A method for quantitative detection of coffee bean two-component mixture based on terahertz time-domain spectroscopy
By combining terahertz time-domain spectroscopy and heterogeneous machine learning, the problems of long detection cycle, high destructiveness and low accuracy in coffee bean adulteration detection are solved. It realizes rapid, non-destructive and high-precision quantitative detection of coffee bean adulteration ratio, and is suitable for spectral quantitative analysis of complex biological matrix samples.
Patent Information
- Application Number
- CN202610546754.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-23
- Publication Date
- 2026-07-17
AI Technical Summary
Existing coffee bean adulteration detection technologies suffer from problems such as long detection cycles, high destructiveness, low accuracy, and weak anti-interference capabilities, making it difficult to achieve high-precision quantitative analysis of the adulteration ratio of high-value coffee beans.
A method combining terahertz time-domain spectroscopy and heterogeneous machine learning was adopted. The sample set was divided by hierarchical Kennard-Stone algorithm, and a GBDT-IF-RF hybrid model was constructed. The heterogeneous feature screening strategy was used to remove noise interference, so as to achieve high-precision quantitative detection of coffee bean component ratio.
It enables rapid, non-destructive, and high-precision quantitative detection of coffee bean adulteration ratios, improves the model's generalization ability and robustness, and is suitable for spectral quantitative analysis of complex biological matrix samples.
Smart Images
Figure CN122409566A_ABST