LAUSR

The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithm is utilized to diagnose thyroid dysfunction serum, and finds the spectrum with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, Principal Component Analysis (PCA) was first used for feature extraction to reduce the dimension of high-dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation (BP) neural network, Extreme Learning Machine (ELM) and Learning Vector Quantization (LVQ) algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA-SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000-2500 cm-1 is 81.74%, which greatly improves the sample test speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction. This article is protected by copyright. All rights reserved.