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Quantitative detection of hepatocyte mixture based on terahertz time-domain spectroscopy using spectral image
Yuqi Cao1, Hanxiao Guan1, Weihang Qiu2
1State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China.
Summary
This study introduces a novel terahertz (THz) imaging technique using Gramian angular fields (GAF) for analyzing complex biological mixtures. The method enhances detection accuracy for mixed samples, outperforming traditional spectral analysis.
Area of Science:
- Terahertz (THz) technology
- Bio-detection
- Spectroscopy
Background:
- Terahertz (THz) technology shows promise for bio-detection.
- Limited research exists on THz applications for analyzing mixtures.
- Traditional 1D spectral analysis struggles with overlapping and distorted spectra in mixtures.
Purpose of the Study:
- To develop an advanced method for quantitative analysis of biological mixtures using THz technology.
- To overcome limitations of traditional 1D spectral feature extraction in complex samples.
- To improve sensitivity and accuracy in THz-based bio-detection of mixtures.
Main Methods:
- Applied the Gramian angular field (GAF) method to transform 1D THz spectra into 2D images.
- Extracted image features using histogram of oriented gradients (HOGs) and gray level histograms (GLHs).
- Utilized a support vector regression (SVR) model for quantitative analysis of hepatocyte mixtures.
Main Results:
- The GAF-based imaging method demonstrated superior stability and accuracy compared to principal component analysis (PCA).
- Achieved a root mean square error (RMSE) of 0.072 and an R-squared (R²) value of 0.932.
- Successfully quantified mixtures of hepatocytes with varying ratios.
Conclusions:
- The combination of data upscaling (GAF) and image processing enhances THz detection algorithms.
- This approach significantly improves the application of THz technology for detecting mixed biological systems.
- The study provides a valuable new tool for complex mixture analysis in bio-detection.
