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Analytical Chemistry
Quality Assurance, Chemometrics, Traceability And Metrological Chemistry
Computer Vision-based Fire-ice Ion Algorithm For Rapid And Nondestructive Authentication Of Ziziphi Spinosae Semen And Its Counterfeits.

Home
Research Domains
Chemical Sciences
Analytical Chemistry
Quality Assurance, Chemometrics, Traceability And Metrological Chemistry
Computer Vision-based Fire-ice Ion Algorithm For Rapid And Nondestructive Authentication Of Ziziphi Spinosae Semen And Its Counterfeits.

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Computer Vision-Based Fire-Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits.

Peng Chen¹, Xutong Shao¹, Guangyu Wen¹

¹Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China.

Foods (Basel, Switzerland)

|January 11, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Authenticating Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) is now easier. A novel algorithm effectively uses color and texture analysis for rapid, non-destructive food authentication.

Keywords:

Ziziphi Spinosae Semen fire–ice ion dimensionality reduction algorithm multivariate statistics nondestructive and fast judgment

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Area of Science:

Food Science
Analytical Chemistry
Computational Biology

Background:

Authenticating Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) presents significant challenges.
Distinguishing between these related plant materials is crucial for quality control and preventing adulteration in food and traditional medicine.

Purpose of the Study:

To develop and validate a robust method for the authentication of ZSS, ZMS, and HAS.
To leverage chromatic and textural properties for accurate sample differentiation.

Main Methods:

Color features were extracted using RGB, CIELAB, and HSI color spaces.
Texture analysis was performed using Gray-Level Co-occurrence Matrix (GLCM) and Law's texture analysis.
A fire-ice ion dimensionality reduction algorithm was employed for feature fusion, followed by Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) for validation.

Main Results:

Significant differences in color and texture were observed among ZSS, ZMS, and HAS samples.
The fire-ice ion algorithm effectively enhanced feature differentiation, with VIP analysis confirming significant differences (VIP > 1, p < 0.05).
Machine learning models (BP, SVM, DBN, RF) achieved high accuracy (98.83-100% training, 95.89-99.32% testing) for authentication.

Conclusions:

The developed method offers a simple, low-cost, and high-precision approach for the fast and nondestructive detection of food authenticity.
This technique is highly reliable for distinguishing between ZSS, ZMS, and HAS, ensuring product integrity.
The study demonstrates the potential of combined chromatic and textural analysis with advanced algorithms for food authentication.