φ
• 1
• )
• cos
• ⁡
• (
• φ
• n
• -
• φ
• 2
• )
• …
• cos
• ⁡
• (
• φ
• n
• -
• φ
• n
• )
• ]
• ;
• and
• utilizing Gray-Level Co-occurrence Matrix (GLCM) to read texture features of each GADF image at 0°, 45°, 90°, and 135° successively, calculating Energy, Entropy, Angular Second Moment (ASM), and Correlation of the GADF image, and outputting a mean and standard deviation of the Energy, the Entropy, the ASM, and the Correlation as a feature subset;
• utilizing Gray-Level Difference Statistics (GLDS) to calculate Mean, Contrast, ASM, and Entropy of the GADF image successively as another feature subset;
• and
• (5) adopting a feature-level data fusion strategy to fuse feature information of the terahertz absorption spectra and the GADF images;
• and establishing a quantitative detection model based on Support Vector Regression (SVR) algorithm, wherein Kennard-Stone (K-S) algorithm is utilized to divide data into a modeling set and a prediction set in a 3:1 ratio, which serves as input data for the quantitative detection model, with a penalty factor set to 4 and a radial basis function parameter set to 2.5.